From a6df67e8bcd5159cde27556f4f6a315f8dc2215f Mon Sep 17 00:00:00 2001 From: shamikam Date: Mon, 16 Jan 2017 02:56:17 +0530 Subject: First Commit --- builder.sce | 39 + builder.sce~ | 39 + cleaner.sce | 38 + etc/FOSSEE_Image_Processing_Toolbox.quit | 34 + etc/FOSSEE_Image_Processing_Toolbox.quit~ | 34 + etc/FOSSEE_Image_Processing_Toolbox.start | 77 + etc/FOSSEE_Image_Processing_Toolbox.start~ | 77 + etc/FOSSEE_Optimization_Toolbox.quit~ | 34 + etc/FOSSEE_Optimization_Toolbox.start~ | 85 + help/builder_help.sce | 21 + help/builder_help.sce~ | 21 + help/en_US/build_help.sce | 17 + help/en_US/build_help.sce~ | 17 + help/en_US/master_help.xml | 19 + help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS | 1 + .../scilab_en_US_help/JavaHelpSearch/DOCS.TAB | 1 + .../en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS | 1 + .../scilab_en_US_help/JavaHelpSearch/POSITIONS | 1 + help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA | 2 + help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP | Bin 0 -> 2048 bytes 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macros/detectSURFFeatures.bin create mode 100644 macros/detectSURFFeatures.sci create mode 100644 macros/dilate.bin create mode 100644 macros/dilate.sci create mode 100644 macros/displayErrors.bin create mode 100644 macros/displayErrors.sci create mode 100644 macros/ellipse.sce create mode 100644 macros/ellipse2poly.bin create mode 100644 macros/ellipse2poly.sci create mode 100644 macros/erode.bin create mode 100644 macros/erode.sci create mode 100644 macros/estimateFundamentalMatrix.bin create mode 100644 macros/estimateFundamentalMatrix.sci create mode 100644 macros/estimateGeometricTransform.bin create mode 100644 macros/estimateGeometricTransform.sci create mode 100644 macros/estimateUncalibratedRectification.bin create mode 100644 macros/estimateUncalibratedRectification.sci create mode 100644 macros/evaluate.bin create mode 100644 macros/evaluate.sci create mode 100644 macros/evaluateImageRetrieval.bin create mode 100644 macros/evaluateImageRetrieval.sci create mode 100644 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100644 macros/genCheckerboardPoints.bin create mode 100644 macros/genCheckerboardPoints.sci create mode 100644 macros/geometricshearer.bin create mode 100644 macros/geometricshearer.sci create mode 100644 macros/getStructuringElement.bin create mode 100644 macros/getStructuringElement.sci create mode 100644 macros/getTextSize.bin create mode 100644 macros/getTextSize.sci create mode 100644 macros/getgaussiankernel.bin create mode 100644 macros/getgaussiankernel.sci create mode 100644 macros/getkerneltype.bin create mode 100644 macros/getkerneltype.sci create mode 100644 macros/getrectsubpix.bin create mode 100644 macros/getrectsubpix.sci create mode 100644 macros/getrotationmatrix2d.bin create mode 100644 macros/getrotationmatrix2d.sci create mode 100644 macros/goodfeaturestotrack.bin create mode 100644 macros/goodfeaturestotrack.sci create mode 100644 macros/grayconnected.bin create mode 100644 macros/grayconnected.sci create mode 100644 macros/graycoprops.bin create mode 100644 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create mode 100644 macros/imboxfilt3.bin create mode 100644 macros/imboxfilt3.sci create mode 100644 macros/imcomplement.bin create mode 100644 macros/imcomplement.sci create mode 100644 macros/imcontour.bin create mode 100644 macros/imcontour.sci create mode 100644 macros/imcontrast.bin create mode 100644 macros/imcontrast.sci create mode 100644 macros/imcrop.bin create mode 100644 macros/imcrop.sci create mode 100644 macros/imcrop.sci~ create mode 100644 macros/imdivide.bin create mode 100644 macros/imdivide.sci create mode 100644 macros/imextendedmax.bin create mode 100644 macros/imextendedmax.sci create mode 100644 macros/imextendedmin.bin create mode 100644 macros/imextendedmin.sci create mode 100644 macros/imfill.bin create mode 100644 macros/imfill.sci create mode 100644 macros/imfindcircles.bin create mode 100644 macros/imfindcircles.sci create mode 100644 macros/imfuse.bin create mode 100644 macros/imfuse.sci create mode 100644 macros/imgSetToList.bin create mode 100644 macros/imgSetToList.sci create mode 100644 macros/imgaborfit.bin create mode 100644 macros/imgaborfit.sci create mode 100644 macros/imgaussfilt3.bin create mode 100644 macros/imgaussfilt3.sci create mode 100644 macros/imguidedfilter.bin create mode 100644 macros/imguidedfilter.sci create mode 100644 macros/imhistmatch.bin create mode 100644 macros/imhistmatch.sci create mode 100644 macros/imhmin.bin create mode 100644 macros/imhmin.sci create mode 100644 macros/imhmin.sci~ create mode 100644 macros/imimposemin.bin create mode 100644 macros/imimposemin.sci create mode 100644 macros/immultiply.bin create mode 100644 macros/immultiply.sci create mode 100644 macros/impixel.bin create mode 100644 macros/impixel.sci create mode 100644 macros/imread.bin create mode 100644 macros/imread.sci create mode 100644 macros/imread.sci~ create mode 100644 macros/imrect.bin create mode 100644 macros/imrect.sci create mode 100644 macros/imresize.bin create mode 100644 macros/imresize.sci create mode 100644 macros/imsharpen.bin create mode 100644 macros/imsharpen.sci create mode 100644 macros/imsharpen.sci~ create mode 100644 macros/imshow.bin create mode 100644 macros/imshow.sci create mode 100644 macros/imshowpair.bin create mode 100644 macros/imshowpair.sci create mode 100644 macros/imsubtract.bin create mode 100644 macros/imsubtract.sci create mode 100644 macros/imwarp.bin create mode 100644 macros/imwarp.sci create mode 100644 macros/imwrite.bin create mode 100644 macros/imwrite.sci create mode 100644 macros/ind2gray.bin create mode 100644 macros/ind2gray.sci create mode 100644 macros/indexImages.bin create mode 100644 macros/indexImages.sci create mode 100644 macros/indexImages.sci~ create mode 100644 macros/integralFilter.bin create mode 100644 macros/integralFilter.sci create mode 100644 macros/integralImage.bin create mode 100644 macros/integralImage.sci create mode 100644 macros/integralImage.sci~ create mode 100644 macros/integralKernel.bin create mode 100644 macros/integralKernel.sci create mode 100644 macros/invertedIndexImages.bin create mode 100644 macros/invertedIndexImages.sci create mode 100644 macros/isEpipoleInImage.bin create mode 100644 macros/isEpipoleInImage.sci create mode 100644 macros/lab2double.bin create mode 100644 macros/lab2double.sci create mode 100644 macros/lab2rgb.bin create mode 100644 macros/lab2rgb.sci create mode 100644 macros/lab2uint16.bin create mode 100644 macros/lab2uint16.sci create mode 100644 macros/lab2uint8.bin create mode 100644 macros/lab2uint8.sci create mode 100644 macros/lab2xyz.bin create mode 100644 macros/lab2xyz.sci create mode 100644 macros/laplacian.bin create mode 100644 macros/laplacian.sci create mode 100644 macros/lib create mode 100644 macros/line.bin create mode 100644 macros/line.sci create mode 100644 macros/lineToBorderPoints.bin create mode 100644 macros/lineToBorderPoints.sci create mode 100644 macros/localMaximaFinder.bin create mode 100644 macros/localMaximaFinder.sci create mode 100644 macros/makecform.bin create mode 100644 macros/makecform.sci create mode 100644 macros/matchFeatures.bin create mode 100644 macros/matchFeatures.sci create mode 100644 macros/mattolist.bin create mode 100644 macros/mattolist.sci create mode 100644 macros/mattolist.sci~ create mode 100644 macros/mean.bin create mode 100644 macros/mean.sci create mode 100644 macros/medianblur.bin create mode 100644 macros/medianblur.sci create mode 100644 macros/minAreaRect.bin create mode 100644 macros/minAreaRect.sci create mode 100644 macros/minimumenclosingcirlce.bin create mode 100644 macros/minimumenclosingcirlce.sci create mode 100644 macros/montage.bin create mode 100644 macros/montage.sci create mode 100644 macros/morphologyEx.bin create mode 100644 macros/morphologyEx.sci create mode 100644 macros/names create mode 100644 macros/ntsc2rgb.bin create mode 100644 macros/ntsc2rgb.sci create mode 100644 macros/ocr.bin create mode 100644 macros/ocr.sci create mode 100644 macros/opencv_imread.bin create mode 100644 macros/opencv_imread.sci create mode 100644 macros/opticalFlowFarneback.bin create mode 100644 macros/opticalFlowFarneback.sci create mode 100644 macros/partition.bin create mode 100644 macros/partition.sci create mode 100644 macros/peopleDetector.bin create mode 100644 macros/peopleDetector.sci create mode 100644 macros/predict.bin create mode 100644 macros/predict.sci create mode 100644 macros/puttext.bin create mode 100644 macros/puttext.sci create mode 100644 macros/pyrDown.bin create mode 100644 macros/pyrDown.sci create mode 100644 macros/pyrMeanShiftFiltering.bin create mode 100644 macros/pyrMeanShiftFiltering.sci create mode 100644 macros/pyrMeanShiftFiltering.sci~ create mode 100644 macros/pyrUp.bin create mode 100644 macros/pyrUp.sci create mode 100644 macros/pyramid.bin create mode 100644 macros/pyramid.sci create mode 100644 macros/readForegroundMask.bin create mode 100644 macros/readForegroundMask.sci create mode 100644 macros/readframe.bin create mode 100644 macros/readframe.sci create mode 100644 macros/rectangle.bin create mode 100644 macros/rectangle.sci create mode 100644 macros/rectifyStereoImages.bin create mode 100644 macros/rectifyStereoImages.sci create mode 100644 macros/rectifyStereoImages.sci~ create mode 100644 macros/regionfill.bin create mode 100644 macros/regionfill.sci create mode 100644 macros/retrieveImages.bin create mode 100644 macros/retrieveImages.sci create mode 100644 macros/rgb2lab.bin create mode 100644 macros/rgb2lab.sci create mode 100644 macros/rgb2ntsc.bin create mode 100644 macros/rgb2ntsc.sci create mode 100644 macros/rgb2xyz.bin create mode 100644 macros/rgb2xyz.sci create mode 100644 macros/roifill.bin create mode 100644 macros/roifill.sci create mode 100644 macros/roipoly.bin create mode 100644 macros/roipoly.sci create mode 100644 macros/rotationVectorToMatrix.bin create mode 100644 macros/rotationVectorToMatrix.sci create mode 100644 macros/scharr.bin create mode 100644 macros/scharr.sci create mode 100644 macros/selectStrongestBbox.bin create mode 100644 macros/selectStrongestBbox.sci create mode 100644 macros/sepFilter2D.bin create mode 100644 macros/sepFilter2D.sci create mode 100644 macros/show.bin create mode 100644 macros/show.sci create mode 100644 macros/showMatchedFeatures.bin create mode 100644 macros/showMatchedFeatures.sci create mode 100644 macros/showReprojectionErrors.bin create mode 100644 macros/showReprojectionErrors.sci create mode 100644 macros/sobel.bin create mode 100644 macros/sobel.sci create mode 100644 macros/ssim.bin create mode 100644 macros/ssim.sci create mode 100644 macros/stereoParameters.bin create mode 100644 macros/stereoParameters.sci create mode 100644 macros/threshold.bin create mode 100644 macros/threshold.sci create mode 100644 macros/trainCascadeObjectDetector.bin create mode 100644 macros/trainCascadeObjectDetector.sci create mode 100644 macros/trainImageCategoryClassifier.bin create mode 100644 macros/trainImageCategoryClassifier.sci create mode 100644 macros/triangulatePoints.bin create mode 100644 macros/triangulatePoints.sci create mode 100644 macros/undistort.bin create mode 100644 macros/undistort.sci create mode 100644 macros/undistortImage.bin create mode 100644 macros/undistortImage.sci create mode 100644 macros/undistortPoints.bin create mode 100644 macros/undistortPoints.sci create mode 100644 macros/viscircle.bin create mode 100644 macros/viscircle.sci create mode 100644 macros/warpaffine.bin create mode 100644 macros/warpaffine.sci create mode 100644 macros/watershed.bin create mode 100644 macros/watershed.sci create mode 100644 macros/whitepoint.bin create mode 100644 macros/whitepoint.sci create mode 100644 macros/wiener2.bin create mode 100644 macros/wiener2.sci create mode 100644 macros/xyz2double.bin create mode 100644 macros/xyz2double.sci create mode 100644 macros/xyz2lab.bin create mode 100644 macros/xyz2lab.sci create mode 100644 macros/xyz2rgb.bin create mode 100644 macros/xyz2rgb.sci create mode 100644 macros/xyz2uint16.bin create mode 100644 macros/xyz2uint16.sci create mode 100644 sci_gateway/builder_gateway.sce create mode 100644 sci_gateway/cleaner_gateway.sce create mode 100644 sci_gateway/cpp/builder_gateway_cpp.sce create mode 100644 sci_gateway/cpp/builder_gateway_cpp.sce~ create mode 100644 sci_gateway/cpp/cleaner.sce create mode 100644 sci_gateway/cpp/common.cpp create mode 100644 sci_gateway/cpp/common.cpp~ create mode 100644 sci_gateway/cpp/common.h create mode 100644 sci_gateway/cpp/dctmtx.cpp create mode 100644 sci_gateway/cpp/lab2xyz.cpp create mode 100644 sci_gateway/cpp/libFOSSEE_Image_Processing_Toolbox.c create mode 100755 sci_gateway/cpp/libFOSSEE_Image_Processing_Toolbox.so create mode 100644 sci_gateway/cpp/loader.sce create mode 100644 sci_gateway/cpp/multithresh.cpp create mode 100644 sci_gateway/cpp/opencv_adapthisteq.cpp create mode 100644 sci_gateway/cpp/opencv_affine2d.cpp create mode 100644 sci_gateway/cpp/opencv_applycform.cpp create mode 100644 sci_gateway/cpp/opencv_approxpolyDP.cpp create mode 100644 sci_gateway/cpp/opencv_arclength.cpp create mode 100644 sci_gateway/cpp/opencv_arrowedline.cpp create mode 100644 sci_gateway/cpp/opencv_bilateralfilter.cpp create mode 100644 sci_gateway/cpp/opencv_blur.cpp create mode 100644 sci_gateway/cpp/opencv_borderInterpolate.cpp create mode 100644 sci_gateway/cpp/opencv_boundingRect.cpp create mode 100644 sci_gateway/cpp/opencv_boxfilter.cpp create mode 100644 sci_gateway/cpp/opencv_bwconvhull.cpp create mode 100644 sci_gateway/cpp/opencv_bwdistgeodesic.cpp create mode 100644 sci_gateway/cpp/opencv_bwlookup.cpp create mode 100644 sci_gateway/cpp/opencv_bwulterode.cpp create mode 100644 sci_gateway/cpp/opencv_canny.cpp create mode 100644 sci_gateway/cpp/opencv_circle.cpp create mode 100644 sci_gateway/cpp/opencv_clipline.cpp create mode 100644 sci_gateway/cpp/opencv_contourarea.cpp create mode 100644 sci_gateway/cpp/opencv_convexhull.cpp create mode 100644 sci_gateway/cpp/opencv_convmtx2.cpp create mode 100644 sci_gateway/cpp/opencv_copymakeborder.cpp create mode 100644 sci_gateway/cpp/opencv_corner.cpp create mode 100644 sci_gateway/cpp/opencv_cornerEigenValsAndVecs.cpp create mode 100644 sci_gateway/cpp/opencv_cornerHarris.cpp create mode 100644 sci_gateway/cpp/opencv_cornerMinEigenVal.cpp create mode 100644 sci_gateway/cpp/opencv_cvtColor.cpp create mode 100644 sci_gateway/cpp/opencv_deconvlucy.cpp create mode 100644 sci_gateway/cpp/opencv_decorrstretch.cpp create mode 100644 sci_gateway/cpp/opencv_demosaic.cpp create mode 100644 sci_gateway/cpp/opencv_demosaic.cpp~ create mode 100644 sci_gateway/cpp/opencv_dilate.cpp create mode 100644 sci_gateway/cpp/opencv_ellipse.cpp create mode 100644 sci_gateway/cpp/opencv_ellipse2poly.cpp create mode 100644 sci_gateway/cpp/opencv_erode.cpp create mode 100644 sci_gateway/cpp/opencv_fftshift.cpp create mode 100644 sci_gateway/cpp/opencv_fillconvexpoly.cpp create mode 100644 sci_gateway/cpp/opencv_filter2D.cpp create mode 100644 sci_gateway/cpp/opencv_findContours.cpp create mode 100644 sci_gateway/cpp/opencv_fitellipse.cpp create mode 100644 sci_gateway/cpp/opencv_fsamp2.cpp create mode 100644 sci_gateway/cpp/opencv_ftrans2.cpp create mode 100644 sci_gateway/cpp/opencv_gabor.cpp create mode 100644 sci_gateway/cpp/opencv_gaussianblur.cpp create mode 100644 sci_gateway/cpp/opencv_getStructuringElement.cpp create mode 100644 sci_gateway/cpp/opencv_getTextSize.cpp create mode 100644 sci_gateway/cpp/opencv_getgaussiankernel.cpp create mode 100644 sci_gateway/cpp/opencv_getkerneltype.cpp create mode 100644 sci_gateway/cpp/opencv_getrectsubpix.cpp create mode 100644 sci_gateway/cpp/opencv_getrotationmatrix2D.cpp create mode 100644 sci_gateway/cpp/opencv_goodfeaturestotrack.cpp create mode 100644 sci_gateway/cpp/opencv_grayconnected.cpp create mode 100644 sci_gateway/cpp/opencv_graycoprops.cpp create mode 100644 sci_gateway/cpp/opencv_graydiffweight.cpp create mode 100644 sci_gateway/cpp/opencv_graydist.cpp create mode 100644 sci_gateway/cpp/opencv_histeq.cpp create mode 100644 sci_gateway/cpp/opencv_houghcircles.cpp create mode 100644 sci_gateway/cpp/opencv_houghlines.cpp create mode 100644 sci_gateway/cpp/opencv_houghlinesp.cpp create mode 100644 sci_gateway/cpp/opencv_ifftshift.cpp create mode 100644 sci_gateway/cpp/opencv_im2double.cpp create mode 100644 sci_gateway/cpp/opencv_imabsdiff.cpp create mode 100644 sci_gateway/cpp/opencv_imadd.cpp create mode 100644 sci_gateway/cpp/opencv_imattributes.cpp create mode 100644 sci_gateway/cpp/opencv_imboxfilt3.cpp create mode 100644 sci_gateway/cpp/opencv_imcomplement.cpp create mode 100644 sci_gateway/cpp/opencv_imcontour.cpp create mode 100644 sci_gateway/cpp/opencv_imcontrast.cpp create mode 100644 sci_gateway/cpp/opencv_imcrop.cpp create mode 100644 sci_gateway/cpp/opencv_imdivide.cpp create mode 100644 sci_gateway/cpp/opencv_imextendedmax.cpp create mode 100644 sci_gateway/cpp/opencv_imextendedmin.cpp create mode 100644 sci_gateway/cpp/opencv_imfill.cpp create mode 100644 sci_gateway/cpp/opencv_imfindcircles.cpp create mode 100644 sci_gateway/cpp/opencv_imfindcircles.cpp~ create mode 100644 sci_gateway/cpp/opencv_imfuse.cpp create mode 100644 sci_gateway/cpp/opencv_imgaborfilt.cpp create mode 100644 sci_gateway/cpp/opencv_imgaussfilt3.cpp create mode 100644 sci_gateway/cpp/opencv_imguidedfilter.cpp create mode 100644 sci_gateway/cpp/opencv_imhistmatch.cpp create mode 100644 sci_gateway/cpp/opencv_imhmax.cpp create mode 100644 sci_gateway/cpp/opencv_imhmin.cpp create mode 100644 sci_gateway/cpp/opencv_imimposemin.cpp create mode 100644 sci_gateway/cpp/opencv_imlincomb.cpp create mode 100644 sci_gateway/cpp/opencv_immultiply.cpp create mode 100644 sci_gateway/cpp/opencv_impixel.cpp create mode 100644 sci_gateway/cpp/opencv_impyramid.cpp create mode 100644 sci_gateway/cpp/opencv_imread.cpp create mode 100644 sci_gateway/cpp/opencv_imrect.cpp create mode 100644 sci_gateway/cpp/opencv_imresize.cpp create mode 100644 sci_gateway/cpp/opencv_imrotate.cpp create mode 100644 sci_gateway/cpp/opencv_imrotate.cpp~ create mode 100644 sci_gateway/cpp/opencv_imsharpen.cpp create mode 100644 sci_gateway/cpp/opencv_imshowpair.cpp create mode 100644 sci_gateway/cpp/opencv_imsubtract.cpp create mode 100644 sci_gateway/cpp/opencv_imwarp.cpp create mode 100644 sci_gateway/cpp/opencv_imwrite.cpp create mode 100644 sci_gateway/cpp/opencv_ind2gray.cpp create mode 100644 sci_gateway/cpp/opencv_ind2rgb.cpp create mode 100644 sci_gateway/cpp/opencv_integralImage.cpp create mode 100644 sci_gateway/cpp/opencv_lab2double.cpp create mode 100644 sci_gateway/cpp/opencv_lab2rgb.cpp create mode 100644 sci_gateway/cpp/opencv_lab2uint16.cpp create mode 100644 sci_gateway/cpp/opencv_lab2uint8.cpp create mode 100644 sci_gateway/cpp/opencv_lab2xyz.cpp create mode 100644 sci_gateway/cpp/opencv_laplacian.cpp create mode 100644 sci_gateway/cpp/opencv_line.cpp create mode 100644 sci_gateway/cpp/opencv_makecform.cpp create mode 100644 sci_gateway/cpp/opencv_mean1.cpp create mode 100644 sci_gateway/cpp/opencv_medianblur.cpp create mode 100644 sci_gateway/cpp/opencv_minAreaRect.cpp create mode 100644 sci_gateway/cpp/opencv_minimumenclosingcirlce.cpp create mode 100644 sci_gateway/cpp/opencv_montage.cpp create mode 100644 sci_gateway/cpp/opencv_morphologyEx.cpp create mode 100644 sci_gateway/cpp/opencv_ntsc2rgb.cpp create mode 100644 sci_gateway/cpp/opencv_puttext.cpp create mode 100644 sci_gateway/cpp/opencv_pyrDown.cpp create mode 100644 sci_gateway/cpp/opencv_pyrMeanShiftFiltering.cpp create mode 100644 sci_gateway/cpp/opencv_pyrUp.cpp create mode 100644 sci_gateway/cpp/opencv_rectangle.cpp create mode 100644 sci_gateway/cpp/opencv_regionfill.cpp create mode 100644 sci_gateway/cpp/opencv_rgb2lab.cpp create mode 100644 sci_gateway/cpp/opencv_rgb2ntsc.cpp 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100644 thirdparty1/linux/include/opencv2/videostab/fast_marching.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/fast_marching_inl.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/frame_source.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/global_motion.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/inpainting.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/log.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/motion_core.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/motion_stabilizing.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/optical_flow.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/outlier_rejection.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/ring_buffer.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/stabilizer.hpp create mode 100644 thirdparty1/linux/include/opencv2/videostab/wobble_suppression.hpp create mode 100644 thirdparty1/linux/include/opencv2/viz.hpp create mode 100644 thirdparty1/linux/include/opencv2/viz/types.hpp create mode 100644 thirdparty1/linux/include/opencv2/viz/viz3d.hpp create mode 100644 thirdparty1/linux/include/opencv2/viz/vizcore.hpp create mode 100644 thirdparty1/linux/include/opencv2/viz/widget_accessor.hpp create mode 100644 thirdparty1/linux/include/opencv2/viz/widgets.hpp create mode 100644 thirdparty1/linux/include/opencv2/xfeatures2d.hpp create mode 100644 thirdparty1/linux/include/opencv2/xfeatures2d/cuda.hpp create mode 100644 thirdparty1/linux/include/opencv2/xfeatures2d/nonfree.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/deriche_filter.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/disparity_filter.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/edge_filter.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/estimated_covariance.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/fast_hough_transform.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/fast_line_detector.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/lsc.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/paillou_filter.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/seeds.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/segmentation.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/slic.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/sparse_match_interpolator.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/structured_edge_detection.hpp create mode 100644 thirdparty1/linux/include/opencv2/ximgproc/weighted_median_filter.hpp create mode 100644 thirdparty1/linux/include/opencv2/xobjdetect.hpp create mode 100644 thirdparty1/linux/include/opencv2/xphoto.hpp create mode 100644 thirdparty1/linux/include/opencv2/xphoto/bm3d_image_denoising.hpp create mode 100644 thirdparty1/linux/include/opencv2/xphoto/dct_image_denoising.hpp create mode 100644 thirdparty1/linux/include/opencv2/xphoto/inpainting.hpp create mode 100644 thirdparty1/linux/include/opencv2/xphoto/white_balance.hpp create mode 120000 thirdparty1/linux/lib/x64/libopencv_aruco.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_aruco.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_aruco.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_bgsegm.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_bgsegm.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_bgsegm.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_bioinspired.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_bioinspired.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_bioinspired.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_calib3d.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_calib3d.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_calib3d.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_ccalib.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_ccalib.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_ccalib.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_core.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_core.so.2.4 create mode 100644 thirdparty1/linux/lib/x64/libopencv_core.so.2.4.9 create mode 120000 thirdparty1/linux/lib/x64/libopencv_core.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_core.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_datasets.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_datasets.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_datasets.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_dnn.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_dnn.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_dnn.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_dpm.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_dpm.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_dpm.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_face.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_face.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_face.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_features2d.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_features2d.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_features2d.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_flann.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_flann.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_flann.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_freetype.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_freetype.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_freetype.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_fuzzy.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_fuzzy.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_fuzzy.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_hdf.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_hdf.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_hdf.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_highgui.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_highgui.so.2.4 create mode 100644 thirdparty1/linux/lib/x64/libopencv_highgui.so.2.4.9 create mode 120000 thirdparty1/linux/lib/x64/libopencv_highgui.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_highgui.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_imgcodecs.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_imgcodecs.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_imgcodecs.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_imgproc.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_imgproc.so.2.4 create mode 100644 thirdparty1/linux/lib/x64/libopencv_imgproc.so.2.4.9 create mode 120000 thirdparty1/linux/lib/x64/libopencv_imgproc.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_imgproc.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_line_descriptor.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_line_descriptor.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_line_descriptor.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_ml.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_ml.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_ml.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_objdetect.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_objdetect.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_objdetect.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_optflow.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_optflow.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_optflow.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_phase_unwrapping.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_phase_unwrapping.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_phase_unwrapping.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_photo.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_photo.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_photo.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_plot.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_plot.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_plot.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_reg.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_reg.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_reg.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_rgbd.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_rgbd.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_rgbd.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_saliency.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_saliency.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_saliency.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_shape.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_shape.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_shape.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_stereo.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_stereo.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_stereo.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_stitching.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_stitching.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_stitching.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_structured_light.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_structured_light.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_structured_light.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_superres.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_superres.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_superres.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_surface_matching.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_surface_matching.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_surface_matching.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_text.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_text.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_text.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_tracking.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_tracking.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_tracking.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_video.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_video.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_video.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_videoio.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_videoio.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_videoio.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_videostab.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_videostab.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_videostab.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_viz.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_viz.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_viz.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_xfeatures2d.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_xfeatures2d.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_xfeatures2d.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_ximgproc.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_ximgproc.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_ximgproc.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_xobjdetect.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_xobjdetect.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_xobjdetect.so.3.2.0 create mode 120000 thirdparty1/linux/lib/x64/libopencv_xphoto.so create mode 120000 thirdparty1/linux/lib/x64/libopencv_xphoto.so.3.2 create mode 100644 thirdparty1/linux/lib/x64/libopencv_xphoto.so.3.2.0 create mode 100644 thirdparty1/linux/lib/x64/pkgconfig/opencv.pc create mode 100644 thirdparty1/linux/lib/x64/python2.7/dist-packages/cv2.so create mode 100644 thirdparty1/linux/lib/x64/python3.4/dist-packages/cv2.cpython-34m.so create mode 100644 unloader.sce diff --git a/builder.sce b/builder.sce new file mode 100644 index 0000000..b21ef26 --- /dev/null +++ b/builder.sce @@ -0,0 +1,39 @@ +mode(-1); + +// Copyright (C) 2017 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +lines(0); +try + getversion('scilab'); +catch + error(gettext('Scilab 5.5.0 or more is required.')); +end; + +// ==================================================================== +if ~with_module("development_tools") then + error(msprintf(gettext("%s module not installed."),"development_tools")); +end +// ==================================================================== +TOOLBOX_NAME = "FOSSEE_Image_Processing_Toolbox"; +TOOLBOX_TITLE = "FOSSEE Image Processing Toolbox"; +// ==================================================================== + + +toolbox_dir = get_absolute_file_path("builder.sce"); + +tbx_builder_macros(toolbox_dir); +tbx_builder_gateway(toolbox_dir); +tbx_builder_help(toolbox_dir); +tbx_build_loader(TOOLBOX_NAME, toolbox_dir); +tbx_build_cleaner(TOOLBOX_NAME, toolbox_dir); + +clear toolbox_dir TOOLBOX_NAME TOOLBOX_TITLE; diff --git a/builder.sce~ b/builder.sce~ new file mode 100644 index 0000000..4757140 --- /dev/null +++ b/builder.sce~ @@ -0,0 +1,39 @@ +mode(-1); + +// Copyright (C) 2017 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +lines(0); +try + getversion('scilab'); +catch + error(gettext('Scilab 5.5.0 or more is required.')); +end; + +// ==================================================================== +if ~with_module("development_tools") then + error(msprintf(gettext("%s module not installed."),"development_tools")); +end +// ==================================================================== +TOOLBOX_NAME = "FOSSEE_Image_Processing_Toolbox"; +TOOLBOX_TITLE = "FOSSEE Image Processing Toolbox"; +// ==================================================================== + + +toolbox_dir = get_absolute_file_path("builder.sce"); + +tbx_builder_macros(toolbox_dir); +//tbx_builder_gateway(toolbox_dir); +tbx_builder_help(toolbox_dir); +tbx_build_loader(TOOLBOX_NAME, toolbox_dir); +tbx_build_cleaner(TOOLBOX_NAME, toolbox_dir); + +clear toolbox_dir TOOLBOX_NAME TOOLBOX_TITLE; diff --git a/cleaner.sce b/cleaner.sce new file mode 100644 index 0000000..370c68f --- /dev/null +++ b/cleaner.sce @@ -0,0 +1,38 @@ +// This file is released under the 3-clause BSD license. See COPYING-BSD. +// Generated by builder.sce: Please, do not edit this file + +try + getversion("scilab"); +catch + error("Scilab 5.0 or more is required."); +end +function perform_clean() + root_tlbx = get_absolute_file_path('cleaner.sce'); + + if isfile(root_tlbx + '/macros/cleanmacros.sce') then + exec(root_tlbx+'/macros/cleanmacros.sce'); + end + + if isfile(root_tlbx + '/src/cleaner_src.sce') then + exec(root_tlbx+'/src/cleaner_src.sce'); + end + + if isfile(root_tlbx + "/sci_gateway/cleaner_gateway.sce") then + exec(root_tlbx + "/sci_gateway/cleaner_gateway.sce"); + mdelete(root_tlbx + "/sci_gateway/cleaner_gateway.sce"); + end + + if isfile(root_tlbx + "/help/cleaner_help.sce") then + exec(root_tlbx + "/help/cleaner_help.sce"); + end + + if isfile(root_tlbx + "/loader.sce") then + mdelete(root_tlbx + "/loader.sce"); + end + + if isfile(root_tlbx + "/unloader.sce") then + mdelete(root_tlbx + "/unloader.sce"); + end +endfunction +perform_clean(); +clear perform_clean; diff --git a/etc/FOSSEE_Image_Processing_Toolbox.quit b/etc/FOSSEE_Image_Processing_Toolbox.quit new file mode 100644 index 0000000..459eb07 --- /dev/null +++ b/etc/FOSSEE_Image_Processing_Toolbox.quit @@ -0,0 +1,34 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + + +function quitModule() + + etc_tlbx = get_absolute_file_path("FOSSEE_Image_Processing_Toolbox.quit"); + etc_tlbx = getshortpathname(etc_tlbx); + root_tlbx = strncpy( etc_tlbx, length(etc_tlbx)-length("\etc\") ); + + //unlink libraries + [bOK, ilib] = c_link('FOSSEE_Optimization_Toolbox'); + if bOK then + ulink(ilib); + end + + // Remove Preferences GUI + // ============================================================================= + if getscilabmode() == "STD" then + removeModulePreferences(root_tlbx); + end + ulink(); +endfunction + +clear quitModule; + diff --git a/etc/FOSSEE_Image_Processing_Toolbox.quit~ b/etc/FOSSEE_Image_Processing_Toolbox.quit~ new file mode 100644 index 0000000..ca53a1c --- /dev/null +++ b/etc/FOSSEE_Image_Processing_Toolbox.quit~ @@ -0,0 +1,34 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + + +function quitModule() + + etc_tlbx = get_absolute_file_path("FOSSEE_Image_Processin_Toolbox.quit"); + etc_tlbx = getshortpathname(etc_tlbx); + root_tlbx = strncpy( etc_tlbx, length(etc_tlbx)-length("\etc\") ); + + //unlink libraries + [bOK, ilib] = c_link('FOSSEE_Optimization_Toolbox'); + if bOK then + ulink(ilib); + end + + // Remove Preferences GUI + // ============================================================================= + if getscilabmode() == "STD" then + removeModulePreferences(root_tlbx); + end + ulink(); +endfunction + +clear quitModule; + diff --git a/etc/FOSSEE_Image_Processing_Toolbox.start b/etc/FOSSEE_Image_Processing_Toolbox.start new file mode 100644 index 0000000..3b6355c --- /dev/null +++ b/etc/FOSSEE_Image_Processing_Toolbox.start @@ -0,0 +1,77 @@ +// Copyright (C) 2017 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +mprintf("Start FOSSEE Image Processing Toolbox\n"); + +[a, opt] = getversion(); +Version = opt(2); + +etc_tlbx = get_absolute_file_path("FOSSEE_Image_Processing_Toolbox.start"); +etc_tlbx = getshortpathname(etc_tlbx); +root_tlbx = strncpy( etc_tlbx, length(etc_tlbx)-length("\etc\") ); + +//Load functions library +// ============================================================================= +mprintf("\tLoad macros\n"); +pathmacros = pathconvert( root_tlbx ) + "macros" + filesep(); +fipt_lib = lib(pathmacros); +clear pathmacros; + +// load gateways +// ============================================================================= + +mprintf("\tLoad gateways\n"); +[a, opt] = getversion(); +Version = opt(2); +ilib_verbose(0); +if getos()=="Windows" then +// lib_path = root_tlbx + "/thirdparty/windows/bin/" + Version;*/ +// link(lib_path+filesep()+"IpOptFSS.dll");*/ +// link(lib_path+filesep()+"IpOpt-vc10.dll");*/ +else + lib_path = root_tlbx + "/thirdparty/linux/lib/" + Version; +// lib_path="/usr/lib";*/ + link(lib_path + "/libopencv_core.so"); + link(lib_path + "/libopencv_highgui.so"); + link(lib_path + "/libopencv_imgproc.so"); + +end +exec(pathconvert(root_tlbx + filesep() + "sci_gateway" + filesep() + "loader_gateway.sce",%f)); + +// Load and add help chapter +// ============================================================================= +if ( %t ) then +if or(getscilabmode() == ["NW";"STD"]) then + mprintf("\tLoad help\n"); + path_addchapter = pathconvert(root_tlbx+"/jar"); + if ( isdir(path_addchapter) <> [] ) then + add_help_chapter("FOSSEE_Image_Processing_Toolbox", path_addchapter, %F); + clear add_help_chapter; + end + clear path_addchapter; +end +end + +// add demos +// ============================================================================= + +if ( %t ) then +if or(getscilabmode() == ["NW";"STD"]) then + mprintf("\tLoad demos\n"); + //pathdemos = pathconvert(root_tlbx+"/demos/sci_FOSSEE_Image_Processing_Toolbox.dem.gateway.sce",%f,%t); + //add_demo("FOSSEE_Image_Processing_Toolbox",pathdemos); + //clear pathdemos ; +end +end + +// ============================================================================= + +clear etc_tlbx root_tlbx Version a opt lib_path; diff --git a/etc/FOSSEE_Image_Processing_Toolbox.start~ b/etc/FOSSEE_Image_Processing_Toolbox.start~ new file mode 100644 index 0000000..bf6a60e --- /dev/null +++ b/etc/FOSSEE_Image_Processing_Toolbox.start~ @@ -0,0 +1,77 @@ +// Copyright (C) 2017 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +mprintf("Start FOSSEE Image Processing Toolbox\n"); + +[a, opt] = getversion(); +Version = opt(2); + +etc_tlbx = get_absolute_file_path("FOSSEE_Image_Processing_Toolbox.start"); +etc_tlbx = getshortpathname(etc_tlbx); +root_tlbx = strncpy( etc_tlbx, length(etc_tlbx)-length("\etc\") ); + +//Load functions library +// ============================================================================= +mprintf("\tLoad macros\n"); +pathmacros = pathconvert( root_tlbx ) + "macros" + filesep(); +fipt_lib = lib(pathmacros); +clear pathmacros; + +// load gateways +// ============================================================================= + +mprintf("\tLoad gateways\n"); +[a, opt] = getversion(); +Version = opt(2); +ilib_verbose(0); +if getos()=="Windows" then +// lib_path = root_tlbx + "/thirdparty/windows/bin/" + Version;*/ +// link(lib_path+filesep()+"IpOptFSS.dll");*/ +// link(lib_path+filesep()+"IpOpt-vc10.dll");*/ +else + lib_path = root_tlbx + "/thirdparty/linux/lib/" + Version; +// lib_path="/usr/lib";*/ +// link(lib_path + "/libopencv_core.so");*/ +// link(lib_path + "/libopencv_highgui.so");*/ +// link(lib_path + "/libopencv_imgproc.so");*/ + +end +exec(pathconvert(root_tlbx + filesep() + "sci_gateway" + filesep() + "loader_gateway.sce",%f)); + +// Load and add help chapter +// ============================================================================= +if ( %t ) then +if or(getscilabmode() == ["NW";"STD"]) then + mprintf("\tLoad help\n"); + path_addchapter = pathconvert(root_tlbx+"/jar"); + if ( isdir(path_addchapter) <> [] ) then + add_help_chapter("FOSSEE_Image_Processing_Toolbox", path_addchapter, %F); + clear add_help_chapter; + end + clear path_addchapter; +end +end + +// add demos +// ============================================================================= + +if ( %t ) then +if or(getscilabmode() == ["NW";"STD"]) then + mprintf("\tLoad demos\n"); + //pathdemos = pathconvert(root_tlbx+"/demos/sci_FOSSEE_Image_Processing_Toolbox.dem.gateway.sce",%f,%t); + //add_demo("FOSSEE_Image_Processing_Toolbox",pathdemos); + //clear pathdemos ; +end +end + +// ============================================================================= + +clear etc_tlbx root_tlbx Version a opt lib_path; diff --git a/etc/FOSSEE_Optimization_Toolbox.quit~ b/etc/FOSSEE_Optimization_Toolbox.quit~ new file mode 100644 index 0000000..86ed18a --- /dev/null +++ b/etc/FOSSEE_Optimization_Toolbox.quit~ @@ -0,0 +1,34 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause +// Author: Harpreet Singh +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + + +function quitModule() + + etc_tlbx = get_absolute_file_path("FOSSEE_Optimization_Toolbox.quit"); + etc_tlbx = getshortpathname(etc_tlbx); + root_tlbx = strncpy( etc_tlbx, length(etc_tlbx)-length("\etc\") ); + + //unlink libraries + [bOK, ilib] = c_link('FOSSEE_Optimization_Toolbox'); + if bOK then + ulink(ilib); + end + + // Remove Preferences GUI + // ============================================================================= + if getscilabmode() == "STD" then + removeModulePreferences(root_tlbx); + end + ulink(); +endfunction + +clear quitModule; + diff --git a/etc/FOSSEE_Optimization_Toolbox.start~ b/etc/FOSSEE_Optimization_Toolbox.start~ new file mode 100644 index 0000000..f2a5a06 --- /dev/null +++ b/etc/FOSSEE_Optimization_Toolbox.start~ @@ -0,0 +1,85 @@ +// Copyright (C) 2017 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +mprintf("Start FOSSEE Image Processing Toolbox\n"); + +[a, opt] = getversion(); +Version = opt(2); + +etc_tlbx = get_absolute_file_path("FOSSEE_Image_Processing_Toolbox.start"); +etc_tlbx = getshortpathname(etc_tlbx); +root_tlbx = strncpy( etc_tlbx, length(etc_tlbx)-length("\etc\") ); + +//Load functions library +// ============================================================================= +mprintf("\tLoad macros\n"); +pathmacros = pathconvert( root_tlbx ) + "macros" + filesep(); +symphony_lib = lib(pathmacros); +clear pathmacros; + +// load gateways +// ============================================================================= + +mprintf("\tLoad gateways\n"); +[a, opt] = getversion(); +Version = opt(2); +ilib_verbose(0); +if getos()=="Windows" then + lib_path = root_tlbx + "/thirdparty/windows/bin/" + Version; + link(lib_path+filesep()+"IpOptFSS.dll"); + link(lib_path+filesep()+"IpOpt-vc10.dll"); +else + lib_path = root_tlbx + "/thirdparty/linux/lib/" + Version; + link(lib_path + "/libCoinUtils.so"); + link(lib_path + "/libClp.so"); + link(lib_path + "/libClpSolver.so"); + link(lib_path + "/libOsi.so"); + link(lib_path + "/libOsiCommonTests.so"); + link(lib_path + "/libOsiClp.so"); + link(lib_path + "/libCgl.so"); + link(lib_path + "/libSym.so"); + link(lib_path + "/libOsiSym.so"); + link(lib_path + "/libcoinblas.so"); + link(lib_path + "/libcoinlapack.so"); + link(lib_path + "/libcoinmumps.so"); + link(lib_path + "/libipopt.so"); +end +exec(pathconvert(root_tlbx + filesep() + "sci_gateway" + filesep() + "loader_gateway.sce",%f)); + +// Load and add help chapter +// ============================================================================= +if ( %t ) then +if or(getscilabmode() == ["NW";"STD"]) then + mprintf("\tLoad help\n"); + path_addchapter = pathconvert(root_tlbx+"/jar"); + if ( isdir(path_addchapter) <> [] ) then + add_help_chapter("FOSSEE_Optimization_Toolbox", path_addchapter, %F); + clear add_help_chapter; + end + clear path_addchapter; +end +end + +// add demos +// ============================================================================= + +if ( %t ) then +if or(getscilabmode() == ["NW";"STD"]) then + mprintf("\tLoad demos\n"); + pathdemos = pathconvert(root_tlbx+"/demos/sci_FOSSEE_Image_Processing_Toolbox.dem.gateway.sce",%f,%t); + add_demo("FOSSEE_Image_Processing_Toolbox",pathdemos); + clear pathdemos ; +end +end + +// ============================================================================= + +clear etc_tlbx root_tlbx Version a opt lib_path; diff --git a/help/builder_help.sce b/help/builder_help.sce new file mode 100644 index 0000000..4d4e031 --- /dev/null +++ b/help/builder_help.sce @@ -0,0 +1,21 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause + +mode(-1) +lines(0) + +toolbox_title = "FOSSEE_Image_Processing_Toolbox" + +help_dir = get_absolute_file_path('builder_help.sce'); + +tbx_builder_help_lang("en_US", help_dir); + +clear toolbox_title; diff --git a/help/builder_help.sce~ b/help/builder_help.sce~ new file mode 100644 index 0000000..da2f4f7 --- /dev/null +++ b/help/builder_help.sce~ @@ -0,0 +1,21 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: Harpreet Singh +// Organization: FOSSEE, IIT Bombay +// Email: harpreet.mertia@gmail.com +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause + +mode(-1) +lines(0) + +toolbox_title = "FOSSEE_Optimization_Toolbox" + +help_dir = get_absolute_file_path('builder_help.sce'); + +tbx_builder_help_lang("en_US", help_dir); + +clear toolbox_title; diff --git a/help/en_US/build_help.sce b/help/en_US/build_help.sce new file mode 100644 index 0000000..81154f3 --- /dev/null +++ b/help/en_US/build_help.sce @@ -0,0 +1,17 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause + +help_lang_dir = get_absolute_file_path('build_help.sce'); + +tbx_build_help(TOOLBOX_TITLE, help_lang_dir); + +clear help_lang_dir; + diff --git a/help/en_US/build_help.sce~ b/help/en_US/build_help.sce~ new file mode 100644 index 0000000..0205c56 --- /dev/null +++ b/help/en_US/build_help.sce~ @@ -0,0 +1,17 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: harpreet.mertia@gmail.com +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause + +help_lang_dir = get_absolute_file_path('build_help.sce'); + +tbx_build_help(TOOLBOX_TITLE, help_lang_dir); + +clear help_lang_dir; + diff --git a/help/en_US/master_help.xml b/help/en_US/master_help.xml new file mode 100644 index 0000000..45c64d2 --- /dev/null +++ b/help/en_US/master_help.xml @@ -0,0 +1,19 @@ + + + + +]> + + + FOSSEE Image Processing Toolbox + + + diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS new file mode 100644 index 0000000..ef46423 --- /dev/null +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB new file mode 100644 index 0000000..e3c71bf --- /dev/null +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB @@ -0,0 +1 @@ +eÿÐjª` \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS new file mode 100644 index 0000000..1324712 --- /dev/null +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS @@ -0,0 +1 @@ +ÐÊ \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS new file mode 100644 index 0000000..60477b1 --- /dev/null +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS @@ -0,0 +1 @@ +eÿ×ãbÙñ.³d \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA b/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA new file mode 100644 index 0000000..a646b00 --- /dev/null +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA @@ -0,0 +1,2 @@ +JavaSearch 1.0 +TMAP bs=2048 rt=0 fl=-1 id1=10 id2=1 diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP new file mode 100644 index 0000000..fced7c7 Binary files /dev/null and b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP differ diff --git a/help/en_US/scilab_en_US_help/ScilabCaution.png b/help/en_US/scilab_en_US_help/ScilabCaution.png new file mode 100644 index 0000000..8edb56e Binary files /dev/null and b/help/en_US/scilab_en_US_help/ScilabCaution.png differ diff --git a/help/en_US/scilab_en_US_help/ScilabEdit.png b/help/en_US/scilab_en_US_help/ScilabEdit.png new file mode 100644 index 0000000..e139988 Binary files /dev/null and b/help/en_US/scilab_en_US_help/ScilabEdit.png differ diff --git a/help/en_US/scilab_en_US_help/ScilabExecute.png b/help/en_US/scilab_en_US_help/ScilabExecute.png new file mode 100644 index 0000000..4acd4b9 Binary files /dev/null and b/help/en_US/scilab_en_US_help/ScilabExecute.png differ diff --git a/help/en_US/scilab_en_US_help/ScilabImportant.png b/help/en_US/scilab_en_US_help/ScilabImportant.png new file mode 100644 index 0000000..3b51020 Binary files /dev/null and b/help/en_US/scilab_en_US_help/ScilabImportant.png differ diff --git a/help/en_US/scilab_en_US_help/ScilabNote.png b/help/en_US/scilab_en_US_help/ScilabNote.png new file mode 100644 index 0000000..ed46c3e Binary files /dev/null and b/help/en_US/scilab_en_US_help/ScilabNote.png differ diff --git a/help/en_US/scilab_en_US_help/ScilabTip.png b/help/en_US/scilab_en_US_help/ScilabTip.png new file mode 100644 index 0000000..ed46c3e Binary files /dev/null and b/help/en_US/scilab_en_US_help/ScilabTip.png differ diff --git a/help/en_US/scilab_en_US_help/ScilabWarning.png b/help/en_US/scilab_en_US_help/ScilabWarning.png new file mode 100644 index 0000000..8edb56e Binary files /dev/null and b/help/en_US/scilab_en_US_help/ScilabWarning.png differ diff --git a/help/en_US/scilab_en_US_help/c_code.css b/help/en_US/scilab_en_US_help/c_code.css new file mode 100644 index 0000000..948d2ee --- /dev/null +++ b/help/en_US/scilab_en_US_help/c_code.css @@ -0,0 +1,54 @@ +.ccomment { + font-style: italic; + color: #b22222 +} + +.cdefault { + font-style: normal; + color: #000000 +} + +.copenclose { + font-style: normal; + color: #000000 +} + +.coperator { + font-style: normal; + color: #000000 +} + +.cstring { + font-style: normal; + color: #a6557a +} + +.ctype { + font-style: normal; + color: #55a655 +} + +.cpreprocessor { + font-style: normal; + color: #9965a6 +} + +.cid { + font-style: normal; + color: #000000 +} + +.ckeyword { + font-style: normal; + color: #ad3ff2 +} + +.cmodifier { + font-style: normal; + color: #ad3ff2 +} + +.cnumber { + font-style: normal; + color: #008b8b +} diff --git a/help/en_US/scilab_en_US_help/index.html b/help/en_US/scilab_en_US_help/index.html new file mode 100644 index 0000000..651ec0d --- /dev/null +++ b/help/en_US/scilab_en_US_help/index.html @@ -0,0 +1,52 @@ + + + + + + +
+ + + + +
+ + + + + +
+
+
+ + + + +

+

FOSSEE Image Processing Toolbox

+ +
+ +
+ + + + + + +
Report an issue
+ + + + + +
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+ + diff --git a/help/en_US/scilab_en_US_help/jhelpidx.xml b/help/en_US/scilab_en_US_help/jhelpidx.xml new file mode 100644 index 0000000..aa5a66e --- /dev/null +++ b/help/en_US/scilab_en_US_help/jhelpidx.xml @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/jhelpmap.jhm b/help/en_US/scilab_en_US_help/jhelpmap.jhm new file mode 100644 index 0000000..cdbcb20 --- /dev/null +++ b/help/en_US/scilab_en_US_help/jhelpmap.jhm @@ -0,0 +1,5 @@ + + + + + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/jhelpset.hs b/help/en_US/scilab_en_US_help/jhelpset.hs new file mode 100644 index 0000000..7c93217 --- /dev/null +++ b/help/en_US/scilab_en_US_help/jhelpset.hs @@ -0,0 +1,28 @@ + + + +FOSSEE Image Processing Toolbox + +top + + + +TOC + +javax.help.TOCView +jhelptoc.xml + + +Index + +javax.help.IndexView +jhelpidx.xml + + +Search + +javax.help.SearchView +JavaHelpSearch + + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/jhelptoc.xml b/help/en_US/scilab_en_US_help/jhelptoc.xml new file mode 100644 index 0000000..5ef8b88 --- /dev/null +++ b/help/en_US/scilab_en_US_help/jhelptoc.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/scilab_code.css b/help/en_US/scilab_en_US_help/scilab_code.css new file mode 100644 index 0000000..658f42e --- /dev/null +++ b/help/en_US/scilab_en_US_help/scilab_code.css @@ -0,0 +1,96 @@ +.scilabcomment { + font-style: italic; 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See COPYING-BSD. +// Generated by builder.sce: Please, do not edit this file + +try + getversion("scilab"); +catch + error("Scilab 5.0 or more is required."); +end; + +exec(get_absolute_file_path("loader.sce")+"etc/"+"FOSSEE_Image_Processing_Toolbox.start"); diff --git a/macros/BRISKPoints.bin b/macros/BRISKPoints.bin new file mode 100644 index 0000000..ccb0151 Binary files /dev/null and b/macros/BRISKPoints.bin differ diff --git a/macros/BRISKPoints.sci b/macros/BRISKPoints.sci new file mode 100644 index 0000000..d3d80f3 --- /dev/null +++ b/macros/BRISKPoints.sci @@ -0,0 +1,30 @@ +function [varargout] = BRISKPoints(inp) +// This function returns a BRISKPoints struct for storing BRISK interest points. +// +// Calling Sequence +// myStruct = BRISKPoints(Location of KeyPoints) +// +// Parameters +// results: BRISKPoints struct which contains Location, Orientation, Metric, Scale and Count. +// Location: Location of Interest Point. +// +// Description +// This function returns a BRISKPoints struct with default values. +// +// Examples +// image = imread('sample.jpg'); +// results = detectBRISKFeatures(image); +// myStruct = BRISKPoints(results.KeyPoints); +// +// Authors +// Shashank Shekhar + [ lhs, rhs ] = argn(0) + if rhs > 1 then + error(msprintf("Too many input arguments")) + end + if lhs > 1 then + error(msprintf("Too many output arguments")) + end + [a b c d e] = ocv_BRISKPoints(inp); + varargout(1) = struct('Location', a, 'Orientation', b, 'Metric', c ,'Scale', d, 'Count', e); +endfunction diff --git a/macros/CascadeObjectDetector.bin b/macros/CascadeObjectDetector.bin new file mode 100644 index 0000000..ea78a31 Binary files /dev/null and b/macros/CascadeObjectDetector.bin differ diff --git a/macros/CascadeObjectDetector.sci b/macros/CascadeObjectDetector.sci new file mode 100644 index 0000000..c1c4a7f --- /dev/null +++ b/macros/CascadeObjectDetector.sci @@ -0,0 +1,31 @@ +function [out]=insertObjectAnnotation(input_image,object) +// Detect human body parts like face ,nose,eye and ear. +// +// Calling Sequence +// output_image = insertObjectAnnotation(input_image,object); +// +// Parameters +// +// input_image : image matrix on which Cascadeobject detection has to be performed +// object : An input string which specify the name of the object which has to be detect +// output_image : an output image in which object are bounded by rectangle. +// +// Description +// This function uses the Viola-Jones algorithm to detect the object from given input image.The function first finds the target object on the image then makes a rectangle on detected part of the image.To detect the object it uses already trained data which are stored in .xml file.User can use own trained data for better object detection. +// +// Examples +// a = imread('lena.jpeg'); +// b = insertObjectAnnotation(a,'face'); +// imshow(b) +// +// Authors. +// +//Diwakar Bhardwaj + input_image1=mattolist(input_image); + a=opencv_CascadeObjectDetector(input_image,object); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/DCT.bin b/macros/DCT.bin new file mode 100644 index 0000000..19443dc Binary files /dev/null and b/macros/DCT.bin differ diff --git a/macros/DCT.sci b/macros/DCT.sci new file mode 100644 index 0000000..d7dd08a --- /dev/null +++ b/macros/DCT.sci @@ -0,0 +1,33 @@ +function [dstImg] = DCT(srcImg) +// Performs forward Discrete Cosine Transform of the 1D or 2D array. +// +// Calling Sequence +// [dstMat] = DCT(srcMat) +// +// Parameters +// srcMat = 1D or 2D floating type array +// dstMat = The output matrix +// +// Description +// Returns the forward transform of the input matrix. It uses the flag to perform the transform. The flag is 0 by default so as to perform forward transform. +// +// Examples +// [srcMat] = [230.3 23.1 432.5; 321 543.1 89.5] +// [dstMAt] = DCT(srcMat) +// +// Bibliography +// Literature references one pr. line +// +// code for the above function +// + + srcMat = mattolist(srcImg) + output = opencv_DCT(srcMat) + + channels = size(output) + + for i = 1:channels // for i channel image + output_image(:,:,i) = output(i) + end +endfunction + diff --git a/macros/Deinterlacer.bin b/macros/Deinterlacer.bin new file mode 100644 index 0000000..14f0bdc Binary files /dev/null and b/macros/Deinterlacer.bin differ diff --git a/macros/Deinterlacer.sci b/macros/Deinterlacer.sci new file mode 100644 index 0000000..6ced08a --- /dev/null +++ b/macros/Deinterlacer.sci @@ -0,0 +1,56 @@ +function [dstImg] = Deinterlacer(srcImg, varargin) +// Removes the motion antiquity from Images +// +// Calling Sequence +// [dstImg] = Deinterlacer(srcImg) +// [dstImg] = Deinterlacer(srcImg, method) +// +// Parameters +// srcImg: The input Matrix +// method: for specifying the method used for deinterlacing +// dstimg: The output Matrix +// +// Description +// The input is an image, srcImg, containing the motion antiquity. The varargin specifies the 2nd argument(method) for the function. If second argument i.e method is not specified then default method is used. +// The method may be any of following types :- +// +// Line repeatation +// Linear interpolation +// Vertical temporal median filtering +// +// +// Examples +// srcImg = imread('lena.jpeg') +// [dstImg] = Deinterlacer(srcImg) // no method specified, default method used +// [dstImg] = Deinterlacer(srcImg, "Linear interpolation") // Deinterlacing done using Linear interpolation +// +// start of coding after on empty line to mark the end of the head_comments section +// + + [lhs,rhs]=argn(0) + + //To check the number of input and output arguments + if rhs>2 then + error(msprintf(" Too many input arguments to the function")) + end + + srcMat = mattolist(srcImg) + + select rhs + case 1 then // if no method specified then use default method + output = opencv_Deinterlacer(srcMat) + case 2 then // if method specified + if typeof(varargin(1)) <> "string" then + error(msprintf(" argument 2 must string")) + end + output = opencv_Deinterlacer(srcMat, varargin(1)) + end + + channels = size(output) + + for i = 1:channels + dstImg(:,:,i) = output(i) + end + +endfunction + diff --git a/macros/FFT.bin b/macros/FFT.bin new file mode 100644 index 0000000..c3b6185 Binary files /dev/null and b/macros/FFT.bin differ diff --git a/macros/FFT.sci b/macros/FFT.sci new file mode 100644 index 0000000..3ada874 --- /dev/null +++ b/macros/FFT.sci @@ -0,0 +1,22 @@ +function fourierTransform=FFT(inputMatrix) +// This funtions returns discrete Fourier Transform of 2D input matrix +// +// Calling Sequence +// fourierTransform=FFT(inputMatrix); +// +// Parameters +// inputMatrix: Input matrix must be 2-D. +// +// Description +// It returns the 2D discrete Fourier transform of two dimensional input matrix. +// +// Examples +// I=imread("cameraman.tif"); +// fourier=FFT(I); + + [rows cols channels]=size(inputMatrix); + if channels <> 1 then + error(msprintf("Wrong input, input must be 2-D matrix")); + end + fourierTransform=opencv_FFT(inputMatrix); +endfunction diff --git a/macros/ForegroundDetector.bin b/macros/ForegroundDetector.bin new file mode 100644 index 0000000..6a4edff Binary files /dev/null and b/macros/ForegroundDetector.bin differ diff --git a/macros/ForegroundDetector.sci b/macros/ForegroundDetector.sci new file mode 100644 index 0000000..16a1e81 --- /dev/null +++ b/macros/ForegroundDetector.sci @@ -0,0 +1,55 @@ +function [varargout] = ForegroundDetector(varargin) +// This function creates a ForeggroundDetector struct which is used to detect Foreground with Gaussian Mixture Models. +// +// Calling Sequence +// result = ForegroundDetector() +// result = ForegroundDetector(Name, Value, ..) +// +// Parameters +// results: ForegroundDetector struct containing AdaptLearningRate, NumTrainingFrames, MinimumBackgroundRatio, NumGaussians and LearningRate. +// AdaptLearningRate : (optional) If true, it enables the adaptation of Learning Rate during the period specified by number of training frames. +// NumTrainingFrames : (optional) The number of initial video frames employed to train the Background Model. +// MinimumBackgroundRatio : (optional) +// NumGaussians : (optional) +// LearningRate : (optional) This comes into action when AdaptLearningRate is enabled. +// +// Description +// This function returns a ForegreoundDetector Struct which basically sets a background model and tracks changes in an input video frame with respect to the background and then computes the Foreground Mask. +// +// Examples +// myStr = ForegroundDetector("NumGaussians", 4) +// +// Authors +// Shashank Shekhar + [ lhs, rhs ] = argn(0) + if modulo(rhs,2)~=0 then + error(msprintf("Incorrect number of input arguments")) + end + + if rhs > 10 then + error(msprintf("Too many input arguments")) + end + if lhs > 1 then + error(msprintf("Too many output arguments")) + end + select rhs + case 0 then + [fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ]= ocv_ForegroundDetector() + + case 2 then + [fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2)) + + case 4 then + [fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2), varargin(3), varargin(4)) + + case 6 then + [fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + + case 8 then + [fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + + case 10 then + [fdAdaptLearningRate, fdNumTrainingFrames,fdMinimumBackgroundRatio, fdNumGaussians, fdLearningRate ] = ocv_ForegroundDetector(varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10)) + end + varargout(1) = struct("AdaptLearningRate", fdAdaptLearningRate, "NumTrainingFrames", fdNumTrainingFrames, "MinimumBackgroundRatio", fdMinimumBackgroundRatio, "NumGaussians", fdNumGaussians, "LearningRate", fdLearningRate); +endfunction diff --git a/macros/IDCT.bin b/macros/IDCT.bin new file mode 100644 index 0000000..00196cb Binary files /dev/null and b/macros/IDCT.bin differ diff --git a/macros/IDCT.sci b/macros/IDCT.sci new file mode 100644 index 0000000..217b00c --- /dev/null +++ b/macros/IDCT.sci @@ -0,0 +1,46 @@ +function new_image = IDCT(image) +// Compute inverse Discrete Transform of image +// +// Calling Sequence +// newimage = IDCT(image) +// +// Parameters +// image : floating-point image +// newimage : IDCT of the input image +// +// Description +// The IDCT function computes the inverse cosine transform of a floating-point image with even number of rows and columns. +// +// Examples +// I = imread('cameraman.tif') +// I = double(I) +// J = IDCT(I) +// imshow(J) +// +// Authors +// Suraj Prakash + + [rows, cols, channel] = size(image); + + if (modulo(rows, 2) <> 0) & (modulo(cols, 2) <> 0) then + error(msprintf("Image doesnot have even number of rows and columns\n")); + elseif (modulo(rows, 2)) <> 0 then + error(msprintf("Image doesnot have even number of rows\n")); + elseif (modulo(cols, 2)) <> 0 then + error(msprintf("Image doesnot have even number of cols\n")); + end + + if channel > 1 then + error(msprintf("Input image should be single channel")); + end + image_list = mattolist(image) + + out = opencv_IDCT(image_list) + + length_out = size(out) + + for i = 1 : length_out + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/IFFT.bin b/macros/IFFT.bin new file mode 100644 index 0000000..ab750d5 Binary files /dev/null and b/macros/IFFT.bin differ diff --git a/macros/IFFT.sci b/macros/IFFT.sci new file mode 100644 index 0000000..7f10c42 --- /dev/null +++ b/macros/IFFT.sci @@ -0,0 +1,31 @@ +function [out]=IFFT(inputimage) +// Computes the inverse descrete fourier transform of image +// +// Calling Sequence +// output_image = IFFT(inputimage); +// +// Parameters +// +// inputimage : Grayscale image +// out_image : IFFT of input image +// +// Description +// This function computes the inverse descrete fourier transform of input image.The image should be grayscale. +// +// Examples +// a = imread('cameraman.tif'); +// b = IFFT(a); +// imshow(b) +// +// Authors: +// +//Diwakar Bhardwaj + + + inputimage1=mattolist(inputimage); + a=opencv_IFFT(inputimage1); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end +endfunction; diff --git a/macros/KalmanFilter.bin b/macros/KalmanFilter.bin new file mode 100644 index 0000000..5c72453 Binary files /dev/null and b/macros/KalmanFilter.bin differ diff --git a/macros/KalmanFilter.sci b/macros/KalmanFilter.sci new file mode 100644 index 0000000..0275099 --- /dev/null +++ b/macros/KalmanFilter.sci @@ -0,0 +1,25 @@ +function [output] = KalmanFilter(varargin) + [lhs,rhs] = argn(0) + if rhs>10 + error(msprintf(" Too many input arguments")); + end + select rhs + case 0 then + a = opencv_KalmanFilter(); + case 2 then + a = opencv_KalmanFilter(varargin(0),varargin(1)); + case 4 then + a = opencv_KalmanFilter(varargin(0),varargin(1),varargin(2),varargin(3)); + case 6 then + a = opencv_KalmanFilter(varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5)); + case 8 then + a = opencv_KalmanFilter(varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7)); + case 10 then + a = opencv_KalmanFilter(varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8),varargin(9)); + end + + d = size(a); + for i = 1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/SURFPoints.bin b/macros/SURFPoints.bin new file mode 100644 index 0000000..2a70e05 Binary files /dev/null and b/macros/SURFPoints.bin differ diff --git a/macros/SURFPoints.sci b/macros/SURFPoints.sci new file mode 100644 index 0000000..ac305d1 --- /dev/null +++ b/macros/SURFPoints.sci @@ -0,0 +1,33 @@ +function varargout=SURFPoints(varargin) +// This function returns a SURFPoints struct for storing SURF interest points. +// +// Calling Sequence +// myStruct = SURFPoints(Location of Interest Points) +// +// Parameters +// results: SURFPoints struct which contains Location, Orientation, Metric, SignOfLaplacian, Scale and Count. +// Location: Location of Interest Point. +// +// Description +// This function returns a SURFPoints struct with default values. +// +// Examples +// image = imread('sample.jpg'); +// results = detectSURFFeatures(image); +// myStruct = SURFPoints(results.KeyPoints); +// +// Authors +// Shashank Shekhar + [lhs rhs]=argn(0); + if lhs<1 then + error(msprintf(" Not enough output arguments. One expected")) + elseif lhs>1 then + error(msprintf(" Too many output arguments. One expected")) + end + if rhs>1 then + error(msprintf(" Too many input arguments. One expected")) + end + [a b c d e f] = ocv_SURFPoints(varargin(1)); + varargout(1) = struct('KeyPoints', a, 'Orientation', b, 'Metric', c ,'SignOfLaplacian', d,'Scale', e, 'Count', f ); +endfunction + diff --git a/macros/TemplateMatcher.bin b/macros/TemplateMatcher.bin new file mode 100644 index 0000000..2c72f06 Binary files /dev/null and b/macros/TemplateMatcher.bin differ diff --git a/macros/TemplateMatcher.sci b/macros/TemplateMatcher.sci new file mode 100644 index 0000000..d5e60d8 --- /dev/null +++ b/macros/TemplateMatcher.sci @@ -0,0 +1,60 @@ +function new_image = TemplateMatcher(image, template_image, varargin) +// Compares a template in overlapped image regions +// +// Calling Sequence +// newimage = TemplateMatcher(image, template_image) +// +// Parameters +// image : 8-bit or 32-bit floating image +// template_image : Searched template. It must not be greater than the source image and have the same data type of input image. +// newimage : Map of comparison results. It must be single channel 32-bit floating-point. Its size is (W-w+1) * (H-h+1) if image is W * H and template_image is w * h +// +// Description +// The function compares the w * h sized images of the original image against the template image and stores the result in the new_image. +// +// Examples +// I = imread('cameraman.tif'); +// Igray = rgb2gray(I); +// T = Igray(20:75, 90:135); +// J = TemplateImage(I, T); +// imshow(J); +// +// Authors +// Suraj Prakash + + + [ lhs rhs ] = argn(0) + if lhs > 1 then + error(msprintf("Too many output argument")) + end + + if rhs > 2 then + error(msprintf("Too many input arguments")) + end + + [imagerows imagecols imagechannel] = size(image) + [t_rows t_cols t_channel] = size(template_image) + if t_rows > imagerows | t_cols > imagecols then + error(msprintf("Template image is greater than image\n")) + end + + image_list = mattolist(image) + template_image_list = mattolist(template_image) + + if rhs == 2 then + + temp = opencv_TemplateMatcher(image_list, template_image_list) + + elseif rhs == 3 then + + temp = opencv_TemplateMatcher(image_list, template_image_list, varargin(1)) + + end + + sz = size(temp) + + for i=1 : sz + new_image(:, :, i) = temp(i) + end + +endfunction diff --git a/macros/VideoReader.bin b/macros/VideoReader.bin new file mode 100644 index 0000000..54b4800 Binary files /dev/null and b/macros/VideoReader.bin differ diff --git a/macros/VideoReader.sci b/macros/VideoReader.sci new file mode 100644 index 0000000..b9ecb76 --- /dev/null +++ b/macros/VideoReader.sci @@ -0,0 +1,37 @@ +function[varargout] = VideoReader(varargin) +// This function creates a SURFPoints struct, needed to read Video Files. +// +// Calling Sequence +// result = VideoReader(filename) +// result = VideoReader(filename, Name, Value) +// +// Parameters +// results: VideoReader Struct containing Name, Duration, FramesPerSecond, CurrentTime, FramesCount, Width, Height and Path. +// filename: name of the video file +// CurrentTime : (optional) The timestamp of video in seconds from the beginning of the video. +// +// Description +// This function returns a VideoReader struct with the properties of the video +// +// Examples +// videoStruct = VideoReader('sample.mp4'); +// +// Authors +// Shashank Shekhar +// Tanmay Chaudhari + [lhs, rhs] = argn(0) + if rhs>3 then + error(msprintf(" Too many input arguments. One expected")) + end + if lhs>1 then + error(msprintf(" Too many output arguments. One expected")) + end + + if rhs==3 then + [FileName, duration, fps, currentTime, frames, width, height, fullPath] = ocv_VideoReader(varargin(1), varargin(2), varargin(3)); + else + [FileName, duration, fps, currentTime, frames, width, height, fullPath] = ocv_VideoReader(varargin(1)); + end + + varargout(1) = struct('Filename', FileName, 'Duration', duration, 'FramesPerSecond', fps, 'CurrentTime', currentTime, 'FramesCount', frames, 'Width', width, 'Height', height, "Path", fullPath); +endfunction diff --git a/macros/adapthisteq.bin b/macros/adapthisteq.bin new file mode 100644 index 0000000..c88934e Binary files /dev/null and b/macros/adapthisteq.bin differ diff --git a/macros/adapthisteq.sci b/macros/adapthisteq.sci new file mode 100644 index 0000000..7d9f17f --- /dev/null +++ b/macros/adapthisteq.sci @@ -0,0 +1,16 @@ +function [outputImg]=adapthisteq(inputImage,varargin) + [lhs rhs]=argn(0); + if rhs>2 + error(msprintf(" Too many input arguments")); + end + inputList=mattolist(inputImage); + select rhs + case 1 then + outputList=opencv_adapthisteq(inputList); + case 2 then + outputList=opencv_adapthisteq(inputList,varargin(1)); + end + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/affine2d.bin b/macros/affine2d.bin new file mode 100644 index 0000000..9c079b7 Binary files /dev/null and b/macros/affine2d.bin differ diff --git a/macros/affine2d.sci b/macros/affine2d.sci new file mode 100644 index 0000000..d0078cd --- /dev/null +++ b/macros/affine2d.sci @@ -0,0 +1,12 @@ +function tform = affine2d(image) + + image_list = mattolist(image) + + out = opencv_affine2d(image_list) + + sz = size(out) + for i=1:sz + tform(:, :, i) = out(i) + end + +endfunction diff --git a/macros/approxpolyDP.bin b/macros/approxpolyDP.bin new file mode 100644 index 0000000..04079f1 Binary files /dev/null and b/macros/approxpolyDP.bin differ diff --git a/macros/approxpolyDP.sci b/macros/approxpolyDP.sci new file mode 100644 index 0000000..616febe --- /dev/null +++ b/macros/approxpolyDP.sci @@ -0,0 +1,5 @@ +function output_curve = approxpolyDP(input_curve, epsilon, closed) + + output_curve = opencv_approxpolyDP(input_curve, epsilon, closed) + +endfunction diff --git a/macros/arclength.bin b/macros/arclength.bin new file mode 100644 index 0000000..8a8c97d Binary files /dev/null and b/macros/arclength.bin differ diff --git a/macros/arclength.sci b/macros/arclength.sci new file mode 100644 index 0000000..88b1480 --- /dev/null +++ b/macros/arclength.sci @@ -0,0 +1,5 @@ +function result = arclength(input_curve, closed) + + result = opencv_arclength(input_curve, closed) + +endfunction diff --git a/macros/arrowedline.bin b/macros/arrowedline.bin new file mode 100644 index 0000000..2ecfabf Binary files /dev/null and b/macros/arrowedline.bin differ diff --git a/macros/arrowedline.sci b/macros/arrowedline.sci new file mode 100644 index 0000000..efbe94e --- /dev/null +++ b/macros/arrowedline.sci @@ -0,0 +1,25 @@ +function[dstImg] = arrowedline(srcImg, x1, y1, x2, y2, R, G, B, varargin) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + select rhs + case 8 then + out = arrowedline(srcMat, x1, y1, x2, y2, R, G, B) + case 9 then + out = arrowedline(srcMat, x1, y1, x2, y2, R, G, B,varargin(1)) + case 10 then + out = arrowedline(srcMat, x1, y1, x2, y2, R, G, B, varargin(1),varargin(2)) + case 11 then + out = arrowedline(srcMat, x1, y1, x2, y2, R, G, B, varargin(1),varargin(2), varargin(3)) + case 12 then + out = arrowedline(srcMAt, x1, y1, x2, y2, R, G, B, varargin(1),varargin(2), varargin(3), varargin(4)) + end + + channels = size(out) + + for i = 1:channels + dstImg(:,:,1) = out(1) + end +endfunction diff --git a/macros/autoCorrelator.bin b/macros/autoCorrelator.bin new file mode 100644 index 0000000..7475f1e Binary files /dev/null and b/macros/autoCorrelator.bin differ diff --git a/macros/autoCorrelator.sci b/macros/autoCorrelator.sci new file mode 100644 index 0000000..991b903 --- /dev/null +++ b/macros/autoCorrelator.sci @@ -0,0 +1,46 @@ +function relation = autoCorrelator(inputMatrix) +//Calculate 2-D auto correlation of input matrix +// +//Calling Sequence +// relation=autoCorrelator(inputMatrix) +// +//Parameters +// inputMatrix: input matrix to calculate auto correlation +// +//Description +//This function computes 2-D auto correlation of input matrix +// +// Examples +// rel = autoCorrelator([3 1;2 4]) // examples of use + + [lhs rhs]=argn(0); + if rhs<1 then + error(msprintf("Wrong arguments: Need atleast one argument")) + end + [r,c,channels] = size(inputMatrix); + if channels<> 1 then + error(msprintf("Auto correlator function do not support more than two dimensions")) + elseif type(inputMatrix)<> 1 + error(msprintf("Auto correlator function do not support input type %s",typeof(inputMatrix))) + end + Rep = zeros(r + r*2-2, c + c*2-2); + for x = r : r+r-1 + for y = c : c+c-1 + Rep(x,y) = inputMatrix(x-r+1, y-c+1); + end + end + conjMatrix=conj(inputMatrix); + //(mROw,mCols)- It indicates middle value of the output matrix + relation = zeros(r+r-1,c+c-1); //output matrix + //calculating only half of the output matrix + for x = 1 : r+r-1 + for y = 1 : c+c-1 + for i = 1 : r + for j = 1 : c + relation(x, y) = relation(x, y) + (Rep(x+i-1, y+j-1) * conjMatrix(i, j)); + end + end + end + end + //Copying remainig values +endfunction diff --git a/macros/bagOfFeatures.bin b/macros/bagOfFeatures.bin new file mode 100644 index 0000000..9d54678 Binary files /dev/null and b/macros/bagOfFeatures.bin differ diff --git a/macros/bagOfFeatures.sci b/macros/bagOfFeatures.sci new file mode 100644 index 0000000..480d775 --- /dev/null +++ b/macros/bagOfFeatures.sci @@ -0,0 +1,50 @@ +function [bag]=bagOfFeatures(imageSet,varargin) +// This function is used to create a Bag-of-Words model (BoW model). +// +// Calling Sequence +// bag = bagOfFeatures(imgSet); +// bag = bagOfFeatures(imgSet, Name, Value, ...); +// +// Parameters +// bag: Bag of visual words +// imgSet: An imageSet structure +// VocabularySize: (Optional) Number of visual words (Default- 500) +// StrongestFeatures: (Optional) Fraction of strongest features (Default- 1.0) +// Upright: (Optional) Orientation of SURF feature vector (Default- true) +// Verbose: (Optional) Enable progress display to screen (Default- true) +// +// Description +// BoW model is applied to classify images by treating image features as words. The bagOfFeatures object contains a bag of visual words that help in image classification. +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// +// Authors +// Rohit Suri +// Umang Agrawal + + [lhs rhs]=argn(0); + if lhs>1 + error(msprintf(" Too many output arguments")); + elseif rhs<1 + error(msprintf("At least one argument is required")); + elseif rhs>9 + error(msprintf(" Too many input arguments")); + end + imageSetList=imageSetToList(imageSet); + select rhs + case 1 then + bagList=opencv_bagOfFeatures(imageSetList); + case 3 then + bagList=opencv_bagOfFeatures(imageSetList,varargin(1),varargin(2)); + case 5 then + bagList=opencv_bagOfFeatures(imageSetList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 7 then + bagList=opencv_bagOfFeatures(imageSetList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + case 9 then + bagList=opencv_bagOfFeatures(imageSetList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8)); + end + bag=struct('FilePath',bagList(2)(1,1),'VocabularySize',bagList(3)(1,1),'StrongestFeatures',bagList(4)(1,1),'Upright',bagList(5)(1,1)); +endfunction diff --git a/macros/bagStructToList.bin b/macros/bagStructToList.bin new file mode 100644 index 0000000..8733a0c Binary files /dev/null and b/macros/bagStructToList.bin differ diff --git a/macros/bagStructToList.sci b/macros/bagStructToList.sci new file mode 100644 index 0000000..6ac8c67 --- /dev/null +++ b/macros/bagStructToList.sci @@ -0,0 +1,16 @@ +function bagList=bagStructToList(bag) + if isstruct(bag)<> %T then + error(msprintf("Error: The input argument is not of type bagOfFeatures.")); + end + fieldNamesActual=["FilePath";"VocabularySize";"StrongestFeatures";"Upright"]; + fieldNamesInput=fieldnames(bag); + if fieldNamesActual <> fieldNamesInput then + error(msprintf("Error: The input argument is not of type bagOfFeatures, Wrong field names")); + end + bagList=list(); + bagList(1)='bagOfFeatures'; + bagList(2)=bag.FilePath; + bagList(3)=bag.VocabularySize; + bagList(4)=bag.StrongestFeatures; + bagList(5)=bag.Upright; +endfunction diff --git a/macros/bbox2points.bin b/macros/bbox2points.bin new file mode 100644 index 0000000..d848915 Binary files /dev/null and b/macros/bbox2points.bin differ diff --git a/macros/bbox2points.sci b/macros/bbox2points.sci new file mode 100644 index 0000000..ca58841 --- /dev/null +++ b/macros/bbox2points.sci @@ -0,0 +1,24 @@ +function [ bbox ] = bbox2points(rectangle) +// Returns list of corner points of a rectangle. +// +// Calling Sequence +// bbox = bbox2points(rectangle); +// +// Parameters +// rectangle: A Nx4 matrix where each row is a rectangle of the form [x, y, width, height]; +// points: Returns 4x2xN size matrix which contains all the 4 co-ordinates of each of the N bounding boxes. +// +// Description +// List of corner points of a rectangle. +// +// Examples +// bbox = [1 2 3 4; 5 6 7 8]; +// results = bbox2points(bbox); +// +// Authors +// Tanmay Chaudhari + + a=opencv_bbox2points(rectangle); + out(:,:,1)=a(1); + +endfunction diff --git a/macros/bboxOverlapRatio.bin b/macros/bboxOverlapRatio.bin new file mode 100644 index 0000000..e52928a Binary files /dev/null and b/macros/bboxOverlapRatio.bin differ diff --git a/macros/bboxOverlapRatio.sci b/macros/bboxOverlapRatio.sci new file mode 100644 index 0000000..682b8c8 --- /dev/null +++ b/macros/bboxOverlapRatio.sci @@ -0,0 +1,37 @@ +function [ overlapRatio ] = bboxOverlapRatio(bboxA, bboxB, ratioType) +// Compute the overlap ratio between the bounding boxes A and B. +// +// Calling Sequence +// [ overlapRatio ] = bboxOverlapRatio(bboxA, bboxB); +// [ overlapRatio ] = bboxOverlapRatio(bboxA, bboxB, ratioType); +// +// Parameters +// bboxA: Bounding box A of the form [x, y, width, height]; +// bboxB: Boundng box B of the form [x, y, width, height]; +// ratioType (Optional Argument): Method to be used to compute the ratio. Set this to either 'Union' or 'Min'. Default computation method is set to 'Union'. +// overlapRatio: Overlap ratio between the bounding boxes A and B. +// +// Description +// Compute the overlap ratio between the bounding boxes A and B. +// +// Examples +// bboxA = [1 2 3 4]; +// bboxB = bboxA + 100; +// overlapRatioMin = bboxOverlapRatio(bboxA, bboxB, 'Min'); +// overlapRatioUnion = bboxOverlapRatio(bboxA, bboxB); +// +// Authors +// Tanmay Chaudhari + + [lhs rhs] = argn(0) + if rhs>3 then + error(msprintf("Too many input arguments")) + elseif rhs==3 then + a=opencv_bboxOverlapRatio(input1,input2,varargin(1)) + out=a + elseif rhs==2 then + a=opencv_bboxOverlapRatio(input1,input2) + out=a + end + +endfunction diff --git a/macros/bilateralFilter.bin b/macros/bilateralFilter.bin new file mode 100644 index 0000000..c824f64 Binary files /dev/null and b/macros/bilateralFilter.bin differ diff --git a/macros/bilateralFilter.sci b/macros/bilateralFilter.sci new file mode 100644 index 0000000..c686363 --- /dev/null +++ b/macros/bilateralFilter.sci @@ -0,0 +1,13 @@ +function new_image = bilateralFilter(image, d, sigmaColor, sigmaSpace) + + image_list = mattolist(image) + + out = opencv_bilateralFilter(image_list, d, sigmaColor, sigmaSpace) + + sz = size(out) + + for i = 1: sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/binaryFeatures.bin b/macros/binaryFeatures.bin new file mode 100644 index 0000000..ec5b6c5 Binary files /dev/null and b/macros/binaryFeatures.bin differ diff --git a/macros/binaryFeatures.sci b/macros/binaryFeatures.sci new file mode 100644 index 0000000..bd009c9 --- /dev/null +++ b/macros/binaryFeatures.sci @@ -0,0 +1,34 @@ +function [features]=binaryFeatures(featureVectors) +// Object for storing Feature Vectors +// +// Calling Sequence +// features = binaryFeatures(featureVector); +// +// Parameters +// featureVector: M-by-N matrix consisting of M features stored in N uint8 containers +// features: Binary Feature object for the extracted Features +// +// Description +// This object provides the ability to pass data between extractFeatures and matchFeatures function +// +// Examples +// image_1 = imread('sample1.jpg'); +// points_1 = detectFASTFeatures(image_1); +// [ f1 vpts_1 ] = extractFeatures(image_1, points_1); +// features1 = binaryFeatures(f1); +// +// Authors +// Umang Agrawal +// Sridhar Reddy + + [lhs rhs]=argn(0); + if lhs>1 + error(msprintf(" Too many output arguments")); + elseif rhs>1 + error(msprintf(" Too many input arguments")); + elseif inttype(featureVectors) <> 11 then + error(msprintf("wrong argument #%d: FeatureVectors must be uint8",1)); + end + [rows cols]=size(featureVectors); + features=struct('Type','binaryFeatures','Features',featureVectors,'NumBits',cols*8,'NumFeatures',rows); +endfunction diff --git a/macros/blobAnalysis.bin b/macros/blobAnalysis.bin new file mode 100644 index 0000000..07e0889 Binary files /dev/null and b/macros/blobAnalysis.bin differ diff --git a/macros/blobAnalysis.sci b/macros/blobAnalysis.sci new file mode 100644 index 0000000..216ab7e --- /dev/null +++ b/macros/blobAnalysis.sci @@ -0,0 +1,103 @@ +function [blob] = blobAnalysis(srcImg, varargin) +// Detects blob in the source image +// +// Calling Sequence +// [blob] = blobAnalysis(srcImg) +// [blob] = blobAnalysis(srcImg, Name, Value) +// +// Parameters +// srcImg: The input image Matrix +// Name: filteration method +// Value: filteration method constraints, [1X2] vector +// blob: stores different parameters of the blob +// +// Description +// The function uses SimpleBlobDetector function to detect the blobs then it checks for different Name Value pair arguments and accordingly returns the parameters of the blob such as 2D coordinates of the blob, size of the blob. +// +// The Name-Value pair may be any of following types :- +// +// bool filterByArea, vector [minArea maxArea] +// bool filterByCircularity, vector [minCircularity maxCircularity] +// bool filterByConvexity, vector [minConvexity maxConvexity] +// bool filterByThreshold, vector [minThreshold maxThreshold] +// +// +// Examples +// [srcImg] = imread('blobdetection.jpg'); +// [blob] = blobAnalysis(srcImg); +// [blob] = blobAnalysis(srcImg, "filterByArea", [0.01 1]); +// +// Authors +// Deepshikha + +[lhs,rhs] = argn(0) + + // To check the number of input and output arguments + + if rhs<1 then + error(msprintf(" Not enough input arguments")) + elseif rhs>10 then + error(msprintf(" Too many input arguments to the function")) + elseif lhs<1 then + error(msprintf(" Not enough output arguments")) + elseif lhs>1 then + error(msprintf(" Too many output arguments")) + end + + srcMat = mattolist(srcImg) + + if modulo(rhs,2) == 0 then + error(msprintf("Number of input arguments must be odd")) + end + + select rhs + case 1 then + output = opencv_blobAnalysis(srcMat) + + case 3 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + output = opencv_blobAnalysis(srcMat, varargin(1), varargin(2)) + + case 5 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + output = opencv_blobAnalysis(srcMat, varargin(1), varargin(2), varargin(3), varargin(4)) + + case 7 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + output = opencv_blobAnalysis(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + + case 9 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7))<> "string" + error(msprintf("argument at position 8 must be string")) + end + output = opencv_blobAnalysis(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + end + + blob = struct("Points", output(1), "Size", output(2)) + +endfunction + diff --git a/macros/blur.bin b/macros/blur.bin new file mode 100644 index 0000000..789ec51 Binary files /dev/null and b/macros/blur.bin differ diff --git a/macros/blur.sci b/macros/blur.sci new file mode 100644 index 0000000..4b3bcd0 --- /dev/null +++ b/macros/blur.sci @@ -0,0 +1,9 @@ +function [out]=blur(input_image ,ksize_width,ksize_height,anchorX,anchorY) + input_image1=mattolist(input_image); + a=opencv_blur(input_image1, ksize_width,ksize_height,anchorX,anchorY); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/borderInterpolate.bin b/macros/borderInterpolate.bin new file mode 100644 index 0000000..138900b Binary files /dev/null and b/macros/borderInterpolate.bin differ diff --git a/macros/borderInterpolate.sci b/macros/borderInterpolate.sci new file mode 100644 index 0000000..1f9323d --- /dev/null +++ b/macros/borderInterpolate.sci @@ -0,0 +1,6 @@ + +function result = borderInterpolate(p, len, borderType) + + result = opencv_borderInterpolate(p, len, borderType) + +endfunction diff --git a/macros/boundingRect.bin b/macros/boundingRect.bin new file mode 100644 index 0000000..7c2fd69 Binary files /dev/null and b/macros/boundingRect.bin differ diff --git a/macros/boundingRect.sci b/macros/boundingRect.sci new file mode 100644 index 0000000..9c8e882 --- /dev/null +++ b/macros/boundingRect.sci @@ -0,0 +1,3 @@ +function [rectMat]=boundingRect(points) + rectMat=opencv_boundingRect(points); +endfunction diff --git a/macros/boxfilter.bin b/macros/boxfilter.bin new file mode 100644 index 0000000..099d375 Binary files /dev/null and b/macros/boxfilter.bin differ diff --git a/macros/boxfilter.sci b/macros/boxfilter.sci new file mode 100644 index 0000000..a29f3a5 --- /dev/null +++ b/macros/boxfilter.sci @@ -0,0 +1,9 @@ +function [out]=boxfilter(InputArraysrc, intddepth,size1x,size1y,pointx,pointy, bool_normalize) + input_image1=mattolist(InputArraysrc); + a=opencv_boxfilter(input_image1, intddepth,size1x,size1y,pointx,pointy, bool_normalize); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/buildmacros.sce b/macros/buildmacros.sce new file mode 100644 index 0000000..b4c63c7 --- /dev/null +++ b/macros/buildmacros.sce @@ -0,0 +1,2 @@ +tbx_build_macros("Image",get_absolute_file_path("buildmacros.sce")); +clear tbx_build_macros; diff --git a/macros/bundleAdjustment.bin b/macros/bundleAdjustment.bin new file mode 100644 index 0000000..628938e Binary files /dev/null and b/macros/bundleAdjustment.bin differ diff --git a/macros/bundleAdjustment.sci b/macros/bundleAdjustment.sci new file mode 100644 index 0000000..73d0ca4 --- /dev/null +++ b/macros/bundleAdjustment.sci @@ -0,0 +1,29 @@ +function xyzrefinedPoints = bundleAdjustment(_3dpoints, imagepoints, visibility, cameramatrix, rotation, translation, distcoeffs) +// Refine camera poses and 3-d points +// +// Calling Sequence +// xyzrefinedPoints = bundleAdjustment(_3dpoints, imagepoints, visibility, cameramatrix, rotation, translation, distcoeffs) +// +// Parameters +// _3dpoints : N * 3 object points +// imagepoints : M * N * 2 image points for each camera and each points +// visibility : M * N * 1 visibility matrix, element[i][j] = 1 when object point i is visible from camera j and 0 if not +// cameramatrix : M * 3 * 3 camera matrix(intrinsic parameters) 3 * 3 camera matrix for each image +// rotation : M * 3 * 3 rotation matrix for each image +// translation : M * 3 * 1 translation matrix for each image +// distcoeffs : M * (4 * 5 * 8) * 1 distortion coefficient for each image +// xyzrefinedPoints : Refined N * 3 object points +// +// Description +// The function returns the refined 3-D points. The refinement procedure uses Levenberg-Marquardt algorithm. +// +// Examples +// xyzrefinedPoints = bundleAdjustment(_3dpoints, imagepoints, visibility, cameramatrix, rotation, translation, distcoeffs) +// +// Authors +// Suraj Prakash + + + xyzrefinedPoints = opencv_bundleAdjustment(_3dpoints, imagepoints, visibility, cameramatrix, rotation, translation, distcoeffs) + +endfunction diff --git a/macros/bwlookup.bin b/macros/bwlookup.bin new file mode 100644 index 0000000..0051716 Binary files /dev/null and b/macros/bwlookup.bin differ diff --git a/macros/bwlookup.sci b/macros/bwlookup.sci new file mode 100644 index 0000000..5ab8f66 --- /dev/null +++ b/macros/bwlookup.sci @@ -0,0 +1,8 @@ +function [out]=bwlookup(image,lut) + image1=mattolist(image); + a=opencv_bwlookup(image1,lut); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end +endfunction; diff --git a/macros/bwulterode.bin b/macros/bwulterode.bin new file mode 100644 index 0000000..3c985a5 Binary files /dev/null and b/macros/bwulterode.bin differ diff --git a/macros/bwulterode.sci b/macros/bwulterode.sci new file mode 100644 index 0000000..501c50d --- /dev/null +++ b/macros/bwulterode.sci @@ -0,0 +1,13 @@ +function[dstImg] = bwulterode(srcImg) + + srcMat = mattolist(srcImg) + + out = opencv_bwulterode(srcMat) + + channels = size(out) + + for i = 1:channels + dstImg(:,:,i) = out(i) + end + +endfunction diff --git a/macros/cameraMatrix.bin b/macros/cameraMatrix.bin new file mode 100644 index 0000000..baea024 Binary files /dev/null and b/macros/cameraMatrix.bin differ diff --git a/macros/cameraMatrix.sci b/macros/cameraMatrix.sci new file mode 100644 index 0000000..de177c9 --- /dev/null +++ b/macros/cameraMatrix.sci @@ -0,0 +1,28 @@ +function [ camMatrix ] = cameraMatrix(instrinsicMatrix,rotationMatrix,translationVector) +// Returns camera projection matrix. +// +// Calling Sequence +// camMatrix = cameraMatrix(instrinsicMatrix,rotationMatrix,translationVector); +// +// Parameters +// camMatrix: A 4x3 camera projection matrix, which can be used to project 3-D world points in homogenous co-ordinates into an image. +// instrinsicMatrix: Projection matrix of size 3x3. +// rotationMatrix: A 3x3 matrix which specifies the rotation of a camera. +// translationVector: A 1x3 vector which specifies the translation of a camera. +// +// Description +// Return a matrix of size 4-by-3, which can be used to project 3-D world points in homogenous co-ordinates into an image. +// +// Examples +// instrinsicMatrix = [1 0 0; 1 2 0; 3 4 0]; +// rotationMatrix = [ 0.1417 -0.7409 0.6565; 0.9661 -0.0410 -0.2548; 0.2157 0.6703 0.7100]; +// translationVector = [ -29.2584 35.7824 725.5824]; +// camMatrix = cameraMatrix(instrinsicMatrix,rotationMatrix,translationVector) +// +// Authors +// Tanmay Chaudhari + + a=opencv_cameraMatrix(instrinsicMatrix,rotationMatrix,translationVector); + camMatrix(:,:,1)=a(1); + +endfunction diff --git a/macros/cameraParameters.bin b/macros/cameraParameters.bin new file mode 100644 index 0000000..bd05ac5 Binary files /dev/null and b/macros/cameraParameters.bin differ diff --git a/macros/cameraParameters.sci b/macros/cameraParameters.sci new file mode 100644 index 0000000..9d2d131 --- /dev/null +++ b/macros/cameraParameters.sci @@ -0,0 +1,242 @@ +function varargout=cameraParameters(varargin) +// It returns the cameraParameters structure +// +// Calling Sequence +// cameraParams=cameraParameters(Name,value); +// +// Parameters +// IntrinsicMatrix: 3-by-3 matrix, it specifies the principal point, skew and focal length. Default- 3-by-3 identity matrix +// RadialDistortion: 2 or 3 element vector. Default- [0 0 0] +// TangentialDistortion: 2 element vector. Default- [0 0] +// RotationVectors: P-by-3 matrix. Each row of the matrix represents 3-D rotation of the camera. Default- [] +// TranslationVectors: P-by-3 matrix. Each row of the matrix represents 3-D translation of the camera. Number of rotation vectors should be equal to number of transation vectors. Default- [] +// WorldPoints: M-by-2 matrix. M indicates the number of keypoints. Default- [] +// WorldUnits: Unit of measure of world points. Default- "mm" +// EstimateSkew: logical scalar. Default- %F +// NumRadialDistortionCoefficients: 2 or 3. Default- 2 +// EstimateTangentialDistortion: logical scalar. Default- %F +// ReprojectionErrors: M-by-2-p array. Number of channels must be equal to number of rotation vectors and number of rows should be equal to number of world points. Default-[] +// +// Description +// This function creates cameraParameters struct based on the arguments provided. The struct contains intrinsic, extrinsic, estimation and lens properties. +// +// Examples +// cameraParms=cameraParams("IntrinsicMatrix", [2 0 0; 0 3 0;4 5 1]); +// Output: +// IntrinsicMatrix: [3x3 constant] +// PrincipalPoint: [4,5] +// FocalLength: [2,3] +// Skew: 0 +// RadialDistortion: [0,0,0] +// TangentialDistortion: [0,0] +// RotationMatrices: [0x0 constant] +// RotationVectors: [0x0 constant] +// TranslationVectors: [0x0 constant] +// meanReprojectionError: 0 +// ReprojectionErrors: [0x0 constant] +// ReprojectedPoints: [0x0 constant] +// NumPatterns: 0 +// WorldPoints: [0x0 constant] +// WorldUnits: "mm" +// EstimateSkew: 0 +// NumRadialDistortionCoefficients: 2 +// EstimateTangentialDistortion: 0 + + [lhs rhs]=argn(0); + if lhs<1 then + error(msprintf(" Not enough output arguments")) + elseif lhs>1 then + error(msprintf(" Too many output arguments to the function")) + end + + //default values + IntrinsicMatrix=[1 0 0;0 1 0;0 0 1]; + RadialDistortion=[0 0 0]; + TangentialDistortion=[0 0]; + RotationVectors=[]; + TranslationVectors=[]; + WorldPoints=[]; + WorldUnits='mm'; + EstimateSkew=0; + NumRadialDistortionCoefficients=2; + EstimateTangentialDistortion=0; + ReprojectionErrors=[]; + + imRows=0,imCols=0; + rdRows=0,rdCols=0; + tdRows=0,tdCols=0; + rvRows=0,rvCols=0; + tvRows=0,tvCols=0; + wpRows=0,wpCols=0; + + if modulo(rhs,2) then + error(msprintf(" Wrong number of input arguments")); + end + + for i=1:2:rhs + if strcmpi(varargin(i),"IntrinsicMatrix")==0 then + i=i+1; + IntrinsicMatrix=varargin(i); + [imRows imCols]=size(IntrinsicMatrix); + if ~imRows==3 | ~imCols==3 then + error(msprintf(" wrong value for the input argument %d, IntrinsicMatrix must be 3*3",i)) + end + + elseif strcmpi(varargin(i),'RadialDistortion')==0 then + i=i+1; + RadialDistortion=varargin(i); + [rdRows rdCols]=size(RadialDistortion); + if ~rdRows==1 | (~rdCols==2 & ~rdCols==3) then + error(msprintf(" wrong value for the input argument %d, RadialDistortion must be a vector with 2 or 3 elements",i)) + end + + elseif strcmpi(varargin(i),'TangentialDistortion')==0 then + i=i+1; + TangentialDistortion=varargin(i); + [tdRows tdCols]=size(TangentialDistortion); + if ~tdRows==1 | ~tdCols==2 then + error(msprintf(" wrong value for the input argument %d, TangentialDistortion must be 2-element vector",i)) + end + + elseif strcmpi(varargin(i),'RotationVectors')==0 then + i=i+1; + RotationVectors=varargin(i); + [rvRows rvCols]=size(RotationVectors); + if ~rvCols==3 then + error(msprintf(" wrong value for the input argument %d, RotationVectors must be M*3",i)) + end + + elseif strcmpi(varargin(i),'TranslationVectors')==0 then + i=i+1; + TranslationVectors=varargin(i); + [tvRows tvCols]=size(TranslationVectors); + if ~tvCols==3 then + error(msprintf(" wrong value for the input argument %d, TranslationVectors must be M*3",i)) + end + + elseif strcmpi(varargin(i),'WorldPoints')==0 then + i=i+1; + WorldPoints=varargin(i); + [wpRows wpCols]=size(WorldPoints); + if ~wpCols==2 then + error(msprintf(" wrong value for the input argument %d, WorldPoints must be M*2",i)) + end + + elseif strcmpi(varargin(i),'WorldUnits')==0 then + i=i+1; + WorldUnits=varargin(i); + if ~typeof(WorldUnits)=='string' + error(msprintf(" wrong value for the input argument %d, WorldUnits must be string",i)) + end + + elseif strcmpi(varargin(i),'EstimateSkew')==0 then + i=i+1; + EstimateSkew=varargin(i); + if EstimateSkew==%T + EstimateSkew=1; + elseif EstimateSkew==%F + EstimateSkew=0; + else + error(msprintf(" wrong value for the input argument %d,EstimateSkew must be logical scalar",i)) + end + + elseif strcmpi(varargin(i),'NumRadialDistortionCoefficients')==0 then + i=i+1; + NumRadialDistortionCoefficients=varargin(i); + if ~(NumRadialDistortionCoefficients==2 | NumRadialDistortionCoefficients==3) then + error(msprintf(" wrong value for the input argument %d, NumRadialDistortionCoefficients must be 2 or 3",i)) + end + + elseif strcmpi(varargin(i),'EstimateTangentialDistortion')==0 then + i=i+1; + EstimateTangentialDistortion=varargin(i); + EstimateSkew=varargin(i); + if EstimateSkew==%T + EstimateSkew=1; + elseif EstimateSkew==%F + EstimateSkew=0; + else + error(msprintf(" wrong value for the input argument %d, EstimateTangentialDistortion must be logical scalar",i)) + end + + elseif strcmpi(varargin(i),'ReprojectionErrors')==0 then + i=i+1; + ReprojectionErrors=varargin(i); + [reRows reCols reChannels]=size(ReprojectionErrors); + if ~reCols==2 then + error(msprintf(" wrong value for the input argument %d, ReprojectionErrors must be M-by-2-by-P array",i)) + end + + else + error(msprintf(" Wrong value for the input argument %d, %s is not a recognised parameter",i,varargin(i))); + end + end + + if ~rvRows==tvRows then + error(msprintf(" wrong value for the input arguments, RotationalVectors and TranslationVectors must be of same size")) + end + if ~isempty(ReprojectionErrors) then + if ~reRows==wpRows then + error(msprintf("wrong value for the input arguments, World points and ReprojectctionErrors must contain same number of rows")) + end + if ~reChannels==rvRows then + error(msprintf("wrong value for the input arguments, Number of patterns in ReprojectionErrors and RotationVectors must be of same size")) + end + end + + RotationMatrices=[] + FocalLength=[IntrinsicMatrix(1,1) IntrinsicMatrix(2,2)]; + PrincipalPoint=[IntrinsicMatrix(3,1) IntrinsicMatrix(3,2)]; + Skew=IntrinsicMatrix(2,1); + for i=1:rvRows + RotationVector=RotationVectors(i,:); + angle=norm(RotationVector); + if angle < %eps + RotationMatrices(:,:,i) = eye(3); + else + n= RotationVector ./ angle; + A = [n(1)^2, n(1)*n(2), n(1)*n(3);... + n(2)*n(1), n(2)^2, n(2)*n(3);... + n(3)*n(1), n(3)*n(2), n(3)^2 ]; + + B = [ 0, -n(3), n(2);... + n(3), 0, -n(1);... + -n(2), n(1), 0 ]; + RotationMatrices(:,:,i) = eye(3) * cos(angle) + (1 - cos(angle)) * A + sin(angle) * B; + end + end + + distCoeffs(1,1)=RadialDistortion(1,1); + distCoeffs(1,2)=RadialDistortion(1,2); + distCoeffs(1,3)=TangentialDistortion(1,1); + distCoeffs(1,4)=TangentialDistortion(1,2); + if rdCols==3 + distCoeffs(1,5)=RadiallDistortion(1,3); + else + distCoeffs(1,5)=0; + end + + ReprojectedPoints=[]; + meanReprojectionError=0; + NumPatterns=rvRows; + if(WorldPoints <> []) + for i=1:NumPatterns + ReprojectedPoints(:,:,i)=opencv_projectPoints(WorldPoints,RotationVectors(i,:),TranslationVectors(i,:),IntrinsicMatrix,distCoeffs); + end + end + if ~isempty(ReprojectionErrors) then + ImagePoints=ReprojectedPoints-ReprojectionErrors; + totalErr=0; + for i=1:rvRows + err=0; + for j=1:wpRows + err=err+((ReprojectedPoints(j,1,i)-ImagePoints(j,1,i))+(ReprojectedPoints(j,2,i)-ImagePoints(j,2,i))).^2; + end + totalErr=totalErr+err; + end + totalPoints=rvRows*wpRows; + meanReprojectionError=sqrt(totalErr/totalPoints); + end + + varargout(1)=struct('IntrinsicMatrix',IntrinsicMatrix,'PrincipalPoint',PrincipalPoint,'FocalLength',FocalLength,'Skew',Skew,'RadialDistortion',RadialDistortion,'TangentialDistortion',TangentialDistortion,'RotationMatrices',RotationMatrices,'RotationVectors',RotationVectors,'TranslationVectors',TranslationVectors,'meanReprojectionError',meanReprojectionError,'ReprojectionErrors',ReprojectionErrors,'ReprojectedPoints',ReprojectedPoints,'NumPatterns',NumPatterns,'WorldPoints',WorldPoints,'WorldUnits',WorldUnits,'EstimateSkew',EstimateSkew,'NumRadialDistortionCoefficients',NumRadialDistortionCoefficients,'EstimateTangentialDistortion',EstimateTangentialDistortion); +endfunction; diff --git a/macros/canny.bin b/macros/canny.bin new file mode 100644 index 0000000..f7f0833 Binary files /dev/null and b/macros/canny.bin differ diff --git a/macros/canny.sci b/macros/canny.sci new file mode 100644 index 0000000..0cb9ed7 --- /dev/null +++ b/macros/canny.sci @@ -0,0 +1,9 @@ +function [out]=canny(input_image ,aperture, threshold1, threshold2, gradient) + input_image1=mattolist(input_image); + a=opencv_canny(input_image1 , aperture, threshold1, threshold2, gradient); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/checkCCE.bin b/macros/checkCCE.bin new file mode 100644 index 0000000..18213fb Binary files /dev/null and b/macros/checkCCE.bin differ diff --git a/macros/checkCCE.sci b/macros/checkCCE.sci new file mode 100644 index 0000000..a6396e9 --- /dev/null +++ b/macros/checkCCE.sci @@ -0,0 +1,11 @@ +function checkCCE(varargin) + errors = varargin(1) + + if ~isfield(errors, 'IntrinsicsErrors' ) | ~isfield(errors, 'ExtrinsicsErrors') then + error(msprintf("A CameraCalibrationErrors Struct expected.")) + end + + checkCIE(errors.IntrinsicsErrors); + checkCEE(errors.ExtrinsicsErrors); +endfunction + diff --git a/macros/checkCEE.bin b/macros/checkCEE.bin new file mode 100644 index 0000000..a22b151 Binary files /dev/null and b/macros/checkCEE.bin differ diff --git a/macros/checkCEE.sci b/macros/checkCEE.sci new file mode 100644 index 0000000..bd868ef --- /dev/null +++ b/macros/checkCEE.sci @@ -0,0 +1,22 @@ +function checkCEE(varargin) + extErr = varargin(1) + + if ~isfield(extErr, 'RotVectorsError') | ~isfield(extErr, 'TransVectorsError') then + error(msprintf("An ExtrinsicsEstimationErrors Struct expected.")) + end + + rvErr = extErr.RotVectorsError; + tvErr = extErr.TransVectorsError; + + if type(rvErr)~=1 | type(tvErr)~=1 | ~isreal(rvErr) | ~isreal(tvErr) then + error(msprintf("An ExtrinsicsEstimationErrors Struct expected.")) + end + + [size11 size12] = size(rvErr); + [size11 size12] = size(tvErr); + + if size12~=3 | size11==0 | size11==0 | size12~=3 then + error(msprintf("An ExtrinsicsEstimationErrors Struct expected.")) + end + +endfunction diff --git a/macros/checkCIE.bin b/macros/checkCIE.bin new file mode 100644 index 0000000..695ba78 Binary files /dev/null and b/macros/checkCIE.bin differ diff --git a/macros/checkCIE.sci b/macros/checkCIE.sci new file mode 100644 index 0000000..37cbdbe --- /dev/null +++ b/macros/checkCIE.sci @@ -0,0 +1,27 @@ +function checkCIE(varargin) + intErr = varargin(1) + if ~isfield(intErr, 'SkewError') | ~isfield(intErr,'FocalLengthError' ) | ~isfield(intErr, 'PrincipalPointError') | ~isfield(intErr, 'RadDistortionError') | ~isfield(intErr, 'TanDistortionError' ) then + error(msprintf("An IntrinsicsEstimationErrors Struct expected.")) + end + + skewErr = intErr.SkewError; + flErr = intErr.FocalLengthError; + ppErr = intErr.PrincipalPointError; + rdErr = intErr.RadDistortionError; + tdErr = intErr.TanDistortionError; + + if type(skewErr)~=1 | type(flErr)~=1 | type(ppErr)~=1 | type(rdErr)~=1 | type(tdErr)~=1 | ~isreal(skewErr) | ~isreal(flErr) | ~isreal(ppErr) | ~isreal(rdErr) | ~isreal(tdErr) then + error(msprintf("An IntrinsicsEstimationErrors Struct expected.")) + end + + [size11 size12] = size(skewErr); + [size21 size22] = size(flErr); + [size31 size32] = size(ppErr); + [size41 size42] = size(rdErr); + [size51 size52] = size(tdErr); + + if (size11*size12)~=1 | (size21*size22)~=2 | (size31*size32)~=2 | (size41*size42)<2 | (size41*size42)>3 | (size51*size52)~=2 then + error(msprintf("An IntrinsicsEstimationErrors Struct expected.")) + end + +endfunction diff --git a/macros/checkSCE.bin b/macros/checkSCE.bin new file mode 100644 index 0000000..70b5477 Binary files /dev/null and b/macros/checkSCE.bin differ diff --git a/macros/checkSCE.sci b/macros/checkSCE.sci new file mode 100644 index 0000000..eea8fc9 --- /dev/null +++ b/macros/checkSCE.sci @@ -0,0 +1,29 @@ +function checkSCE(varargin) + stErrors = varargin(1) + + if ~isfield(stErrors,'Camera1IntrinsicsErrors' ) | ~isfield(stErrors,'Camera1ExtrinsicsErrors' ) | ~isfield(stErrors,'Camera2IntrinsicsErrors' ) | ~isfield(stErrors, 'RotationOfCamera2Error' ) | ~isfield(stErrors, 'TransOfCamera2Error') then + error(msprintf("A StereoCalibrationErrors struct expected.")) + end + + cam1intErr = stErrors.Camera1IntrinsicsErrors; + cam1extErr = stErrors.Camera1ExtrinsicsErrors; + cam2intErr = stErrors.Camera2IntrinsicsErrors; + + checkCIE(cam1intErr); + checkCEE(cam1extErr); + checkCIE(cam2intErr); + rotErr = stErrors.RotationOfCamera2Error; + transErr = stErrors.TransOfCamera2Error; + + if type(rotErr)~=1 | type(transErr)~=1 | ~isreal(rotErr) | ~isreal(transErr) then + error(msprintf("A StereoCalibrationErrors struct expected.")) + end + + [size11 size12] = size(rotErr); + [size21 size22] = size(transErr); + + if size11*size12~=3 | size21*size22~=3 then + error(msprintf("A StereoCalibrationErrors struct expected.")) + end +endfunction + diff --git a/macros/checkStereoParam.bin b/macros/checkStereoParam.bin new file mode 100644 index 0000000..f5c29d4 Binary files /dev/null and b/macros/checkStereoParam.bin differ diff --git a/macros/checkStereoParam.sci b/macros/checkStereoParam.sci new file mode 100644 index 0000000..b064f90 --- /dev/null +++ b/macros/checkStereoParam.sci @@ -0,0 +1,33 @@ +function checkStereoParam(varargin) + stereoParam = varargin(1); + + if ~isfield(stereoParam, 'CameraParameters1') | ~isfield(stereoParam,'CameraParameters2') | ~isfield(stereoParam, 'RotationOfCamera2') | ~isfield(stereoParam, 'TranslationOfCamera2') | ~isfield(stereoParam, 'FundamentalMatrix') | ~isfield(stereoParam, 'EssentialMatrix') | ~isfield(stereoParam, 'MeanReprojectionError') | ~isfield(stereoParam, 'NumPatterns') | ~isfield(stereoParam, 'WorldPoints') | ~isfield(stereoParam, 'WorldUnits') then + error(msprintf("A Stereo Paramter struct expected.")) + end + + cam1 = stereoParam.CameraParameters1; + cam2 = stereoParam.CameraParameters2; + checkCameraParam(cam1); + checkCameraParam(cam2); + + rotCam = stereoParam.RotationOfCamera2; + transCam = stereoParam.TranslationOfCamera2; + funMat = stereoParam.FundamentalMatrix; + essenMat = stereoParam.EssentialMatrix; + mre = stereoParam.MeanReprojectionError; + numPat = stereoParam.NumPatterns; + worldPoints = stereoParam.WorldPoints; + worldUnits = stereoParam.WorldUnits; + + if type(rotCam)~=1 | type(transCam)~=1 | type(funMat)~=1 | type(essenMat)~=1 | type(mre)~=1 | type(numPat)~=1 | type(worldPoints)~=1 | type(worldUnits)~=10 | ~isreal(rotCam) | ~isreal(transCam) | ~isreal(funMat) | ~isreal(essenMat) | ~isreal(mre) | ~isreal(numPat) | ~isreal(worldPoints) then + error(msprintf("A Stereo Paramter struct expected.")) + end + + [size11 size12] = size(rotCam); + [size21 size22] = size(transCam); + + if (size11*size12)~=9 | (size21*size22)~=3 then + error(msprintf("A Stereo Paramter struct expected.")) + end + +endfunction diff --git a/macros/circle.bin b/macros/circle.bin new file mode 100644 index 0000000..4828201 Binary files /dev/null and b/macros/circle.bin differ diff --git a/macros/circle.sci b/macros/circle.sci new file mode 100644 index 0000000..37b8dfe --- /dev/null +++ b/macros/circle.sci @@ -0,0 +1,9 @@ +function [out]=circle(input_image , x_coordinate_of_centre , y_coordinate_centre , radius, r_value,g_value,b_value,thickness,linetype,shift) + input_image1=mattolist(input_image); + a=opencv_circle(input_image1 , x_coordinate_of_centre , y_coordinate_centre , radius, r_value,g_value,b_value,thickness,linetype,shift); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/classifierToList.bin b/macros/classifierToList.bin new file mode 100644 index 0000000..3241106 Binary files /dev/null and b/macros/classifierToList.bin differ diff --git a/macros/classifierToList.sci b/macros/classifierToList.sci new file mode 100644 index 0000000..98fad46 --- /dev/null +++ b/macros/classifierToList.sci @@ -0,0 +1,10 @@ +function classifier_list = classifierToList(classifier) + /// classifier is a struct + + if ~isstruct(classifier) + error(msprintf("Structure of classifier required\n")); + end + + classifier_list = list("classifier",classifier.ClassifierLocation, classifier.BagofFeaturesLocation, classifier.Description); + +endfunction diff --git a/macros/clipline.bin b/macros/clipline.bin new file mode 100644 index 0000000..6e60bfd Binary files /dev/null and b/macros/clipline.bin differ diff --git a/macros/clipline.sci b/macros/clipline.sci new file mode 100644 index 0000000..9f177f1 --- /dev/null +++ b/macros/clipline.sci @@ -0,0 +1,3 @@ +function [out]=clipline(width, height, x1 , y1 , x2,y2) + out=opencv_clipline(width, height, x1 , y1 , x2,y2); +endfunction; diff --git a/macros/contourarea.bin b/macros/contourarea.bin new file mode 100644 index 0000000..776b70f Binary files /dev/null and b/macros/contourarea.bin differ diff --git a/macros/contourarea.sci b/macros/contourarea.sci new file mode 100644 index 0000000..feeb7d1 --- /dev/null +++ b/macros/contourarea.sci @@ -0,0 +1,3 @@ +function [out]=contourarea(InputArraycontour, booloriented) + out=opencv_contourarea(InputArraycontour, booloriented); +endfunction diff --git a/macros/convexhull.bin b/macros/convexhull.bin new file mode 100644 index 0000000..72abcd3 Binary files /dev/null and b/macros/convexhull.bin differ diff --git a/macros/convexhull.sci b/macros/convexhull.sci new file mode 100644 index 0000000..0937f55 --- /dev/null +++ b/macros/convexhull.sci @@ -0,0 +1,4 @@ +function [out]=convexhull(pstData,clkwise,returnpoints) + out=opencv_convexhull(pstData,clkwise,returnpoints); + +endfunction; diff --git a/macros/convmtx2.bin b/macros/convmtx2.bin new file mode 100644 index 0000000..906812b Binary files /dev/null and b/macros/convmtx2.bin differ diff --git a/macros/convmtx2.sci b/macros/convmtx2.sci new file mode 100644 index 0000000..5f172d3 --- /dev/null +++ b/macros/convmtx2.sci @@ -0,0 +1,17 @@ +// Function Name = 2-D convolution matrix +// Output = Convolution matrix T +// Input = Input matrix srcMat, Dimensions [m, n] + +function[T] = convmtx2(srcImg, m, n) + + srcMat = mattolist(srcImg) + + out = opencv_convmtx2(srcMat, m, n) + + channels = size(out) + + for i = 1:channels + T(:,:,i) = out(i) + end + +endfunction diff --git a/macros/convolver.bin b/macros/convolver.bin new file mode 100644 index 0000000..4d17a0d Binary files /dev/null and b/macros/convolver.bin differ diff --git a/macros/convolver.sci b/macros/convolver.sci new file mode 100644 index 0000000..ac75c19 --- /dev/null +++ b/macros/convolver.sci @@ -0,0 +1,8 @@ +function [output] = convolver(image1,ksize,values1,scalar) + image = mattolist(image1) + a = opencv_convolver(image,ksize,values1,scalar); + d = size(a); + for i = 1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/copymakeborder.bin b/macros/copymakeborder.bin new file mode 100644 index 0000000..5ad2de9 Binary files /dev/null and b/macros/copymakeborder.bin differ diff --git a/macros/copymakeborder.sci b/macros/copymakeborder.sci new file mode 100644 index 0000000..3f1d567 --- /dev/null +++ b/macros/copymakeborder.sci @@ -0,0 +1,20 @@ +function new_image = copymakeborder(image, top, bottom, left, right, borderType, varargin) + + [lhs, rhs] = argn(0) + + image_list = mattolist(image) + + select rhs + case 6 then + out = opencv_copymakeborder(image_list, top, bottom, left, right, borderType) + + case 7 then + out = opencv_copymakeborder(image_list, top, bottom, left, right, borderType, varargin(1)) + end + + sz = size(out) + for i = 1 : sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/corner.bin b/macros/corner.bin new file mode 100644 index 0000000..e4c85b1 Binary files /dev/null and b/macros/corner.bin differ diff --git a/macros/corner.sci b/macros/corner.sci new file mode 100644 index 0000000..b86a75e --- /dev/null +++ b/macros/corner.sci @@ -0,0 +1,26 @@ +// This function detects corner in image I and returns it in the matrix C + +function[C] = corner(I, varargin) + + [lhs, rhs] = argn(0) + + if rhs == 2 then + error(msprintf("No of input cannot be 2")) + end + + srcMat = mattolist(I) + select rhs + case 1 then + out = opencv_corner(srcMat) + + case 3 then + out = opencv_corner(srcMat, varargin(1), varargin(2)) + end + + channels = size(out) + + for i = 1:channels + C(:,:,i) = out(i) + end + +endfunction diff --git a/macros/cornerEigenValsAndVecs.bin b/macros/cornerEigenValsAndVecs.bin new file mode 100644 index 0000000..dce8b0c Binary files /dev/null and b/macros/cornerEigenValsAndVecs.bin differ diff --git a/macros/cornerEigenValsAndVecs.sci b/macros/cornerEigenValsAndVecs.sci new file mode 100644 index 0000000..11d7905 --- /dev/null +++ b/macros/cornerEigenValsAndVecs.sci @@ -0,0 +1,7 @@ +function [outputImg]= cornerEigenValsAndVecs(inputImage, blockSize, kSize, borderType) + inputList=mattolist(inputImage); + outputList=opencv_cornerEigenValsAndVecs(inputList, blockSize, kSize, borderType); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/cornerHarris.bin b/macros/cornerHarris.bin new file mode 100644 index 0000000..2b2760e Binary files /dev/null and b/macros/cornerHarris.bin differ diff --git a/macros/cornerHarris.sci b/macros/cornerHarris.sci new file mode 100644 index 0000000..b2aaa26 --- /dev/null +++ b/macros/cornerHarris.sci @@ -0,0 +1,7 @@ +function [outputImg]= cornerHarris(inputImage, blockSize, kSize, k, borderType) + inputList=mattolist(inputImage); + outputList=opencv_cornerHarris(inputList, blockSize, kSize, k, borderType); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/cornerMinEigenVal.bin b/macros/cornerMinEigenVal.bin new file mode 100644 index 0000000..0f47c74 Binary files /dev/null and b/macros/cornerMinEigenVal.bin differ diff --git a/macros/cornerMinEigenVal.sci b/macros/cornerMinEigenVal.sci new file mode 100644 index 0000000..8d8f82d --- /dev/null +++ b/macros/cornerMinEigenVal.sci @@ -0,0 +1,7 @@ +function [outputImg]= cornerMinEigenVal(inputImage, blockSize, kSize, borderType) + inputList=mattolist(inputImage); + outputList=opencv_cornerMinEigenVal(inputList, blockSize, kSize, borderType); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/crossCorrelator.bin b/macros/crossCorrelator.bin new file mode 100644 index 0000000..33517cf Binary files /dev/null and b/macros/crossCorrelator.bin differ diff --git a/macros/crossCorrelator.sci b/macros/crossCorrelator.sci new file mode 100644 index 0000000..c676044 --- /dev/null +++ b/macros/crossCorrelator.sci @@ -0,0 +1,48 @@ +function relation = crossCorrelator(inputMatrix1,inputMatrix2) +//Calculate 2-D cross correlation of two input matrices +// +//Calling Sequence +// relation=crossCorrelator(inputMatrix1, inputMatrix2); +// +//Parameters +// inputMatrix1: matrix of any size +// inputMatrix2: matrix of any size +// +//Description +//This function computes 2-D cross correlation of two input matrices +// +// Examples +// A=[1 4;2 3]; +// B=[2 3 5;6 1 4]; +// relation=crossCorrelator(A,B); + +[r,c,channels1] = size(inputMatrix1); +[m,n,channels2] = size(inputMatrix2); + if channels1<> 1 then + error(msprintf("wrong argument #1: cross correlator function do not support more than two dimensions")) + elseif type(inputMatrix1)<> 1 + error(msprintf("wrong argument #1: cross correlator function do not support input type %s",typeof(A))) + end + if channels2<> 1 then + error(msprintf("wrong argument #2: cross correlator function do not support more than two dimensions")) + elseif type(inputMatrix2)<> 1 + error(msprintf("wrong argument #2: cross correlator function do not support input type %s",typeof(B))) + end +conjMatrix=conj(inputMatrix2); +Rep = zeros(r + m*2-2, c + n*2-2); +for x = m : m+r-1 + for y = n : n+c-1 + Rep(x,y) = inputMatrix1(x-m+1, y-n+1); + end +end +relation = zeros(r+m-1,n+c-1); +for x = 1 : r+m-1 + for y = 1 : n+c-1 + for i = 1 : m + for j = 1 : n + relation(x, y) = relation(x, y) + (Rep(x+i-1, y+j-1) * conjMatrix(i, j)); + end + end + end +end +endfunction diff --git a/macros/cvHoughLines.bin b/macros/cvHoughLines.bin new file mode 100644 index 0000000..432a49e Binary files /dev/null and b/macros/cvHoughLines.bin differ diff --git a/macros/cvHoughLines.sci b/macros/cvHoughLines.sci new file mode 100644 index 0000000..cdf7d59 --- /dev/null +++ b/macros/cvHoughLines.sci @@ -0,0 +1,29 @@ +function [points] = cvHoughLines(image,theta,rho) +// Cartesian co-ordinates of points defined by rho and theta pairs. +// +// Calling Sequence +// points = cvHoughLines(image, theta, rho) +// +// Parameters +// image : input image matrix +// theta : vector of values of theta that represent the input line +// rho : vector of values of theta that represent the input line +// points : It is an MX4 matrix containing the points of intersection of the line(represented by rho and theta) with the image boundary. +// +// Description +// +// The function returns the points of intersection of the line defined by rho and theta pairs with the image boundary. Image boundary refers to the left and right vertical boundary and top and bottom horizontal boundary. +// +// Examples +// //Load an image +// I = imread('lena.jpeg'); +// // Calculate the points of intersection +// points = cvHoughLines(I,[pi/3 pi/6],[5 9]); +// +// Authors +// Asmita Bhar +// + + img = mattolist(image); + points = opencv_HoughLines(img,theta,rho); +endfunction diff --git a/macros/cvMaximum.bin b/macros/cvMaximum.bin new file mode 100644 index 0000000..463602d Binary files /dev/null and b/macros/cvMaximum.bin differ diff --git a/macros/cvMaximum.sci b/macros/cvMaximum.sci new file mode 100644 index 0000000..a9ba428 --- /dev/null +++ b/macros/cvMaximum.sci @@ -0,0 +1,295 @@ +//*********************************************************************// +// Author : Asmita Bhar, Kevin George +//*********************************************************************// + +function varargout = cvMaximum(image, varargin) +// Finds maximum values in an input +// +// Calling Sequence +// [val ind] = cvMaximum(image); +// val = cvMaximum(image); (only when IndexOutputPort is false) +// ind = cvMaximum(image); (only when ValueOutputPort is false) +// [val ind] = cvMaximum(image, name, value,...); +// val = cvMaximum(image, name, value, ...); (only when IndexOutputPort is false) +// ind = cvMaximum(image, name, value, ...); (only when ValueOutputPort is false) +// [val ind] = cvMaximum(image, name, value,c,r); (only when ROI Processing is true) +// val = cvMaximum(image, name, value,c,r); (only when ROI Processing is true and IndexOutputPort is false) +// ind = cvMaximum(image, name, value,c,r); (only when ROI Processing is true and ValueOutputPort is false) +// +// Parameters +// image : Input image matrix +// ValueOutputPort (Optional) : This property is set to true to output the maximum value. Default : true. +// IndexOutputPort (Optional) : This property is set to true to output the index of the maximum value. Default : true. +// Dimension (Output) : Dimension along which the function operates - Row, Column, All or Custom. Default : All +// CustomDimension (Optional) : The integer dimension over which the function calculates the maximum. This value cannot exceed the number of dimensions in input. It applies only when 'Dimension' property is set to 'Custom'. Default : 1 +// ROIProcessing (Optional) : It applies only when 'Dimension' property is set to 'All'. It calculates the maximum within a particular region of the image. Default : false +// c (Optional): vector of y-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'. +// r (Optional): vector of x-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'. +// val : Stores the maximum value calculated +// ind : Stores the index at which the maximum value occurs. +// +// Description +// The function calculates the maximum value in a given image matrix. +// +// Examples +// //Load an image +// I = imread('peppers.png'); +// //Calculate the maximum (default dimension is 'All') +// [val ind] = cvMaximum(I); +// //Calculate the maximum when dimension is 'Row' and IndexOutputPort is set to false +// val = cvMaximum(I,'ValueOutputPort','false','Dimension','Row'); +// //Calculate the maximum within a given ROI +// a = [0 100 100 0]; +// b = [0 0 100 100]; +// [val ind] = cvMaximum(I,'ROIProcessing','true',a,b); +// +// Authors +// Asmita Bhar +// Kevin George +// + [lhs,rhs] = argn(0); + if rhs<1 then + error(msprintf("Not enough input arguments")); + end + if rhs>13 then + error(msprintf("Too many input arguments")); + end + if lhs>2 then + error(msprintf("Too many output arguments")); + end + [iRows iCols]=size(image(1)) + iChannels = size(image) + + valueOutputPort = 'true'; + indexOutputPort = 'true'; + dimension = 'All'; + customDimension = 1; + roiProcessing = 'false'; + + flag1=0; + i=1; + while(i2) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + elseif(iChannels==3) then + if(customDimension>3) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + end + + if(roiProcessing=='false') then + if (dimension=='All') then + if(iChannels==1) then + [val ind] = max(image(1)) + ind(1) = 1; + elseif (iChannels==3) then + val1 = max(image(1)) + val2 = max(image(2)) + val3 = max(image(3)) + if (val1>=val2) & (val1>=val3) then + [val ind] = max(image(1)) + ind(3) = 1 + elseif (val2>=val1) & (val2>=val3) then + [val ind] = max(image(2)) + ind(3) = 2 + else + [val ind] = max(image(3)) + ind(3) = 3 + end + end + t = ind(1) + ind(1) = ind(2) + ind(2) = t + end + if (dimension=='Row') then + if(iChannels==1) then + [val ind] = max(image(1),'c'); + elseif(iChannels==3) then + [val1 ind1] = max(image(1),'c'); + [val2 ind2] = max(image(2),'c'); + [val3 ind3] = max(image(3),'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + ind(:,:,1) = ind1; + ind(:,:,2) = ind2; + ind(:,:,3) = ind3; + end + end + if (dimension=='Column') then + if(iChannels==1) then + [val ind] = max(image(1),'r'); + elseif(iChannels==3) then + [val1 ind1] = max(image(1),'r'); + [val2 ind2] = max(image(2),'r'); + [val3 ind3] = max(image(3),'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + ind(:,:,1) = ind1; + ind(:,:,2) = ind2; + ind(:,:,3) = ind3; + end + end + if (dimension=='Custom') then + if(iChannels==1) then + if(customDimension==1) then + [val ind] = max(image(1),'r'); + elseif(customDimension==2) then + [val ind] = max(image(1),'c'); + end + elseif(iChannels==3) then + if(customDimension==1) then + [val1 ind1] = max(image(1),'r'); + [val2 ind2] = max(image(2),'r'); + [val3 ind3] = max(image(3),'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + ind(:,:,1) = ind1; + ind(:,:,2) = ind2; + ind(:,:,3) = ind3; + elseif(customDimension==2) then + [val1 ind1] = max(image(1),'c'); + [val2 ind2] = max(image(2),'c'); + [val3 ind3] = max(image(3),'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + ind(:,:,1) = ind1; + ind(:,:,2) = ind2; + ind(:,:,3) = ind3; + elseif(customDimension==3) then + a = image(1); + b = image(2); + c = image(3); + for i=1:iRows + for j=1:iCols + val(i,j)= max([a(i,j) b(i,j) c(i,j)]); + if(val(i,j)==a(i,j)) then + ind(i,j)=1; + elseif(val(i,j)==b(i,j)) then + ind(i,j)=2; + else + ind(i,j)=3; + end + end + + end + end + end + end + end + if(roiProcessing=='true') then + I2 = roipoly(image,c,r); + out = I2; + output = find(out>0); + [rows cols] = size(out); + if(iChannels==1) + ROI = zeros(iRows,iCols); + for i=1:cols + ROI(output(i)) = image(1)(output(i)); + end + elseif(iChannels==3) + ROI1 = zeros(iRows,iCols); + ROI2 = zeros(iRows,iCols); + ROI3 = zeros(iRows,iCols); + for i=1:cols + ROI1(output(i)) = image(1)(output(i)); + ROI2(output(i)) = image(2)(output(i)); + ROI3(output(i)) = image(3)(output(i)); + end + ROI = list(ROI1,ROI2,ROI3); + end + if (dimension=='All') then + if(iChannels==1) then + a=ROI; + [val ind] = max(a(find(a>0))); + //ind(1) = 1; + elseif (iChannels==3) then + a=ROI(1); + b=ROI(2); + c=ROI(3); + val1 = max(a(find(a>0))); + val2 = max(b(find(b>0))); + val3 = max(c(find(c>0))); + if (val1>=val2) & (val1>=val3) then + [val ind] = max(ROI(1)) + //ind(3) = 1 + elseif (val2>=val1) & (val2>=val3) then + [val ind] = max(ROI(2)) + //ind(3) = 2 + else + [val ind] = max(ROI(3)) + //ind(3) = 3 + end + end + //t = ind(1) + //ind(1) = ind(2) + //ind(2) = t + end + end + + + if (~strcmpi(valueOutputPort,'true') & ~strcmpi(indexOutputPort,'true')) then + varargout = list(val,ind); + elseif (~strcmpi(valueOutputPort,'true') & ~strcmpi(indexOutputPort,'false')) then + varargout = val + elseif (~strcmpi(indexOutputPort,'true') & ~strcmpi(valueOutputPort,'false')) then + varargout = ind + end + +endfunction + diff --git a/macros/cvMean.bin b/macros/cvMean.bin new file mode 100644 index 0000000..b2977a0 Binary files /dev/null and b/macros/cvMean.bin differ diff --git a/macros/cvMean.sci b/macros/cvMean.sci new file mode 100644 index 0000000..ce8318e --- /dev/null +++ b/macros/cvMean.sci @@ -0,0 +1,217 @@ +//*********************************************************************// +// Author : Asmita Bhar, Kevin George +//*********************************************************************// + +function [val] = cvMean(image, varargin) +// Finds mean values in an input +// +// Calling Sequence +// val = cvMean(image); +// val = cvMean(image, name, value,...); +// val = cvMean(image, name, value,c,r); (only when ROI Processing is true) +// +// Parameters +// image : Input image matrix +// Dimension (Output) : Dimension along which the function operates - Row, Column, All or Custom. Default : All +// CustomDimension (Optional) : The integer dimension over which the function calculates the mean. This value cannot exceed the number of dimensions in input. It applies only when 'Dimension' property is set to 'Custom'. Default : 1 +// ROIProcessing (Optional) : It applies only when 'Dimension' property is set to 'All'. It calculates the mean within a particular region of the image. Default : false +// c (Optional): vector of y-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'. +// r (Optional): vector of x-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'. +// val : Stores the mean value calculated +// +// Description +// The function calculates the mean value in a given image matrix. +// +// Examples +// //Load an image +// I = imread('peppers.png'); +// //Calculate the mean (default dimension is 'All') +// val = cvMean(I); +// //Calculate the mean when dimension is 'Row' +// val = cvMean(I,'Dimension','Row'); +// //Calculate the mean within a given ROI +// a = [0 100 100 0]; +// b = [0 0 100 100]; +// val = cvMean(I,'ROIProcessing','true',a,b); +// +// Authors +// Asmita Bhar +// Kevin George +// + [lhs,rhs] = argn(0); + if rhs<1 then + error(msprintf("Not enough input arguments")); + end + if rhs>9 then + error(msprintf("Too many input arguments")); + end + [iRows iCols]=size(image(1)) + iChannels = size(image) + + dimension = 'All'; + customDimension = 1; + roiProcessing = 'false'; + + flag1=0; + i=1; + while(i2) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + elseif(iChannels==3) then + if(customDimension>3) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + end + + if(iChannels==1) + I = double(image(1)); + elseif(iChannels==3) + I1 = double(image(1)); + I2 = double(image(2)); + I3 = double(image(3)); + end + + if(roiProcessing=='false') then + if (dimension=='All') then + if(iChannels==1) then + val = mean(I) + elseif (iChannels==3) then + val1 = mean(I1) + val2 = mean(I2) + val3 = mean(I3) + val = mean([val1 val2 val3]) + end + end + if (dimension=='Row') then + if(iChannels==1) then + val = mean(I,'c'); + elseif(iChannels==3) then + val1 = mean(I1,'c'); + val2 = mean(I2,'c'); + val3 = mean(I3,'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + end + end + if (dimension=='Column') then + if(iChannels==1) then + val = mean(I,'r'); + elseif(iChannels==3) then + val1 = mean(I1,'r'); + val2 = mean(I2,'r'); + val3 = mean(I3,'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + end + end + if (dimension=='Custom') then + if(iChannels==1) then + if(customDimension==1) then + val = mean(I,'r'); + elseif(customDimension==2) then + val = mean(I,'c'); + end + elseif(iChannels==3) then + if(customDimension==1) then + val1 = mean(I1,'r'); + val2 = mean(I2,'r'); + val3 = mean(I3,'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + elseif(customDimension==2) then + val1 = mean(I1,'c'); + val2 = mean(I2,'c'); + val3 = mean(I3,'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + elseif(customDimension==3) then + for i=1:iRows + for j=1:iCols + val(i,j)= mean([I1(i,j) I2(i,j) I3(i,j)]); + end + + end + end + end + end + end + if(roiProcessing=='true') then + I4 = roipoly(image,c,r); + out = I4; + output = find(out>0); + [rows cols] = size(out); + if(iChannels==1) + ROI = zeros(iRows,iCols); + for i=1:cols + ROI(output(i)) = image(1)(output(i)); + end + elseif(iChannels==3) + ROI1 = zeros(iRows,iCols); + ROI2 = zeros(iRows,iCols); + ROI3 = zeros(iRows,iCols); + for i=1:cols + ROI1(output(i)) = image(1)(output(i)); + ROI2(output(i)) = image(2)(output(i)); + ROI3(output(i)) = image(3)(output(i)); + end + ROI = list(ROI1,ROI2,ROI3); + end + if (dimension=='All') then + if(iChannels==1) then + a=ROI; + val = mean(a(find(a>0))); + elseif (iChannels==3) then + a=ROI(1); + b=ROI(2); + c=ROI(3); + val1 = mean(a(find(a>0))); + val2 = mean(b(find(b>0))); + val3 = mean(c(find(c>0))); + val = mean([val1 val2 val3]); + end + end + end + +endfunction + diff --git a/macros/cvMedian.bin b/macros/cvMedian.bin new file mode 100644 index 0000000..1424ac4 Binary files /dev/null and b/macros/cvMedian.bin differ diff --git a/macros/cvMedian.sci b/macros/cvMedian.sci new file mode 100644 index 0000000..a403984 --- /dev/null +++ b/macros/cvMedian.sci @@ -0,0 +1,119 @@ +//************************************************** +// Author : Asmita Bhar +//************************************************** + +function [out] = cvMedian(image,varargin) +// Finds median values in an input +// +// Calling Sequence +// val = cvMedian(image) +// val = cvMedian(image, name, value, .....) +// +// Parameters +// image : Input image matrix +// SortMethod (Optional) : The sort method to calculate the median - Quick sort or Insertion Sort. Default : Quick sort +// Dimension (Optional) : Dimension along which the function operates - Row, Column, All or Custom. Default : All +// CustomDimension (Optional) : The integer dimension over which the function calculates the median. This value cannot exceed the number of dimensions in input. It applies only when 'Dimension' property is set to 'Custom'. Default : 1 +// +// Description +// The function calculates the median values in a given input image matrix. +// +// Examples +// //Load an image +// I = imread('peppers.png'); +// val1 = cvMedian(I); // calculates the median value considering dimension as 'All' +// val2 = cvMedian(I,'Dimension','Row'); //calculates the median value in 'Row' dimension +// +// Authors +// Asmita Bhar +// + + [lhs,rhs] = argn(0); + if rhs<1 then + error(msprintf("Not enough input arguments")); + end + if rhs>7 then + error(msprintf("Too many input arguments")); + end + + [iRows iCols]=size(image(1)) + iChannels = size(image) + + sortMethod = 'Quick sort'; + dimension = 'All'; + customDimension = 1; + + flag=0; + i=1; + while(i13 then + error(msprintf("Too many input arguments")); + end + if lhs>2 then + error(msprintf("Too many output arguments")); + end + [iRows iCols]=size(image(1)) // finding the number of rows and columns of the image + iChannels = size(image) //finding the number of channels in the image + + + // default values + valueOutputPort = 'true'; + indexOutputPort = 'true'; + dimension = 'All'; + customDimension = 1; + roiProcessing = 'false'; + + flag1=0; + i=1; + + // taking the optional argumnets as input + while(i2) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + elseif(iChannels==3) then + if(customDimension>3) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + end + + // calculating minimum for the image if ROI is not mentioned + if(roiProcessing=='false') then + //calculating minimum if dimension is all + if (dimension=='All') then + //calculating minimum for a single channel image + if(iChannels==1) then + [val ind] = min(image(1)) + ind(3) = 1; + // calculating minimum for a 3-channel image + elseif (iChannels==3) then + val1 = min(image(1)) + val2 = min(image(2)) + val3 = min(image(3)) + if (val1<=val2) & (val1<=val3) then + [val ind] = min(image(1)) + ind(3) = 1 + elseif (val2<=val1) & (val2<=val3) then + [val ind] = min(image(2)) + ind(3) = 2 + else + [val ind] = min(image(3)) + ind(3) = 3 + end + end + // swapping the indices to match matlab output + t = ind(1) + ind(1) = ind(2) + ind(2) = t + end + // calculating minimum if dimension is Row + if (dimension=='Row') then + //calculating minimum if dimension is all + if(iChannels==1) then + [val ind] = min(image(1),'c'); + // calculating minimum for a 3-channel image + elseif(iChannels==3) then + [val1 ind1] = min(image(1),'c'); + [val2 ind2] = min(image(2),'c'); + [val3 ind3] = min(image(3),'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + ind(:,:,1) = ind1; + ind(:,:,2) = ind2; + ind(:,:,3) = ind3; + end + end + if (dimension=='Column') then + //calculating minimum if dimension is Column + if(iChannels==1) then + [val ind] = min(image(1),'r'); + // calculating minimum for a 3-channel image + elseif(iChannels==3) then + [val1 ind1] = min(image(1),'r'); + [val2 ind2] = min(image(2),'r'); + [val3 ind3] = min(image(3),'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + ind(:,:,1) = ind1; + ind(:,:,2) = ind2; + ind(:,:,3) = ind3; + end + end + if (dimension=='Custom') then + //calculating minimum if dimension is Custom + if(iChannels==1) then + //calculating for dimension 1 i.e. Column + if(customDimension==1) then + [val ind] = min(image(1),'r'); + //calculating for imension 2 i.e. Row + elseif(customDimension==2) then + [val ind] = min(image(1),'c'); + end + // calculating minimum for a 3-channel image + elseif(iChannels==3) then + //calculating for dimension 1 i.e. Column + if(customDimension==1) then + [val1 ind1] = min(image(1),'r'); + [val2 ind2] = min(image(2),'r'); + [val3 ind3] = min(image(3),'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + ind(:,:,1) = ind1; + ind(:,:,2) = ind2; + ind(:,:,3) = ind3; + //calculating for dimension 2 i.e. Row + elseif(customDimension==2) then + [val1 ind1] = min(image(1),'c'); + [val2 ind2] = min(image(2),'c'); + [val3 ind3] = min(image(3),'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + ind(:,:,1) = ind1; + ind(:,:,2) = ind2; + ind(:,:,3) = ind3; + //calculating for dimension 3 i.e Channel + elseif(customDimension==3) then + a = image(1); + b = image(2); + c = image(3); + for i=1:iRows + for j=1:iCols + val(i,j)= min([a(i,j) b(i,j) c(i,j)]); + if(val(i,j)==a(i,j)) then + ind(i,j)=1; + elseif(val(i,j)==b(i,j)) then + ind(i,j)=2; + else + ind(i,j)=3; + end + end + + end + end + end + end + end + //calculating minimum if ROI is mentioned + if(roiProcessing=='true') then + I2 = roipoly(image,c,r); + out = I2; + output = find(out>0); + [rows cols] = size(out); + if(iChannels==1) + ROI = zeros(iRows,iCols); + for i=1:cols + ROI(output(i)) = image(1)(output(i)); + end + elseif(iChannels==3) + ROI1 = zeros(iRows,iCols); + ROI2 = zeros(iRows,iCols); + ROI3 = zeros(iRows,iCols); + for i=1:cols + ROI1(output(i)) = image(1)(output(i)); + ROI2(output(i)) = image(2)(output(i)); + ROI3(output(i)) = image(3)(output(i)); + end + ROI = list(ROI1,ROI2,ROI3); + end + if (dimension=='All') then + if(iChannels==1) then + a=ROI; + [val ind] = min(a(find(a>0))); + elseif (iChannels==3) then + a=ROI(1); + b=ROI(2); + c=ROI(3); + val1 = min(a(find(a>0))); + val2 = min(b(find(b>0))); + val3 = min(c(find(c>0))); + if (val1<=val2) & (val1<=val3) then + [val ind] = min(a(find(a>0))); + elseif (val2<=val1) & (val2<=val3) then + [val ind] = min(b(find(b>0))); + else + [val ind] = min(c(find(c>0))); + end + end + end + end + + // output values according to the user input for valueoutputport and indexoutputport + if (~strcmpi(valueOutputPort,'true') & ~strcmpi(indexOutputPort,'true')) then + varargout = list(val,ind); + elseif (~strcmpi(valueOutputPort,'true') & ~strcmpi(indexOutputPort,'false')) then + varargout = list(val); + elseif (~strcmpi(indexOutputPort,'true') & ~strcmpi(valueOutputPort,'false')) then + varargout = list(ind); + end + +endfunction + diff --git a/macros/cvStandardDeviation.bin b/macros/cvStandardDeviation.bin new file mode 100644 index 0000000..5cdfb93 Binary files /dev/null and b/macros/cvStandardDeviation.bin differ diff --git a/macros/cvStandardDeviation.sci b/macros/cvStandardDeviation.sci new file mode 100644 index 0000000..260553b --- /dev/null +++ b/macros/cvStandardDeviation.sci @@ -0,0 +1,217 @@ +//*********************************************************************// +// Author : Asmita Bhar, Kevin George +//*********************************************************************// + +function [val] = cvStandardDeviation(image, varargin) +// Finds standard deviation values in an input +// +// Calling Sequence +// val = cvStandardDeviation(image); +// val = cvStandardDeviation(image, name, value,...); +// val = cvStandardDeviation(image, name, value,c,r); (only when ROI Processing is true) +// +// Parameters +// image : Input image matrix +// Dimension (Output) : Dimension along which the function operates - Row, Column, All or Custom. Default : All +// CustomDimension (Optional) : The integer dimension over which the function calculates the minimum. This value cannot exceed the number of dimensions in input. It applies only when 'Dimension' property is set to 'Custom'. Default : 1 +// ROIProcessing (Optional) : It applies only when 'Dimension' property is set to 'All'. It calculates the standard deviation within a particular region of the image. Default : false +// c (Optional): vector of y-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'. +// r (Optional): vector of x-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'. +// val : Stores the standard deviation value calculated +// +// Description +// The function calculates the standard deviation value in a given image matrix. +// +// Examples +// //Load an image +// I = imread('peppers.png'); +// //Calculate the standard deviation (default dimension is 'All') +// val = cvStandardDeviation(I); +// //Calculate the standard deviation when dimension is 'Row' +// val = cvStandardDeviation(I,'Dimension','Row'); +// //Calculate the standard deviation within a given ROI +// a = [0 100 100 0]; +// b = [0 0 100 100]; +// val = cvStandardDeviation(I,'ROIProcessing','true',a,b); +// +// Authors +// Asmita Bhar +// Kevin George +// + [lhs,rhs] = argn(0); + if rhs<1 then + error(msprintf("Not enough input arguments")); + end + if rhs>9 then + error(msprintf("Too many input arguments")); + end + [iRows iCols]=size(image(1)) + iChannels = size(image) + + dimension = 'All'; + customDimension = 1; + roiProcessing = 'false'; + + flag1=0; + i=1; + while(i2) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + elseif(iChannels==3) then + if(customDimension>3) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + end + + if(iChannels==1) + I = double(image(1)); + elseif(iChannels==3) + I1 = double(image(1)); + I2 = double(image(2)); + I3 = double(image(3)); + end + + if(roiProcessing=='false') then + if (dimension=='All') then + if(iChannels==1) then + val = stdev(I) + elseif (iChannels==3) then + val1 = stdev(I1) + val2 = stdev(I2) + val3 = stdev(I3) + val = stdev([val1 val2 val3]) + end + end + if (dimension=='Row') then + if(iChannels==1) then + val = stdev(I,'c'); + elseif(iChannels==3) then + val1 = stdev(I1,'c'); + val2 = stdev(I2,'c'); + val3 = stdev(I3,'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + end + end + if (dimension=='Column') then + if(iChannels==1) then + val = stdev(I,'r'); + elseif(iChannels==3) then + val1 = stdev(I1,'r'); + val2 = stdev(I2,'r'); + val3 = stdev(I3,'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + end + end + if (dimension=='Custom') then + if(iChannels==1) then + if(customDimension==1) then + val = stdev(I,'r'); + elseif(customDimension==2) then + val = stdev(I,'c'); + end + elseif(iChannels==3) then + if(customDimension==1) then + val1 = stdev(I1,'r'); + val2 = stdev(I2,'r'); + val3 = stdev(I3,'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + elseif(customDimension==2) then + val1 = stdev(I1,'c'); + val2 = stdev(I2,'c'); + val3 = stdev(I3,'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + elseif(customDimension==3) then + for i=1:iRows + for j=1:iCols + val(i,j)= stdev([I1(i,j) I2(i,j) I3(i,j)]); + end + + end + end + end + end + end + if(roiProcessing=='true') then + I4 = roipoly(image,c,r); + out = I4; + output = find(out>0); + [rows cols] = size(out); + if(iChannels==1) + ROI = zeros(iRows,iCols); + for i=1:cols + ROI(output(i)) = I(output(i)); + end + elseif(iChannels==3) + ROI1 = zeros(iRows,iCols); + ROI2 = zeros(iRows,iCols); + ROI3 = zeros(iRows,iCols); + for i=1:cols + ROI1(output(i)) = I1(output(i)); + ROI2(output(i)) = I2(output(i)); + ROI3(output(i)) = I3(output(i)); + end + ROI = list(ROI1,ROI2,ROI3); + end + if (dimension=='All') then + if(iChannels==1) then + a=ROI; + val = stdev(a(find(a>0))); + elseif (iChannels==3) then + a=ROI(1); + b=ROI(2); + c=ROI(3); + val1 = stdev(a(find(a>0))); + val2 = stdev(b(find(b>0))); + val3 = stdev(c(find(c>0))); + val = stdev([val1 val2 val3]); + end + end + end + +endfunction + diff --git a/macros/cvVariance.bin b/macros/cvVariance.bin new file mode 100644 index 0000000..94c7ae2 Binary files /dev/null and b/macros/cvVariance.bin differ diff --git a/macros/cvVariance.sci b/macros/cvVariance.sci new file mode 100644 index 0000000..bbe18ad --- /dev/null +++ b/macros/cvVariance.sci @@ -0,0 +1,217 @@ +//*********************************************************************// +// Author : Asmita Bhar, Kevin George +//*********************************************************************// + +function [val] = cvVariance(image, varargin) +// Finds variance values in an input +// +// Calling Sequence +// val = cvVariance(image); +// val = cvVariance(image, name, value,...); +// val = cvVariance(image, name, value,c,r); (only when ROI Processing is true) +// +// Parameters +// image : Input image matrix +// Dimension (Output) : Dimension along which the function operates - Row, Column, All or Custom. Default : All +// CustomDimension (Optional) : The integer dimension over which the function calculates the minimum. This value cannot exceed the number of dimensions in input. It applies only when 'Dimension' property is set to 'Custom'. Default : 1 +// ROIProcessing (Optional) : It applies only when 'Dimension' property is set to 'All'. It calculates the variance within a particular region of the image. Default : false +// c (Optional): vector of y-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'. +// r (Optional): vector of x-coordinates of vectices of a rectangle(ROI). Applicable only when 'ROIProcssing' is set to 'true'. +// val : Stores the variance calculated +// +// Description +// The function calculates the variance value in a given image matrix. +// +// Examples +// //Load an image +// I = imread('peppers.png'); +// //Calculate the variance (default dimension is 'All') +// val = cvVariance(I); +// //Calculate the variance when dimension is 'Row' +// val = cvVariance(I,'Dimension','Row'); +// //Calculate the variance within a given ROI +// a = [0 100 100 0]; +// b = [0 0 100 100]; +// val = cvVariance(I,'ROIProcessing','true',a,b); +// +// Authors +// Asmita Bhar +// Kevin George +// + [lhs,rhs] = argn(0); + if rhs<1 then + error(msprintf("Not enough input arguments")); + end + if rhs>9 then + error(msprintf("Too many input arguments")); + end + [iRows iCols]=size(image(1)) + iChannels = size(image) + + dimension = 'All'; + customDimension = 1; + roiProcessing = 'false'; + + flag1=0; + i=1; + while(i2) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + elseif(iChannels==3) then + if(customDimension>3) + error(msprintf("CustomDimension cannot be greater than the dimension of the input.")); + end + end + + if(iChannels==1) + I = double(image(1)); + elseif(iChannels==3) + I1 = double(image(1)); + I2 = double(image(2)); + I3 = double(image(3)); + end + + if(roiProcessing=='false') then + if (dimension=='All') then + if(iChannels==1) then + val = variance(I) + elseif (iChannels==3) then + val1 = variance(I1) + val2 = variance(I2) + val3 = variance(I3) + val = variance([val1 val2 val3]) + end + end + if (dimension=='Row') then + if(iChannels==1) then + val = variance(I,'c'); + elseif(iChannels==3) then + val1 = variance(I1,'c'); + val2 = variance(I2,'c'); + val3 = variance(I3,'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + end + end + if (dimension=='Column') then + if(iChannels==1) then + val = variance(I,'r'); + elseif(iChannels==3) then + val1 = variance(I1,'r'); + val2 = variance(I2,'r'); + val3 = variance(I3,'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + end + end + if (dimension=='Custom') then + if(iChannels==1) then + if(customDimension==1) then + val = variance(I,'r'); + elseif(customDimension==2) then + val = variance(I,'c'); + end + elseif(iChannels==3) then + if(customDimension==1) then + val1 = variance(I1,'r'); + val2 = variance(I2,'r'); + val3 = variance(I3,'r'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + elseif(customDimension==2) then + val1 = variance(I1,'c'); + val2 = variance(I2,'c'); + val3 = variance(I3,'c'); + val(:,:,1) = val1; + val(:,:,2) = val2; + val(:,:,3) = val3; + elseif(customDimension==3) then + for i=1:iRows + for j=1:iCols + val(i,j)= variance([I1(i,j) I2(i,j) I3(i,j)]); + end + + end + end + end + end + end + if(roiProcessing=='true') then + I4 = roipoly(image,c,r); + out = I4; + output = find(out>0); + [rows cols] = size(out); + if(iChannels==1) + ROI = zeros(iRows,iCols); + for i=1:cols + ROI(output(i)) = image(1)(output(i)); + end + elseif(iChannels==3) + ROI1 = zeros(iRows,iCols); + ROI2 = zeros(iRows,iCols); + ROI3 = zeros(iRows,iCols); + for i=1:cols + ROI1(output(i)) = image(1)(output(i)); + ROI2(output(i)) = image(2)(output(i)); + ROI3(output(i)) = image(3)(output(i)); + end + ROI = list(ROI1,ROI2,ROI3); + end + if (dimension=='All') then + if(iChannels==1) then + a=ROI; + val = variance(a(find(a>0))); + elseif (iChannels==3) then + a=ROI(1); + b=ROI(2); + c=ROI(3); + val1 = variance(a(find(a>0))); + val2 = variance(b(find(b>0))); + val3 = variance(c(find(c>0))); + val = variance([val1 val2 val3]); + end + end + end + +endfunction + diff --git a/macros/cv_imread.bin b/macros/cv_imread.bin new file mode 100644 index 0000000..214b649 Binary files /dev/null and b/macros/cv_imread.bin differ diff --git a/macros/cv_imread.sci b/macros/cv_imread.sci new file mode 100644 index 0000000..100cae3 --- /dev/null +++ b/macros/cv_imread.sci @@ -0,0 +1,11 @@ +function[dstMat] = cv_imread(matA) + + out = opencv_imread(matA) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/cv_imread.sci~ b/macros/cv_imread.sci~ new file mode 100644 index 0000000..de70a07 --- /dev/null +++ b/macros/cv_imread.sci~ @@ -0,0 +1,11 @@ +function[dstMat] = imread(matA) + + out = opencv_imread(matA) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/cvtColor.bin b/macros/cvtColor.bin new file mode 100644 index 0000000..379c49c Binary files /dev/null and b/macros/cvtColor.bin differ diff --git a/macros/cvtColor.sci b/macros/cvtColor.sci new file mode 100644 index 0000000..e112154 --- /dev/null +++ b/macros/cvtColor.sci @@ -0,0 +1,20 @@ +function new_image = cvtColor(image, code, varargin) + + [ lhs, rhs ] = argn(0) + + image_list = mattolist(image) + + select rhs + case 2 then + out = opencv_cvtColor(image_list, code) + + case 3 then + out = opencv_cvtColor(image_list, code, varargin(1)) + end + + sz = size(out) + for i=1:sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/dctmtx.bin b/macros/dctmtx.bin new file mode 100644 index 0000000..0f89e5b Binary files /dev/null and b/macros/dctmtx.bin differ diff --git a/macros/dctmtx.sci b/macros/dctmtx.sci new file mode 100644 index 0000000..b17d25d --- /dev/null +++ b/macros/dctmtx.sci @@ -0,0 +1,4 @@ +function [out]=dctmtx(n) + out=opencv_dctmtx(n); + +endfunction; diff --git a/macros/decorrstretch.bin b/macros/decorrstretch.bin new file mode 100644 index 0000000..d72aad5 Binary files /dev/null and b/macros/decorrstretch.bin differ diff --git a/macros/decorrstretch.sci b/macros/decorrstretch.sci new file mode 100644 index 0000000..257f191 --- /dev/null +++ b/macros/decorrstretch.sci @@ -0,0 +1,12 @@ +function new_image = decorrstretch(image) + + image_list = mattolist(image) + + out = opencv_decorrstretch(image_list) + + sz = size(out) + for i=1:sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/demosaic.bin b/macros/demosaic.bin new file mode 100644 index 0000000..9547d64 Binary files /dev/null and b/macros/demosaic.bin differ diff --git a/macros/demosaic.sci b/macros/demosaic.sci new file mode 100644 index 0000000..529511a --- /dev/null +++ b/macros/demosaic.sci @@ -0,0 +1,8 @@ +function [out]=demosaic(src,code,dstCn) + input_image1=mattolist(src); + a=opencv_demosaic( input_image1,code,dstCn); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end +endfunction; diff --git a/macros/detectBRISKFeatures.bin b/macros/detectBRISKFeatures.bin new file mode 100644 index 0000000..dc7de07 Binary files /dev/null and b/macros/detectBRISKFeatures.bin differ diff --git a/macros/detectBRISKFeatures.sci b/macros/detectBRISKFeatures.sci new file mode 100644 index 0000000..4449727 --- /dev/null +++ b/macros/detectBRISKFeatures.sci @@ -0,0 +1,47 @@ +function [varargout] = detectBRISKFeatures(image, varargin) +// This function is used to detect BRISK(Binary Robust Invariant Scalable Keypoints) Features in a grayscale Image. +// +// Calling Sequence +// result = detectBRISKFeatures(Image); +// result = detectBRISKFeatures(Image, Name, Value, ...) +// +// Parameters +// result: BRISKPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features. +// Image : Input image, specified as a A-by-N 2D grayscale. +// MinContrast : (Optional) The minimum difference in intensity between a corner and its surrounding region. (Default: 0.2). The value must be between 0 and 1. +// NumOctaves : (Optional)The number of Octaves that the detector uses. (Default - 3) The value must be an integer scalar in between 1 and 4. +// MinQuality : (Optional) This specifies the minimum quality accepted for corners. (Default - 0.1) The value must be between 0 and 1. +// ROI : (Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates. +// +// Description +// This function returns the BRISK features detected in a 2D grayscale image. +// +// Examples +// image = imread('sample.jpg'); +// results = detectBRISKFeatures(image); +// +// Authors +// Shashank Shekhar + + image_list = mattolist(image); + [ lhs, rhs ] = argn(0) + if rhs > 9 then + error(msprintf("Too many input arguments")) + end + if lhs > 1 then + error(msprintf("Too many output arguments")) + end + select rhs + case 1 then + [a b c d e]= ocv_detectBRISKFeatures(image_list) + case 3 then + [a b c d e]= ocv_detectBRISKFeatures(image_list, varargin(1), varargin(2)) + case 5 then + [a b c d e]= ocv_detectBRISKFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4)) + case 7 then + [a b c d e]= ocv_detectBRISKFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + case 9 then + [a b c d e]= ocv_detectBRISKFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + end + varargout(1) = struct('Keypoints', a, 'Orientation', b, 'Metric', c ,'Scale', d, 'Count', e); +endfunction diff --git a/macros/detectCheckerboardPoints.bin b/macros/detectCheckerboardPoints.bin new file mode 100644 index 0000000..0e17878 Binary files /dev/null and b/macros/detectCheckerboardPoints.bin differ diff --git a/macros/detectCheckerboardPoints.sci b/macros/detectCheckerboardPoints.sci new file mode 100644 index 0000000..b4e50c7 --- /dev/null +++ b/macros/detectCheckerboardPoints.sci @@ -0,0 +1,50 @@ +function [imagePoints,boardSize,varargout]=detectCheckerboardPoints(input1,varargin) +// This function is used to detect a checkerboard pattern in an image. +// +// Calling Sequence +// [imagePoints boardSize] = detectCheckerboardPoints(I); +// [imagePoints boardSize imagesUsed] = detectCheckerboardPoints(imageFileNames); +// [imagePoints boardSize imagesUsed] = detectCheckerboardPoints(images); +// [imagePoints boardSize imagesUsed] = detectCheckerboardPoints(imageFileNames1,imageFileNames2); +// [imagePoints boardSize imagesUsed] = detectCheckerboardPoints(images1,images2); +// +// Parameters +// imagePoints: Matrix containing checkerboard points. +// boardSize: Matrix (1x2) containing boardSize +// imagesUsed: Images used to detect checkerboard in case of multiple input images +// imageFileNames: Path to image files +// images: Input images +// +// Description +// This function detects a checkerboard in an image and returns the points of the checkeboard corners. +// +// Examples +// [imagePoints boardSize imagesUsed] = detectCheckerboardPoints(imageFileNames); +// +// [imagePoints boardSize imagesUsed] = detectCheckerboardPoints(images1,images2); +// +// Authors +// Rohit Suri + + [lhs rhs]=argn(0); + if lhs>3 + error(msprintf(" Too many output arguments")); + elseif rhs>2 + error(msprintf(" Too many input arguments")); + end + select rhs + case 1 then + [points boardSize usedImages]=opencv_detectCheckerboardPoints(input1); + case 2 then + [points boardSize usedImages]=opencv_detectCheckerboardPoints(input1,varargin(1)); + end + for i=1:size(points(1)) + for j=1:size(points(1)(1),'r') + imagePoints(:,:,i)=points(1)(i); + end + end + if lhs==3 then + len=size(usedImages,'c'); + varargout(1)=matrix(usedImages,len,1); + end +endfunction diff --git a/macros/detectFASTFeatures.bin b/macros/detectFASTFeatures.bin new file mode 100644 index 0000000..da32183 Binary files /dev/null and b/macros/detectFASTFeatures.bin differ diff --git a/macros/detectFASTFeatures.sci b/macros/detectFASTFeatures.sci new file mode 100644 index 0000000..e78baba --- /dev/null +++ b/macros/detectFASTFeatures.sci @@ -0,0 +1,53 @@ +function [cornerPoints]=detectFASTFeatures(image,varargin) +// This function is used to detect the corner points using FAST Alogrithm +// +// Calling Sequence +// [ Location Count Metric ] = detectFASTFeatures( Image, Name, Value... ) +// +// Parameters +// Image: Input Image, should be a 2-D grayscale. The Input Image should be real +// MinQuality [Optional Input Argument]: Minimum Accepted Quality of Corners, can be specified as a scalar value between [0,1]. Default: 0.1 +// MinContrast [Optional Input Argument]: Minimum Intensity difference for Corners to be detected, can be specified as a scalar value between[0,1]. Default: 0.2 +// ROI [Optional Input Argument]: Specify a rectangular region of operation. Format [ x y width height ]. Default: [1 1 size(Image,2) size(Image,1)] +// Location: Set of x,y coordinates for the deteccted points +// Count: Number of corner points detected +// Metric: Value describing the strength of each detected Point +// +// Description +// The detectFASTFeatures function uses the Features from Accelerated Segment Test (FAST) algorithm to find feature points. +// +// Examples +// image = imread('sample.jpg'); +// [location count metric] = detectFastFeatures(image); +// +// With Optional Arguments: +// [location count metric] = detectFASTFeatures(image,"MinContrast",0.2); +// +// Authors +// Umang Agrawal +// Sridhar Reddy + + [lhs rhs]=argn(0); + if lhs>3 + error(msprintf(" Too many output arguments")); + elseif rhs-1>6 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs-1,2)<>0 + error(msprintf("Either Argument Name or its Value missing")); + end + imageList=mattolist(image); + select rhs-1 + case 0 then + [location count metric]=opencv_detectFASTFeatures(imageList); + case 2 then + [location count metric]=opencv_detectFASTFeatures(imageList,varargin(1),varargin(2)); + case 4 then + [location count metric]=opencv_detectFASTFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [location count metric]=opencv_detectFASTFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + end + cornerPoints=struct('Type','cornerPoints','Location',location,'Metric',metric,'Count',count); + //for i=1:count + // cornerPoints(i)=struct('Location',location(i,:),'metric',metric(i,:),'Count',1); + //end +endfunction diff --git a/macros/detectHarrisFeatures.bin b/macros/detectHarrisFeatures.bin new file mode 100644 index 0000000..60db293 Binary files /dev/null and b/macros/detectHarrisFeatures.bin differ diff --git a/macros/detectHarrisFeatures.sci b/macros/detectHarrisFeatures.sci new file mode 100644 index 0000000..f4792f7 --- /dev/null +++ b/macros/detectHarrisFeatures.sci @@ -0,0 +1,52 @@ +function [cornerPoints]=detectHarrisFeatures(image,varargin) +// This function is used to find corner points in an image using Harris algorithm. +// +// Calling Sequence +// points = detectHarrisFeatures(I); +// points = detectHarrisFeatures(I, Name, Value, ...); +// +// Parameters +// points: Structure of corner points +// I: Input image to detectHarrisFeatures() +// MinQuality: (Optional) Minimum accepted quality of corners (Default- 0.01) +// FilterSize: (Optional) Dimension of Gaussian Filter (Default: 5) +// ROI: (Optional) Rectangular region for corner detection +// SensitivityFactor: (Optional) SensitivityFactor of Harris algorithm (Default- 0.04) +// +// Description +// This function detects corners in an image I. These corner points are used to extract features and hence recognize the contents of an image. +// +// Examples +// I = imread('sample.jpg'); +// points = detectHarrisFeatures(I); +// +// Authors +// Rohit Suri +// Sridhar Reddy + + [lhs rhs]=argn(0); + if lhs>1 + error(msprintf(" Too many output arguments")); + elseif rhs>9 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs,2)==0 + error(msprintf("Either Argument Name or its Value missing")); + end + imageList=mattolist(image); + select rhs-1 + case 0 then + [location metric count]=opencv_detectHarrisFeatures(imageList); + case 2 then + [location metric count]=opencv_detectHarrisFeatures(imageList,varargin(1),varargin(2)); + case 4 then + [location metric count]=opencv_detectHarrisFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [location metric count]=opencv_detectHarrisFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + case 8 then + [location metric count]=opencv_detectHarrisFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8)); + end + cornerPoints=struct('Type','cornerPoints','Location',location,'Metric',metric,'Count',count); + //for i=1:count(1,1) + // cornerPoints(i)=struct('Location',location(i,:),'Metric',metric(i,:),'Count',1); + //end +endfunction diff --git a/macros/detectMSERFeatures.bin b/macros/detectMSERFeatures.bin new file mode 100644 index 0000000..ec3010b Binary files /dev/null and b/macros/detectMSERFeatures.bin differ diff --git a/macros/detectMSERFeatures.sci b/macros/detectMSERFeatures.sci new file mode 100644 index 0000000..9e1377a --- /dev/null +++ b/macros/detectMSERFeatures.sci @@ -0,0 +1,57 @@ +function [regions]=detectMSERFeatures(image,varargin) +// This function is used to detect MSER features +// +// Calling Sequence +// list_pixels = detectMSERFeatures(image, Name, Value... ) +// [list_pixels Count Location Axes Orientation] = detectMSERFeatures(image, Name, Value... ) +// +// Parameters +// Image: Input Image, should be a 2-D grayscale. The Input Image should be real +// ThresholdDelta [Optional Input Argument]: Step Size between intensity to be checked for measurement of stability. Values: Percentage numeric value. Default: 2.0 +// RegionAreaRange [Optional Input Argument]: Size of the region in Pixels specified as [minArea maxArea]. Default Value: [30 14000] +// ROI [Optional Input Argument]: Specify a rectangular region of operation. Format [ x y width height ]. Default: [1 1 size(Image,2) size(Image,1)] +// MaxAreaVariation [Optional Input Argument]: Maximum Area Variation between external regions at varying thresholds for determination of stability. Values in range of (0,1]. Default 0.25 +// list_pixels: Array of point coordinates for detected regions +// Location: A M-by-2 Matrix of [x y] coordinates of centroid of the ellipsoidal fit to the region +// Count: Count of the number of stable regions detected +// Axes: A M-by-2 Matrix of [majorAxis minorAxis] values for each ellipsoidal fit to the detected regions +// Orientation: A M-by-1 Vector containing the angle of orientation of the ellipsoidal fit in the range of [-pi/2 +pi/2] radians +// +// Description +// This function is used to detect the MSER regions, and also the corresponding ellipsoidal fit to the detected regions. +// +// Examples +// image = imread('sample.jpg'); +// [regions] = detectMSERFeatures(image); +// +// With Optional Arguments: +// [regions location axes] = detectMSERFeatures(image, "ThresholdDelta", 10.0) +// +// Authors +// Umang Agrawal +// Sridhar Reddy + + [lhs rhs]=argn(0); + if rhs>9 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs-1,2)<>0 + error(msprintf("Either Argument Name or its Value missing")); + end + imageList=mattolist(image); + select rhs-1 + case 0 then + [pixelIdxList count location axes orientation]=opencv_detectMSERFeatures(imageList); + case 2 then + [pixelIdxList count location axes orientation]=opencv_detectMSERFeatures(imageList,varargin(1),varargin(2)); + case 4 then + [pixelIdxList count location axes orientation]=opencv_detectMSERFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [pixelIdxList count location axes orientation]=opencv_detectMSERFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + case 8 then + [pixelIdxList count location axes orientation]=opencv_detectMSERFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8)); + end + //for i=1:size(pixelIdxList) + // regions(i)=struct('Count',1,'Location',location(i,:),'Axes',axes(i,:),'Orientation',orientation(i,:),'PixelIdxList',pixelIdxList(i)); + //end + regions=struct('Type','MSERObject','Count',count,'Location',location,'Axes',axes,'Orientation',orientation,'PixelIdxList',pixelIdxList), +endfunction diff --git a/macros/detectMinEigenFeatures.bin b/macros/detectMinEigenFeatures.bin new file mode 100644 index 0000000..80a65bb Binary files /dev/null and b/macros/detectMinEigenFeatures.bin differ diff --git a/macros/detectMinEigenFeatures.sci b/macros/detectMinEigenFeatures.sci new file mode 100644 index 0000000..e6b4485 --- /dev/null +++ b/macros/detectMinEigenFeatures.sci @@ -0,0 +1,50 @@ +function [cornerPoints]=detectMinEigenFeatures(image,varargin) +// This function is used to find corner points in an image using Minimum Eigen Value algorithm. +// +// Calling Sequence +// points = detectMinEigenFeatures(I); +// points = detectMinEigenFeatures(I, Name, Value, ...); +// +// Parameters +// points: Structure of corner points +// I: Input image to detectHarrisFeatures() +// MinQuality: (Optional) Minimum accepted quality of corners (Default- 0.01) +// FilterSize: (Optional) Dimension of Gaussian Filter (Default: 5) +// ROI: (Optional) Rectangular region for corner detection +// +// Description +// This function detects corners in an image I. These corner points are used to extract features and hence recognize the contents of an image. +// +// Examples +// I = imread('sample.jpg'); +// points = detectMinEigenFeatures(I); +// +// Authors +// Rohit Suri +// Sridhar Reddy + + [lhs rhs]=argn(0); + if lhs>1 + error(msprintf(" Too many output arguments")); + elseif rhs>7 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs,2)==0 + error(msprintf("Either Argument Name or its Value missing")); + end + imageList=mattolist(image); + select rhs-1 + case 0 then + [location metric count]=opencv_detectMinEigenFeaturess(imageList); + case 2 then + [location metric count]=opencv_detectMinEigenFeatures(imageList,varargin(1),varargin(2)); + case 4 then + [location metric count]=opencv_detectMinEigenFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [location metric count]=opencv_detectMinEigenFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + end + //disp(count(1,1)); + cornerPoints=struct('Type','cornerPoints','Location',location,'Metric',metric,'Count',count); + //for i=1:count(1,1) + // cornerPoints(i)=struct('Type','cornerPoints','Location',location(i,:),'Metric',metric(i,:),'Count',1); + //end +endfunction diff --git a/macros/detectSURFFeatures.bin b/macros/detectSURFFeatures.bin new file mode 100644 index 0000000..ebefc15 Binary files /dev/null and b/macros/detectSURFFeatures.bin differ diff --git a/macros/detectSURFFeatures.sci b/macros/detectSURFFeatures.sci new file mode 100644 index 0000000..1ee7395 --- /dev/null +++ b/macros/detectSURFFeatures.sci @@ -0,0 +1,45 @@ +function [varargout] = detectSURFFeatures(image, varargin) +// This function is used to detect SURF(Speeded Up Robust Features) Features in a grayscale Image. +// +// Calling Sequence +// result = detectSURFFeatures(Image); +// result = detectSURFFeatures(Image, Name, Value, ...) +// +// Parameters +// result: SURFPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features. +// Image : Input image, specified as a A-by-N 2D grayscale. +// MetricThreshold : (Optional) With default value equal to 1000, it is to be specified as a scalar. Every interest point detected has a strength associated with it. In case, only the stronget ones are needed, this parameter has to be given a larger value. To get more no of interest points/blobs, it is to be reduced. +// NumOctaves : (Optional)With default value equal to 3, it is to be specified as a scalar. Larger the number of octaves, larger is the size of blobs detected. This is because higher octave use large sized filters. Value must be an integer scalar in between 1 and 4. +// NumScaleLevels : (Optional)With default value equal to 4, it is to be specified as a scalar. It denotes the number of scale level for each octave. The Value must be an integer scalar greater than or equal to 3. +// ROI : (Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates. +// Description +// This function return the SURF(Speeded Up Robust Features) Interest Points for a 2D Grayscale image. It is scale- and rotation- invariant point detector and descriptor and its application include Camera Calibration, 3D Reconstruction, Object Recognition to name a few. +// +// Examples +// image = imread('sample.jpg'); +// results = detectSURFFeatures(image); +// +// Authors +// Shashank Shekhar + image_list = mattolist(image); + [ lhs, rhs ] = argn(0) + if rhs > 9 then + error(msprintf("Too many input arguments")) + end + if lhs > 1 then + error(msprintf("Not enough input arguments")) + end + select rhs + case 1 then + [a b c d e f] = ocv_detectSURFFeatures(image_list) + case 3 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2)) + case 5 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4)) + case 7 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + case 9 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + end + varargout(1) = struct('KeyPoints', a, 'Orientation', b, 'Metric', c ,'SignOfLaplacian', d,'Scale', e, 'Count', f ); +endfunction \ No newline at end of file diff --git a/macros/dilate.bin b/macros/dilate.bin new file mode 100644 index 0000000..a3f0780 Binary files /dev/null and b/macros/dilate.bin differ diff --git a/macros/dilate.sci b/macros/dilate.sci new file mode 100644 index 0000000..0c21511 --- /dev/null +++ b/macros/dilate.sci @@ -0,0 +1,8 @@ +function [out]=dilate(input_image,actualkernel,anchor_x,anchor_y,iteration) + input_image1=mattolist(input_image); + a=opencv_dilate(input_image1,actualkernel,anchor_x,anchor_y,iteration); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end +endfunction; diff --git a/macros/displayErrors.bin b/macros/displayErrors.bin new file mode 100644 index 0000000..944fc58 Binary files /dev/null and b/macros/displayErrors.bin differ diff --git a/macros/displayErrors.sci b/macros/displayErrors.sci new file mode 100644 index 0000000..ae16b53 --- /dev/null +++ b/macros/displayErrors.sci @@ -0,0 +1,249 @@ +function displayErrors(varargin) + [lhs rhs]=argn(0); + if rhs<2 then + error(msprintf(" Not enough input arguments")) + elseif rhs>2 then + error(msprintf(" Too many input arguments. Two arguments expected.")) + end + typeOferrors =0; + errors = varargin(1); + if ~isstruct(errors) then + error(msprintf("Invalid Input Argument. A CameraCalibrationError Struct or StereoCalibrationError Struct expected. ")) + end + errorNames = fieldnames(errors); + [size1 zz] = size(errorNames); + + if size1 == 2 then + typeOferrors = 1; + elseif size1 == 5 then + typeOferrors = 2; + else + error(msprintf("Invalid Input Argument. A CameraCalibrationError Struct or StereoCalibrationError Struct expected. ")) + end + + if typeOferrors == 1 then + cameraParam = varargin(2); + checkCCE(errors); + if ~isstruct(cameraParam) then + error(msprintf(" A CameraParameters Struct expected")) + end + jj = fieldnames(cameraParam) + [size3 zz] = size(jj); + if size3~=18 then + error(msprintf("An CameraParameters struct expected.")) + end + checkCameraParam(cameraParam); + focalErr = string(cameraParam.FocalLength) + " +/- "; + fE = string(errors.IntrinsicsErrors.FocalLengthError) ; + prinErr = string(cameraParam.PrincipalPoint) + " +/- "; + pE = string(errors.IntrinsicsErrors.PrincipalPointError); + radErr = string(cameraParam.RadialDistortion) + " +/- "; + rdE = string(errors.IntrinsicsErrors.RadDistortionError); + for i=1:2 + focalErr(i) = focalErr(i) + fE(i); + prinErr(i) = prinErr(i) + pE(i); + end + [size1 xx] = size(radErr); + size1 =size1*xx; + for i=1:size1 + radErr(i) = radErr(i) + rdE(i); + end + + rotVecErr = string(cameraParam.RotationVectors) + " +/- "; + rvE = string(errors.ExtrinsicsErrors.RotVectorsError); + [size21 size22] = size(rotVecErr); + printf("%d %d \n", size21,size22); + for i=1:size21 + for j=1:size22 + rotVecErr(i,j) = rotVecErr(i,j) + rvE(i,j); + end + end + + tranVecErr = string(cameraParam.TranslationVectors) + " +/- "; + tvE = string(errors.ExtrinsicsErrors.TransVectorsError); + [size31 size32] = size(tranVecErr); + printf("%d %d \n", size31,size32); + for i=1:size31 + for j=1:size32 + tranVecErr(i,j) = tranVecErr(i,j) + tvE(i,j); + end + end + + + printf("\n\t\t\tStandard Errors of Estimated Camera Parameters\n"); + printf("\t\t\t----------------------------------------------\n\n"); + printf(" Intrinsics\n"); + printf(" ----------\n"); + printf(" Focal length (pixels): [\t"); + printf("%s\t\t%s\t]\n", focalErr(1), focalErr(2)); + printf(" Principal point (pixels): [\t"); + printf("%s\t\t%s\t]\n", prinErr(1), prinErr(2)); + printf(" Radial distortion: [\t"); + for i=1:size1 + printf("%s\t\t", radErr(i)); + end + printf("\t]\n\n"); + printf(" Extrinsics\n"); + printf(" ----------\n"); + printf(" Rotation vectors:\n"); + for i=1:size21 + printf(" [\t"); + for j=1:size22 + printf("%s\t\t", rotVecErr(i,j)); + end + printf("\t]\n"); + end + if size21*size22==0 then + printf("\n"); + end + printf(" Translation vectors (mm):\n"); + for i=1:size21 + printf(" [\t"); + for j=1:size22 + printf("%s\t\t", tranVecErr(i,j)); + end + printf("\t]\n"); + + end + printf("\n"); + + else + stereoParam = varargin(2); + checkSCE(errors); + if ~isstruct(stereoParam) then + error(msprintf(" A Stereo Parameter Struct expected")) + end + jj = fieldnames(stereoParam) + [size4 zz] = size(jj); + if size4~=10 then + error(msprintf("A Stereo Parameters Struct Expected")) + end + checkStereoParam(stereoParam); + focalErr1 = string(stereoParam.CameraParameters1.FocalLength) + " +/- "; + fE1 = string(errors.Camera1IntrinsicsErrors.FocalLengthError) ; + prinErr1 = string(stereoParam.CameraParameters1.PrincipalPoint) + " +/- "; + pE1 = string(errors.Camera1IntrinsicsErrors.PrincipalPointError); + radErr1 = string(stereoParam.CameraParameters1.RadialDistortion) + " +/- "; + rdE1 = string(errors.Camera1IntrinsicsErrors.RadDistortionError); + for i=1:2 + focalErr1(i) = focalErr1(i) + fE1(i); + prinErr1(i) = prinErr1(i) + pE1(i); + end + [size11 xx] = size(radErr1); + size11 =size11*xx; + for i=1:size11 + radErr1(i) = radErr1(i) + rdE1(i); + end + + focalErr2 = string(stereoParam.CameraParameters2.FocalLength) + " +/- "; + fE2 = string(errors.Camera2IntrinsicsErrors.FocalLengthError) ; + prinErr2 = string(stereoParam.CameraParameters2.PrincipalPoint) + " +/- "; + pE2 = string(errors.Camera2IntrinsicsErrors.PrincipalPointError); + radErr2 = string(stereoParam.CameraParameters2.RadialDistortion) + " +/- "; + rdE2 = string(errors.Camera2IntrinsicsErrors.RadDistortionError); + for i=1:2 + focalErr2(i) = focalErr2(i) + fE2(i); + prinErr2(i) = prinErr2(i) + pE2(i); + end + [size12 xx] = size(radErr2); + size12 =size12*xx; + for i=1:size12 + radErr2(i) = radErr2(i) + rdE2(i); + end + + rotVecErr = string(stereoParam.CameraParameters1.RotationVectors) + " +/- "; + rvE = string(errors.Camera1ExtrinsicsErrors.RotVectorsError); + [size21 size22] = size(rotVecErr); + printf("%d %d \n", size21,size22); + for i=1:size21 + for j=1:size22 + rotVecErr(i,j) = rotVecErr(i,j) + rvE(i,j); + end + end + + tranVecErr = string(stereoParam.CameraParameters1.TranslationVectors) + " +/- "; + tvE = string(errors.Camera1ExtrinsicsErrors.TransVectorsError); + [size31 size32] = size(tranVecErr); + printf("%d %d \n", size31,size32); + for i=1:size31 + for j=1:size32 + tranVecErr(i,j) = tranVecErr(i,j) + tvE(i,j); + end + end + + rotOfCam = stereoParam.RotationOfCamera2; + rotOfCamErr = string([rotOfCam(1,1) rotOfCam(2,1) rotOfCam(3,1)]) + " +/- "; + rotE = string(errors.RotationOfCamera2Error); + for i=1:3 + rotOfCamErr(i) = rotOfCamErr(i) + rotE(i); + end + + transOfCamErr = string(stereoParam.TranslationOfCamera2) + " +/- "; + toCE = string(errors.TransOfCamera2Error) ; + for i=1:3 + transOfCamErr(i) = transOfCamErr(i) + toCE(i); + end + + + + printf("\n\t\t\tStandard Errors of Estimated Stereo Camera Parameters\n"); + printf("\t\t\t-----------------------------------------------------\n\n"); + printf(" Camera 1 Intrinsics\n"); + printf(" -------------------\n"); + printf(" Focal length (pixels): [\t"); + printf("%s\t\t%s\t]\n", focalErr1(1), focalErr1(2)); + printf(" Principal point (pixels): [\t"); + printf("%s\t\t%s\t]\n", prinErr1(1), prinErr1(2)); + printf(" Radial distortion: [\t"); + for i=1:size11 + printf("%s\t\t", radErr1(i)); + end + printf("\t]\n\n"); + printf(" Camera 1 Extrinsics\n"); + printf(" -------------------\n"); + printf(" Rotation vectors:\n"); + for i=1:size21 + printf(" [\t"); + for j=1:size22 + printf("%s\t\t", rotVecErr(i,j)); + end + printf("\t]\n"); + end + if size21*size22==0 then + printf("\t]\n"); + end + printf(" Translation vectors (mm):\n"); + for i=1:size21 + printf(" [\t"); + for j=1:size22 + printf("%s\t\t", tranVecErr(i,j)); + end + printf("\t]\n"); + end + printf("\n"); + printf(" Camera 2 Intrinsics\n"); + printf(" -------------------\n"); + printf(" Focal length (pixels): [\t"); + printf("%s\t\t%s\t]\n", focalErr2(1), focalErr2(2)); + printf(" Principal point (pixels): [\t"); + printf("%s\t\t%s\t]\n", prinErr2(1), prinErr2(2)); + printf(" Radial distortion: [\t"); + for i=1:size12 + printf("%s\t\t", radErr2(i)); + end + printf("\t]\n\n"); + printf(" Position And Orientation of Camera 2 Relative to Camera 1\n"); + printf(" ---------------------------------------------------------\n"); + printf(" Rotation of camera 2: [\t"); + for i=1:3 + printf("%s\t\t", rotOfCamErr(i)); + end + printf("\t]\n"); + printf(" Translation of camera 2 (mm): [\t"); + for i=1:3 + printf("%s\t\t", transOfCamErr(i)); + end + printf("\t]\n"); + end + +endfunction diff --git a/macros/ellipse.sce b/macros/ellipse.sce new file mode 100644 index 0000000..f6fc170 --- /dev/null +++ b/macros/ellipse.sce @@ -0,0 +1,9 @@ +function [out]=ellipse(input_image , x_coordinate_of_centre , y_coordinate_centre , first_axis, second_axis, angle, startAngle, endAngle, r_value,g_value,b_value,thickness,linetype,shift) +input_image1=mattolist(input_image); + a=opencv_ellipse(input_image1 , x_coordinate_of_centre , y_coordinate_centre , first_axis, second_axis, angle, startAngle, endAngle, r_value,g_value,b_value,thickness,linetype,shift); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/ellipse2poly.bin b/macros/ellipse2poly.bin new file mode 100644 index 0000000..77e4307 Binary files /dev/null and b/macros/ellipse2poly.bin differ diff --git a/macros/ellipse2poly.sci b/macros/ellipse2poly.sci new file mode 100644 index 0000000..da7b157 --- /dev/null +++ b/macros/ellipse2poly.sci @@ -0,0 +1,3 @@ +function [out] = ellipse2poly(centre_x,centre_y,width,height,angle,arcstart,arcend,delta) + out = opencv_ellipse2poly(centre_x,centre_y,width,height,angle,arcstart,arcend,delta); +endfunction diff --git a/macros/erode.bin b/macros/erode.bin new file mode 100644 index 0000000..d2dd61b Binary files /dev/null and b/macros/erode.bin differ diff --git a/macros/erode.sci b/macros/erode.sci new file mode 100644 index 0000000..336e220 --- /dev/null +++ b/macros/erode.sci @@ -0,0 +1,9 @@ +function [out]=erode(input_image ,kernel,actualkernel,anchorX,anchorY) +input_image1=mattolist(input_image); + a=opencv_erode(input_image1 ,kernel,actualkernel,anchorX,anchorY); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/estimateFundamentalMatrix.bin b/macros/estimateFundamentalMatrix.bin new file mode 100644 index 0000000..6c6b83e Binary files /dev/null and b/macros/estimateFundamentalMatrix.bin differ diff --git a/macros/estimateFundamentalMatrix.sci b/macros/estimateFundamentalMatrix.sci new file mode 100644 index 0000000..433db08 --- /dev/null +++ b/macros/estimateFundamentalMatrix.sci @@ -0,0 +1,41 @@ +function [fundamentalMatrix]=estimateFundamentalMatrix(matchedPoints1,matchedPoints2,varargin) +// It returns the 3-by-3 fundamental matrix corresponding to the input points +// +// Calling Sequence +// fundamentalMat=estimateFundamentalMatrix(matchedPoints1,matchedPoints2); +// fundamentalMat=estimateFundamentalMatrix(matchedPoints1,matchedPoints2,'method','RANSAC','param1',2,'param2',0.99); +// +// Parameters +// matchedPoints1: M-by-2 array of first image points. These points can be SURFPoints, MSERRegions, cornerPoints. +// matchedPoints2: M-by-2 array of the second image points of the same size and format as matchedPoints1. +// method: method for computing fundamental matrix. Possible methods are 7POINT,8POINT,RANSAC,LMEDS and RANSAC is the default one. +// param1: It indiacates maximum distance from a point to an epipolar line in pixels +// param2: It specifies a desirable level of confidence +// +// Description +// estimateFundamentalMatrix function calculates a fundamental matrix from the corresponding points in two images. +// +// Examples +// I=imread("lena_left.jpeg"); +// cornerPoints1=detectMinEigenFeatures(I); +// I2=imread("lena_right.jpeg"); +// cornerPoints2=detectMinEigenFeatures(I2); +// fundamentalMat=(cornerPoints1.Location,cornerPoints2.Location); + + [lhs rhs]=argn(0); + if rhs>10 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs,2)==0 + error(msprintf("Either Argument Name or its Value missing")); + end + select rhs + case 2 then + [fundamentalMatrix]=opencv_estimateFundamentalMatrix(matchedPoints1,matchedPoints2); + case 4 then + [fundamentalMatrix]=opencv_estimateFundamentalMatrix(matchedPoints1,matchedPoints2,varargin(1),varargin(2)); + case 6 then + [fundamentalMatrix]=opencv_estimateFundametanlMatrix(matchedPoints1,matchedPoints2,varargin(1),varargin(2),varargin(3),varargin(4)); + case 8 then + [fundamentalMatrix]=opencv_estimateFundamentalMatrix(matchedPoints1,matchedPoints2,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + end +endfunction diff --git a/macros/estimateGeometricTransform.bin b/macros/estimateGeometricTransform.bin new file mode 100644 index 0000000..c501a9a Binary files /dev/null and b/macros/estimateGeometricTransform.bin differ diff --git a/macros/estimateGeometricTransform.sci b/macros/estimateGeometricTransform.sci new file mode 100644 index 0000000..f0ef6fa --- /dev/null +++ b/macros/estimateGeometricTransform.sci @@ -0,0 +1,5 @@ +function [output] = estimateGeometricTransform(img1,img2) + I1 = mattolist(img1); + I2 = mattolist(img2); + output = opencv_estimateGeometricTransform(I1,I2) +endfunction diff --git a/macros/estimateUncalibratedRectification.bin b/macros/estimateUncalibratedRectification.bin new file mode 100644 index 0000000..38765cf Binary files /dev/null and b/macros/estimateUncalibratedRectification.bin differ diff --git a/macros/estimateUncalibratedRectification.sci b/macros/estimateUncalibratedRectification.sci new file mode 100644 index 0000000..50867e9 --- /dev/null +++ b/macros/estimateUncalibratedRectification.sci @@ -0,0 +1,35 @@ +function [T1,T2]=estimateUncalibratedRectification(fundamentalMatrix,inlierPoints1,inlierPoints2,imageSize) +// It returns projective transformation for rectifying stereo images +// +// Calling Sequence +// [T1,T2]=estimateUncalibratedRectification((fundamentalMatrix,inlierPoints1,inlierPoints2,imageSize); +// +// Parameters +// fundamentalMatrix: 3-by-3 fundamental matrix. +// inlierPoints1: M-by-2 matrix +// inlierPoints2: M-by-2 matrix +// imageSize: 2 element vector, indicating size of the second image. +// +// Description +// estimateUncalibratedRectification gives transformation of both images as 3-by-3 matrices +// +// Examples +// I=imread("lena_left.jpeg"); +// cornerPoints1=detectMinEigenFeatures(I); +// I2=imread("lena_right.jpeg"); +// cornerPoints2=detectMinEigenFeatures(I2); +// fundamentalMat=(cornerPoints1.Location,cornerPoints2.Location); +// [T1, T2]=estimateUncalibratedRectification(fundamentalMat,cornerPoints1.Location,cornerPoints2.Location,size(I2)); + + [lhs rhs]=argn(0); + if lhs>2 + error(msprintf(" Too many output arguments")); + elseif rhs<2 + error(msprintf(" More input arguments required")); + elseif rhs>4 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs,2)==0 + error(msprintf("Either Argument Name or its Value missing")); + end + [T1 T2]=opencv_estimateUncalibratedRectification(fundamentalMatrix,inlierPoints1,inlierPoints2,imageSize); +endfunction diff --git a/macros/evaluate.bin b/macros/evaluate.bin new file mode 100644 index 0000000..64831d7 Binary files /dev/null and b/macros/evaluate.bin differ diff --git a/macros/evaluate.sci b/macros/evaluate.sci new file mode 100644 index 0000000..9ab7d28 --- /dev/null +++ b/macros/evaluate.sci @@ -0,0 +1,31 @@ +function confMat = evaluate(classifier, imageSet) +// This function is used to evaluate an image classifier. +// +// Calling Sequence +// confMat = evaluate(classifier, imgSet) +// +// Parameters +// confMat: Confusion matrix as a result of the evaluation +// classifier: Image category classifer +// imgSet: An imageSet struct to be evaluated +// +// Description +// This function evaluates an image category classifier on an input image set. +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// categoryClassifier = trainImageCategoryClassifier(trainingSet, bag); +// confMatrix = evaluate(categoryClassifer, testSet); +// +// Authors +// Rohit Suri +// Umang Agrawal + + classifier_list = classifierToList(classifier) + imageSet_list = imageSetToList(imageSet) + + confMat = opencv_evaluate(classifier_list, imageSet_list) + +endfunction diff --git a/macros/evaluateImageRetrieval.bin b/macros/evaluateImageRetrieval.bin new file mode 100644 index 0000000..6931451 Binary files /dev/null and b/macros/evaluateImageRetrieval.bin differ diff --git a/macros/evaluateImageRetrieval.sci b/macros/evaluateImageRetrieval.sci new file mode 100644 index 0000000..a8ac99d --- /dev/null +++ b/macros/evaluateImageRetrieval.sci @@ -0,0 +1,104 @@ +function [varargout] = evaluateImageRetrieval(image, IndexImage, ExpectedID, varargin) +// This function is used to evaluate the Image Search Results +// +// Calling Sequence +// averagePrecision = evaluateImageRetrieval(queryImage, ImageIndex, expectedIDs, Name, Value... ); +// [averagePrecision imageID Scores] = evaluateImageRetrieval(queryImage, ImageIndex, expectedIDs); +// +// Parameters +// queryImage: The query image, for which the similar image has to be found. Can be a grayscale or a RGB image +// ImageIndex: imageIndex object that contains the data set of all the images to be compared +// expectedIDs: A row or column vector containing the IDs of expected Similarity +// NumResults [Optional Input Argument]: Maximum number of results to be returned. Value: any integer (20 default) +// ROI [Optional Input Argument]: Query Image search region. Format [ x y width height ]. Default: [1 1 size(Image,2) size(Image,1)] +// averagePrecision: Average Precision Metric. Value in the range [0 1] +// imageID: M-by-1 vector consisting of Ranked Index of retrieved Images +// Scores: M-by-1 vector containing the similarity metric in the range 0 to 1 +// +// Description +// It returns the average precision metric for measuring the accuracy of image search results for the queryImage. +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// imageindex = indexImages(trainingSet, bag); +// queryImage = imread('sample.jpg'); +// imageIDs = retrieveImages(queryImage, imageindex); +// exp_id = [3 4 1 2]; /*For a 4 element image set*/ +// precision = evaluateImageRetreival(queryImage, imageindex, exp_id); +// +// Authors +// Umang Agrawal +// Rohit Suri + + /// varargout(1) = average_precision + /// varargout(2) = index + /// varargout(3) = score + [ lhs rhs ] = argn(0) + if rhs > 7 then + error(msprintf("Too many input arguments")) + end + + if lhs > 3 then + error(msprintf("Too many output arguments")) + end + image_list = mattolist(image) + + if lhs == 1 then + select rhs + case 3 then + + average_precision= opencv_evalutateImageRetrieval(image_list, IndexImage, ExpectedID) + + case 5 then + + average_precision = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2)) + + case 7 then + + average_precision = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2), varargin(3), varargin(4)) + end + + varargout(1) = average_precision + + elseif lhs == 2 then + select rhs + case 3 then + + [average_precision, index] = opencv_evalutateImageRetrieval(image_list, IndexImage, ExpectedID) + + case 5 then + + [average_precision, index] = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2)) + + case 7 then + + [average_precision, index] = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2), varargin(3), varargin(4)) + end + + varargout(1) = average_precision + varargout(2) = index + + elseif lhs == 3 then + select rhs + case 3 then + + [average_precision, index, score] = opencv_evalutateImageRetrieval(image_list, IndexImage, ExpectedID) + + case 5 then + + [average_precision, index, score] = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2)) + + case 7 then + + [average_precision, index, score] = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2), varargin(3), varargin(4)) + end + + varargout(1) = average_precision + varargout(2) = index + varargout(3) = score + + end + +endfunction diff --git a/macros/extractFeatures.bin b/macros/extractFeatures.bin new file mode 100644 index 0000000..efd8414 Binary files /dev/null and b/macros/extractFeatures.bin differ diff --git a/macros/extractFeatures.sci b/macros/extractFeatures.sci new file mode 100644 index 0000000..8147891 --- /dev/null +++ b/macros/extractFeatures.sci @@ -0,0 +1,168 @@ +function [featureVector, ValidPoints]=extractFeatures(image,points,class,varargin) +// This function is used to extract interest point descriptors +// +// Calling Sequence +// [features, validPoints] = extractFeatures(Image, points, Class, Name, Value... ) +// +// Parameters +// Image: Input Image, should be a 2-D grayscale. The Input Image should be real +// points: M-by-2 vector containing the list of Interest Points +// Class: The Name of the feature detecting algorithm used for interest point detection +// Method: The desired algorithm to be used for feature vector calculation +// Metric: M-by-1 vector containing the strength of eack interest Point +// BlockSize: An odd integer scalar for defining a square neighbourhood centered at each interest point for Block Detection Method +// Upright: Rotation Invariance Flag specified as a boolean 'True' or 'False'. Default Value: False(0) +// SURFSize: Length of SURF Feature Vector. Values: [64(default)|128] +// Orientation: M-by-1 vector containing the orientation of the detected feature in radians +// Axes: M-by-2 vector containing the axes length values detected for MSER Object +// Scale: M-by-1 vector containing the scale of detection of each Interest Point +// SignOfLaplacian: M-by-1 vector containing the values of Sign of Laplacian of a SURF Object +// features: M-by-N Feature Matrix containing M feature vectors each of length N +// validPoints: M-by-2 Matrix containing the set of valid points whose features have been extracted +// +// Description +// The function derives the descriptors from pixels surrounding an interest point, where each interest point specifies the location of the detected region center. +// +// Examples +// image = imread('sample.jpg'); +// [corners count metric] = detectHarrisFeatures(image); +// [features, valid_corners] = extractFeatures(image, corners, "Class", "cornerPoints", "Metric", metric); +// +// Authors +// Umang Agrawal +// Sridhar Reddy + + [lhs rhs]=argn(0); + if lhs>2 + error(msprintf(" Too many output arguments")); + elseif rhs>13 + error(msprintf(" Too many input arguments")); + end + imageList=mattolist(image); + + //default values + blockSize=0; + upright=1; + surfSize=64; + if (class=="cornerPoints" | class=="BRISKPoints") + method="FREAK"; + elseif (class=="MSERRegions" | class=="SURFPoints") + method="SURF"; + elseif (class=="Matrix") + method="Block"; + else + error(msprintf(" Invalid class type, check documentation for the list of valid types")); + end + + //Count to ensure argument called only once + count_metric=0,count_orientation,count_scale,count_axes,count_signOfLaplacian; + count_method=0, count_block=0, count_surf=0, count_upright=0; + + //Parsing input arguments + for i=1:rhs-3 + if (varargin(i)=="Metric") + if(count_metric==0) + i=i+1; + metric=varargin(i); + else + error(msprintf(" Metric argument has been called twice")); + end + elseif (varargin(i)=="Orientation") + if(count_orientation==0) + i=i+1; + orientation=varargin(i); + else + error(msprintf(" Orientation argument has been called twice")); + end + elseif (varargin(i)=="Scale") + if(count_scale==0) + i=i+1; + scale=varargin(i); + else + error(msprintf(" Scale argument has been called twice")); + end + elseif (varargin(i)=="Axes") + if(count_axes==0) + i=i+1; + axes=varargin(i); + else + error(msprintf(" Axes argument has been called twice")); + end + elseif (varargin(i)=="SignOfLaplacian") + if(count_signOfLaplacian==0) + i=i+1; + signOfLaplacian=varargin(i); + else + error(msprintf("SignOfLaplacian argument has been called twice")); + end + elseif (varargin(i)=="Method") + if(count_method==0) + i=i+1; + method=varargin(i); + else + error(msprintf("Method argument has been called twice")); + end + elseif (varargin(i)=="BlockSize") + if(count_block==0) + i=i+1; + blockSize=varargin(i); + else + error(msprintf("BlockSize argument has been called twice")); + end + elseif (varargin(i)=="SurfSize") + if(count_surf==0) + i=i+1; + surfSize=varargin(i); + else + error(msprintf("SurfSize argument has been called twice")); + end + elseif (varargin(i)=="Upright") + if(count_upright==0) + i=i+1; + upright=varargin(i); + else + error(msprintf("Upright argument has been called twice")); + end + else + error(msprintf("Wrong argument, check documentation for the list of valid arguments")); + end + end + + + select class + case "cornerPoints" then + if(count_metric==0) + error(msprintf("For cornerPoints Class Metric argument is necessary")); + end + [featureVectors ValidPoints numBits numFeatures count metric]=extractFeatures(imageList, points, "Class", class, "Method", method, "surfSize", surfSize, "blockSize", blockSize, "upright", upright, "Metric",metric); + + case "BRISKPoints" then + if(count_metric==0 | count_scale==0 | count_orientation==0) + error(msprintf("For BRISKPoints Class Metric, Orientation and Scale arguments are necessary")); + end + [featureVectors ValidPoints numBits numFeatures scale metrix orientation count]=extractFeatures(imageList, points, "Class", class, "Method", method, "surfSize", surfSize, "blockSize", blockSize, "upright", upright, "Metric",metric,"Orientation",orientation,"Scale",scale); + + case "MSERRegions" then + if( count_orientation==0 | count_axes==0) + error(msprintf("For MSERRegions Class Orientation Matrix and Axes Matrix arguments are necessary.")); + end + [featureVectors ValidPoints numBits numFeatures count axes orientation]=extractFeatures(imageList, points, "Class", class, "Method", method, "surfSize", surfSize, "blockSize", blockSize, "upright", upright, "Metric",metric,"Orientation",orientation,"Axes",axes); + + case "SURFPoints" then + if(count_scale==0 | count_metric==0 | count_signOfLaplacian==0 | count_orientation==0) + error(msprintf("For SURFPoints Class Metric, Orientation, Scale and SignOfLaplacian arguments are necessary")); + end + [featureVectors ValidPoints numBits numFeatures count scale metrix signOfLaplacian orientation]=extractFeatures(imageList, points, "Class", class, "Method", method, "surfSize", surfSize, "blockSize", blockSize, "upright", upright, "Metric",metric,"Orientation",orientation,"Scale",scale,"SignOfLaplacian",signOfLaplacian); + + case "Matrix" then + if(count_block==0) + error(msprintf("For Matrix Class BlockSize argument is necessary")); + end + [featureVectors ValidPoints numBits numFeatures]=extractFeatures(imageList, points, "Class", class, "Method", method, "surfSize", surfSize, "blockSize", blockSize, "upright", upright,"BlockSize",blockSize); + end + + + + featureVector=struct("Type","binaryFeatures",'Features',featureVectors,'NumBits',numBits,'NumFeatures',numFeatures); + +endfunction diff --git a/macros/extractHOGFeatures.bin b/macros/extractHOGFeatures.bin new file mode 100644 index 0000000..df60285 Binary files /dev/null and b/macros/extractHOGFeatures.bin differ diff --git a/macros/extractHOGFeatures.sci b/macros/extractHOGFeatures.sci new file mode 100644 index 0000000..cf7aca6 --- /dev/null +++ b/macros/extractHOGFeatures.sci @@ -0,0 +1,24 @@ +function [featureVector] = extractHOGFeatuers(I, varargin) + img = mattolist(I); + [lhs rhs] = argn(0); + if rhs<1 then + error(msprintf("Not enough input arguments")); + end + if rhs>9 then + error(msprintf("Too many input arguments")); + end + l = rhs-1; + if(l==0) then + featureVector = opencv_extractHOGFeatures(img); + elseif (l==2) then + featureVector = opencv_extractHOGFeatures(img,varargin(0),varargin(1)); + elseif (l==4) then + featureVector = opencv_extractHOGFeatures(img,varargin(0),varargin(1),varargin(2),varargin(3)); + elseif (l==6) then + featureVector = opencv_extractHOGFeatures(img,varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5)); + else + featureVector = opencv_extractHOGFeatures(img,varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7)); + end + +endfunction + diff --git a/macros/extractLBPFeatures.bin b/macros/extractLBPFeatures.bin new file mode 100644 index 0000000..d057c9b Binary files /dev/null and b/macros/extractLBPFeatures.bin differ diff --git a/macros/extractLBPFeatures.sci b/macros/extractLBPFeatures.sci new file mode 100644 index 0000000..177e660 --- /dev/null +++ b/macros/extractLBPFeatures.sci @@ -0,0 +1,46 @@ +function [out]=extractLBPFeatures(input1,varargin) + [lhs rhs] = argn(0) + if rhs>13 then + error(msprintf("Too many input arguments")) + elseif rhs==13 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8),varargin(9),varargin(10),varargin(11),varargin(12)) + out(:,:,1)=a(1) + elseif rhs==12 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8),varargin(9),varargin(10),varargin(11)) + out(:,:,1)=a(1) + elseif rhs==11 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8),varargin(9),varargin(10)) + out(:,:,1)=a(1) + elseif rhs==10 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8),varargin(9)) + out(:,:,1)=a(1) + elseif rhs==9 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8)) + out(:,:,1)=a(1) + elseif rhs==8 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7)) + out(:,:,1)=a(1) + elseif rhs==7 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)) + out(:,:,1)=a(1) + elseif rhs==6 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5)) + out(:,:,1)=a(1) + elseif rhs==5 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3),varargin(4)) + out(:,:,1)=a(1) + elseif rhs==4 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2),varargin(3)) + out(:,:,1)=a(1) + elseif rhs==3 then + a=opencv_extractLBPFeatures(input1,varargin(1),varargin(2)) + out(:,:,1)=a(1) + elseif rhs==2 then + a=opencv_extractLBPFeatures(input1,varargin(1)) + out(:,:,1)=a(1) + elseif rhs==1 then + a=opencv_extractLBPFeatures(input1) + out(:,:,1)=a(1) + end, + +endfunction; diff --git a/macros/fftshift.bin b/macros/fftshift.bin new file mode 100644 index 0000000..d9858da Binary files /dev/null and b/macros/fftshift.bin differ diff --git a/macros/fftshift.sci b/macros/fftshift.sci new file mode 100644 index 0000000..48b242a --- /dev/null +++ b/macros/fftshift.sci @@ -0,0 +1,9 @@ +function [out]=fftshift(image) + image1=mattolist(image); + a=opencv_fftshift(image1); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/fillconvexpoly.bin b/macros/fillconvexpoly.bin new file mode 100644 index 0000000..c8ddae8 Binary files /dev/null and b/macros/fillconvexpoly.bin differ diff --git a/macros/fillconvexpoly.sci b/macros/fillconvexpoly.sci new file mode 100644 index 0000000..ac40c8c --- /dev/null +++ b/macros/fillconvexpoly.sci @@ -0,0 +1,8 @@ +function [out] = fillconvexpoly(img,pstData,npts,r_value,g_value,b_value,linetype,shift) + image = mattolist(img); + a = opencv_fillconvexpoly(image,pstData,npts,r_value,g_value,b_value,linetype,shift) + d = size(a); + for i=1:d + out(:,:,i) = a(i); + end +endfunction diff --git a/macros/filter.bin b/macros/filter.bin new file mode 100644 index 0000000..7c420be Binary files /dev/null and b/macros/filter.bin differ diff --git a/macros/filter.sci b/macros/filter.sci new file mode 100644 index 0000000..e628942 --- /dev/null +++ b/macros/filter.sci @@ -0,0 +1,8 @@ +function [out]=filter(input_image,depth,kernel_matrix,anchor_x,anchor_y,delta) + input_image1=mattolist(input_image); + a=opencv_filter(input_image1,depth,kernel_matrix,anchor_x,anchor_y,delta); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end +endfunction; diff --git a/macros/findcontours.bin b/macros/findcontours.bin new file mode 100644 index 0000000..d331762 Binary files /dev/null and b/macros/findcontours.bin differ diff --git a/macros/findcontours.sci b/macros/findcontours.sci new file mode 100644 index 0000000..382aa88 --- /dev/null +++ b/macros/findcontours.sci @@ -0,0 +1,7 @@ +function [outputMat]= findcontours(inputImage, Mode, method, point_x, point_y) + inputList=mattolist(inputImage); + outputList=opencv_findcontours(inputList,Mode, method, point_x, point_y) + for i=1:size(outputList) + outputMat(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/fitellipse.bin b/macros/fitellipse.bin new file mode 100644 index 0000000..7a3510b Binary files /dev/null and b/macros/fitellipse.bin differ diff --git a/macros/fitellipse.sci b/macros/fitellipse.sci new file mode 100644 index 0000000..cb0331f --- /dev/null +++ b/macros/fitellipse.sci @@ -0,0 +1,4 @@ +function [out]=fitellipse(InputArraypoints) + [t1 t2 t3 t4 t5]= opencv_fitellipse(InputArraypoints); + out=struct("size1",t1,"size2",t2,"center1",t3,"center2",t4,"angle",t3); +endfunction; diff --git a/macros/fsamp2.bin b/macros/fsamp2.bin new file mode 100644 index 0000000..c1a77e7 Binary files /dev/null and b/macros/fsamp2.bin differ diff --git a/macros/fsamp2.sci b/macros/fsamp2.sci new file mode 100644 index 0000000..4c3b97a --- /dev/null +++ b/macros/fsamp2.sci @@ -0,0 +1,31 @@ +// Function Name = Frequency sampling + +// Inputs = Frequency response Hd; frequency vectors f1, f2; dimension [m n] + +// Output = matrix h of dimension m x n, stores the filter coeff + +function[h] = fsamp2(Hd, varargin) + + [lhs, rhs] = argn(0) + + select rhs + case 1 then + out = opencv_fsamp2(Hd) + + case 2 then + out = opencv_fsamp2(Hd, varargin(1)) + + case 3 then + out = opencv_fsamp2(Hd, varargin(1), varargin(2)) + + case 4 then + out = opencv_fsamp2(Hd, varargin(1), varargin(2), varargin(3)) + end + + channel = size(out) + + for i = 1: channel + h(:,:,i) = out(i) + end + +endfunction diff --git a/macros/ftrans2.bin b/macros/ftrans2.bin new file mode 100644 index 0000000..1fdf531 Binary files /dev/null and b/macros/ftrans2.bin differ diff --git a/macros/ftrans2.sci b/macros/ftrans2.sci new file mode 100644 index 0000000..233e9cc --- /dev/null +++ b/macros/ftrans2.sci @@ -0,0 +1,25 @@ +// Function Name = Frequency Transformation + +// Inputs = 1-D FIR filter b; Transform t(varargin(1)) + +// Output = 2-D FIR filter h + +function[h] = ftrans2(b, t) + + [lhs, rhs] = argn(0) + + select rhs + case 1 then + out = opencv_ftrans2(b) + + case 2 then + out = opencv_ftrans2(b, varargin(1)) + end + + channel = size(out) + + for i = 1: channel + h(:,:,i) = out(i) + end + +endfunction diff --git a/macros/gabor.bin b/macros/gabor.bin new file mode 100644 index 0000000..68a9800 Binary files /dev/null and b/macros/gabor.bin differ diff --git a/macros/gabor.sci b/macros/gabor.sci new file mode 100644 index 0000000..02e6ea9 --- /dev/null +++ b/macros/gabor.sci @@ -0,0 +1,6 @@ +function [outputImg]=gabor(wavelength,orientation) + outputList=opencv_gabor(wavelength,orientation); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/gaussianblur.bin b/macros/gaussianblur.bin new file mode 100644 index 0000000..50872a6 Binary files /dev/null and b/macros/gaussianblur.bin differ diff --git a/macros/gaussianblur.sci b/macros/gaussianblur.sci new file mode 100644 index 0000000..8b6b0c1 --- /dev/null +++ b/macros/gaussianblur.sci @@ -0,0 +1,7 @@ +function [outputImg]= gaussianblur(inputImage,ksize_height,ksize_width,sigmaX,sigmaY) + inputList=mattolist(inputImage); + outputList=opencv_gaussianblur(inputList,ksize_height,ksize_width,sigmaX,sigmaY); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/genCheckerboardPoints.bin b/macros/genCheckerboardPoints.bin new file mode 100644 index 0000000..e7a42a8 Binary files /dev/null and b/macros/genCheckerboardPoints.bin differ diff --git a/macros/genCheckerboardPoints.sci b/macros/genCheckerboardPoints.sci new file mode 100644 index 0000000..53528bc --- /dev/null +++ b/macros/genCheckerboardPoints.sci @@ -0,0 +1,26 @@ +function [ worldPoints ] = genCheckerboardPoints(boardSize, squareSize) +// This function returns list of checkerboard corner points. +// +// Calling Sequence +// worldPoints = genCheckerboardPoints(boardSize, squareSize); +// +// Parameters +// worldPoints: A list of size N-by-2 matrix containing x and y co-ordinates of the checkerboard corner points. The origin is set to lower-right corner of the top-left square. +// boardSize: Size of the checkerboard. +// squareSize: Size of each of square in the checkerboard. +// +// Description +// Return a list of size N-by-2, which contains the corner points of all squares of a checkerboard. +// +// Examples +// boardSize = [10 10]; +// squareSize = 8; +// worldPoints = genCheckerboardPoints(boardSize, squareSize); +// +// Authors +// Tanmay Chaudhari + + a=opencv_genCheckerboardPoints(boardSize, squareSize); + worldPoints(:,:,1)=a(1); + +endfunction diff --git a/macros/geometricshearer.bin b/macros/geometricshearer.bin new file mode 100644 index 0000000..2da1efb Binary files /dev/null and b/macros/geometricshearer.bin differ diff --git a/macros/geometricshearer.sci b/macros/geometricshearer.sci new file mode 100644 index 0000000..8d8e72a --- /dev/null +++ b/macros/geometricshearer.sci @@ -0,0 +1,33 @@ +function [out] = GeometricShearer(inputimage,direction,value) +// Shear the image +// +// Calling Sequence +// output_image = GeometricShearer(inputimage,direction,value); +// +// Parameters +// +// inputimage : image matrix on which geometric shear has to be applied +// direction : An input string that like. 'Horizontal' to shear the image in horizontal direction or 'Vertical' to shear the image in vertical direction +// value : A scalar value i.e amount to want shift the pixel of image +// output_image : sheared image +// +// Description +// This function returns the sheared image.It shears the image by shifting the rows or columns of image. +// +// Examples +// a = imread('lena.jpeg'); +// b = GeometricShearer(a,'Horzontal',5); +// imshow(b); +// +// Authors: +// +//Diwakar Bhardwaj + + inputimage1=mattolist(inputimage); + a = opencv_GeometricShearer(inputimage1,direction,value); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/getStructuringElement.bin b/macros/getStructuringElement.bin new file mode 100644 index 0000000..e579a01 Binary files /dev/null and b/macros/getStructuringElement.bin differ diff --git a/macros/getStructuringElement.sci b/macros/getStructuringElement.sci new file mode 100644 index 0000000..23b384c --- /dev/null +++ b/macros/getStructuringElement.sci @@ -0,0 +1,5 @@ +function structuring_element = getStructuringElement(gettype, cols, rows, anchorX, anchorY) + + structuring_element = opencv_getStructuringElement(gettype, cols, rows, anchorX, anchorY) + +endfunction diff --git a/macros/getTextSize.bin b/macros/getTextSize.bin new file mode 100644 index 0000000..89bb3f6 Binary files /dev/null and b/macros/getTextSize.bin differ diff --git a/macros/getTextSize.sci b/macros/getTextSize.sci new file mode 100644 index 0000000..a8c911a --- /dev/null +++ b/macros/getTextSize.sci @@ -0,0 +1,3 @@ +function [textSize,baseLine]= getTextSize(inputtext,fontface,fontscale,thickness) + [textSize,baseLine]=opencv_getTextSize(inputtext,fontface,fontscale,thickness) +endfunction diff --git a/macros/getgaussiankernel.bin b/macros/getgaussiankernel.bin new file mode 100644 index 0000000..1845109 Binary files /dev/null and b/macros/getgaussiankernel.bin differ diff --git a/macros/getgaussiankernel.sci b/macros/getgaussiankernel.sci new file mode 100644 index 0000000..4fcf144 --- /dev/null +++ b/macros/getgaussiankernel.sci @@ -0,0 +1,5 @@ +function output = getGaussianKernel(ksize, sigma, ktype) + + output = opencv_getGaussianKernel(ksize, sigma, ktype) + +endfunction diff --git a/macros/getkerneltype.bin b/macros/getkerneltype.bin new file mode 100644 index 0000000..c87cddf Binary files /dev/null and b/macros/getkerneltype.bin differ diff --git a/macros/getkerneltype.sci b/macros/getkerneltype.sci new file mode 100644 index 0000000..fafe18d --- /dev/null +++ b/macros/getkerneltype.sci @@ -0,0 +1,4 @@ +function [out]=getkerneltype(InputArray,pointpx,pointpy) + out=opencv_getkerneltype(InputArray,pointpx,pointpy); + +endfunction; diff --git a/macros/getrectsubpix.bin b/macros/getrectsubpix.bin new file mode 100644 index 0000000..9f533ec Binary files /dev/null and b/macros/getrectsubpix.bin differ diff --git a/macros/getrectsubpix.sci b/macros/getrectsubpix.sci new file mode 100644 index 0000000..792be36 --- /dev/null +++ b/macros/getrectsubpix.sci @@ -0,0 +1,13 @@ +function new_image = getrectsubpix(image, alpha, beta, centerX, centerY, patchType) + + image_list = mattolist(image) + + out = opencv_getrectsubpix(image_list, alpha, beta, centerX, centerY, patchType) + + sz = size(out) + + for i = 1 : sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/getrotationmatrix2d.bin b/macros/getrotationmatrix2d.bin new file mode 100644 index 0000000..fe238e3 Binary files /dev/null and b/macros/getrotationmatrix2d.bin differ diff --git a/macros/getrotationmatrix2d.sci b/macros/getrotationmatrix2d.sci new file mode 100644 index 0000000..f1cc44f --- /dev/null +++ b/macros/getrotationmatrix2d.sci @@ -0,0 +1,3 @@ +function [out]=getrotationmatrix2d(Point2fcenter, doubleangle, doublescale) + out=opencv_getrotationmatrix2d(Point2fcenter, doubleangle, doublescale); +endfunction; diff --git a/macros/goodfeaturestotrack.bin b/macros/goodfeaturestotrack.bin new file mode 100644 index 0000000..0b21763 Binary files /dev/null and b/macros/goodfeaturestotrack.bin differ diff --git a/macros/goodfeaturestotrack.sci b/macros/goodfeaturestotrack.sci new file mode 100644 index 0000000..d8bb5bc --- /dev/null +++ b/macros/goodfeaturestotrack.sci @@ -0,0 +1,7 @@ +function corner_points = goodfeaturestotrack(image, maxcorners, qualityLevel, minDistance, blocksize, usedetector, k) + + image_list = mattolist(image) + + corner_points = opencv_goodfeaturestotrack(image_list, maxcorners, qualityLevel, minDistance, blocksize, usedetector, k) + +endfunction diff --git a/macros/grayconnected.bin b/macros/grayconnected.bin new file mode 100644 index 0000000..839bdbd Binary files /dev/null and b/macros/grayconnected.bin differ diff --git a/macros/grayconnected.sci b/macros/grayconnected.sci new file mode 100644 index 0000000..09a7289 --- /dev/null +++ b/macros/grayconnected.sci @@ -0,0 +1,21 @@ +function [out]=grayconnected(input1,input2,input3,varargin) + inputn=mattolist(input1); + [lhs rhs] = argn(0) + if rhs>4 then + error(msprintf("Too many input arguments")) + elseif rhs==4 then + a=opencv_grayconnected(inputn,input2,input3,varargin(1)); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + else + a=opencv_grayconnected(inputn,input2,input3); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + end, + +endfunction; + diff --git a/macros/graycoprops.bin b/macros/graycoprops.bin new file mode 100644 index 0000000..694912a Binary files /dev/null and b/macros/graycoprops.bin differ diff --git a/macros/graycoprops.sci b/macros/graycoprops.sci new file mode 100644 index 0000000..d33c2a0 --- /dev/null +++ b/macros/graycoprops.sci @@ -0,0 +1,19 @@ +function[stats] = graycoprops(srcImg, varargin) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + select rhs + case 1 then + out = graycoprops(srcMat) + case 2 then + out = graycoprops(srcMat, varargin(1)) + end + + channels = size(out) + + for i = 1:channels + stats(:,:,1) = out(1) + end +endfunction diff --git a/macros/graydiffweight.bin b/macros/graydiffweight.bin new file mode 100644 index 0000000..d809d37 Binary files /dev/null and b/macros/graydiffweight.bin differ diff --git a/macros/graydiffweight.sci b/macros/graydiffweight.sci new file mode 100644 index 0000000..443a79f --- /dev/null +++ b/macros/graydiffweight.sci @@ -0,0 +1,9 @@ +function [out]=graydiffweight(image,refgrayval) + image1=mattolist(image); + a=opencv_graydiffweight(image1,refgrayval); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/graydist.bin b/macros/graydist.bin new file mode 100644 index 0000000..bb5187d Binary files /dev/null and b/macros/graydist.bin differ diff --git a/macros/graydist.sci b/macros/graydist.sci new file mode 100644 index 0000000..fc3c081 --- /dev/null +++ b/macros/graydist.sci @@ -0,0 +1,13 @@ +function[dstImg] = graydist(srcImg, mask) + + srcMat = mattolist(srcImg) + + out = opencv_graydist(srcMat, mask) + + channels = size(out) + + for i = 1:channels + dstImg(:,:,i) = out(i) + end + +endfunction diff --git a/macros/has_frame.bin b/macros/has_frame.bin new file mode 100644 index 0000000..92ca7ca Binary files /dev/null and b/macros/has_frame.bin differ diff --git a/macros/has_frame.sci b/macros/has_frame.sci new file mode 100644 index 0000000..158fb73 --- /dev/null +++ b/macros/has_frame.sci @@ -0,0 +1,22 @@ +function [out]=hasFrame() +// This function is used to check whether there is a frame to read in the current video, read through VideoReader. +// +// Calling Sequence +// results = hasFrame(); +// +// Parameters +// results: A boolean. True if there is a frame to read and false if not. +// +// Description +// This is a helper function for VideoReader and readFrame() function. It returns true if the video has a +// frame to read, else it returns false. +// +// Examples +// videoStr = VideoReader('sample.mp4'); +// hasFrame(); +// +// Authors +// Shashank Shekhar +// Tanmay Chaudhari + out=ocv_hasFrame(); +endfunction; diff --git a/macros/histeq.bin b/macros/histeq.bin new file mode 100644 index 0000000..8b6c28b Binary files /dev/null and b/macros/histeq.bin differ diff --git a/macros/histeq.sci b/macros/histeq.sci new file mode 100644 index 0000000..398facf --- /dev/null +++ b/macros/histeq.sci @@ -0,0 +1,9 @@ +function [out]=histeq(image) + image1=mattolist(image); + a=opencv_histeq(image1); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/houghcircles.bin b/macros/houghcircles.bin new file mode 100644 index 0000000..85a767c Binary files /dev/null and b/macros/houghcircles.bin differ diff --git a/macros/houghcircles.sci b/macros/houghcircles.sci new file mode 100644 index 0000000..b5ada30 --- /dev/null +++ b/macros/houghcircles.sci @@ -0,0 +1,7 @@ +function outputImg= houghcircles(inputImage , dp , mindist , param1, param2 ,minradius, maxradius) + inputList=mattolist(inputImage) + outputList=opencv_houghcircles(inputList , dp , mindist , param1, param2 ,minradius, maxradius) + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/houghlines.bin b/macros/houghlines.bin new file mode 100644 index 0000000..c74e2b8 Binary files /dev/null and b/macros/houghlines.bin differ diff --git a/macros/houghlines.sci b/macros/houghlines.sci new file mode 100644 index 0000000..ae9bc1c --- /dev/null +++ b/macros/houghlines.sci @@ -0,0 +1,4 @@ +function [out]=houghlines(image,rho, theta, threshold, srn, stn) + image1=mattolist(image); + out=opencv_houghlines(image1,rho, theta, threshold, srn, stn); +endfunction; diff --git a/macros/houghlinesp.bin b/macros/houghlinesp.bin new file mode 100644 index 0000000..5656868 Binary files /dev/null and b/macros/houghlinesp.bin differ diff --git a/macros/houghlinesp.sci b/macros/houghlinesp.sci new file mode 100644 index 0000000..81007aa --- /dev/null +++ b/macros/houghlinesp.sci @@ -0,0 +1,7 @@ +function output = houghlinesp(image, rho, theta, threshold, minLineLength, maxLineGap) + + image_list = mattolist(image) + + output = opencv_houghlinesp(image_list, rho, theta, threshold, minLineLength, maxLineGap) + +endfunction diff --git a/macros/ifftshift.bin b/macros/ifftshift.bin new file mode 100644 index 0000000..db03f31 Binary files /dev/null and b/macros/ifftshift.bin differ diff --git a/macros/ifftshift.sci b/macros/ifftshift.sci new file mode 100644 index 0000000..0d79a51 --- /dev/null +++ b/macros/ifftshift.sci @@ -0,0 +1,12 @@ +function new_image = ifftshift(image) + + image_list = mattolist(image) + + out = opencv_ifftshift(image_list) + + sz = size(out) + for i = 1 : sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/im2double.bin b/macros/im2double.bin new file mode 100644 index 0000000..79ad7f0 Binary files /dev/null and b/macros/im2double.bin differ diff --git a/macros/im2double.sci b/macros/im2double.sci new file mode 100644 index 0000000..7f8e585 --- /dev/null +++ b/macros/im2double.sci @@ -0,0 +1,13 @@ +function[doubleMat] = im2double(matA) + + srcMat = mattolist(matA) + + out = opencv_im2double(srcMat) + + ch = size(out) + + for i = 1:ch + doubleMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imadd.bin b/macros/imadd.bin new file mode 100644 index 0000000..8c14b1a Binary files /dev/null and b/macros/imadd.bin differ diff --git a/macros/imadd.sci b/macros/imadd.sci new file mode 100644 index 0000000..93e1273 --- /dev/null +++ b/macros/imadd.sci @@ -0,0 +1,7 @@ +function[sum] = imadd(matA, matB) + + [lhs, rhs] = argn(0) + + sum = opencv_imadd(matA, matB) + +endfunction diff --git a/macros/imageSet.bin b/macros/imageSet.bin new file mode 100644 index 0000000..aa7de86 Binary files /dev/null and b/macros/imageSet.bin differ diff --git a/macros/imageSet.sci b/macros/imageSet.sci new file mode 100644 index 0000000..55a4e03 --- /dev/null +++ b/macros/imageSet.sci @@ -0,0 +1,47 @@ +function [imgSet]=imageSet(imageFolder,varargin) +// This function is used to create a collection of images. +// +// Calling Sequence +// imgSet = imageSet(location) +// imgSet = imageSet(location, 'recursive') +// +// Parameters +// imgSet: Structure containing collection of images +// location: Address of the folder from which collection is to be created +// +// Description +// This function creates an imageSet structure with the following attributes- Description, ImageLocation, and Count. +// +// Examples +// imgSet = imageSet(directory); +// +// imgSet = imageSet(directory,'recursive'); +// +// Authors +// Rohit Suri + + [lhs rhs]=argn(0); + if lhs>1 + error(msprintf(" Too many output arguments")); + elseif rhs<1 + error(msprintf(" Not enough input arguments")); + elseif rhs>2 + error(msprintf(" Too many input arguments")); + end + if rhs==1 then + imgSetList=opencv_imageSet(imageFolder); + for i=1:imgSetList(3) + imgLocations(i)=imgSetList(4)(1)(1,i); + end + imgSet=struct('Description',imgSetList(2),'ImageLocation',imgLocations,'Count',double(imgSetList(3))); + else + imgSetList=opencv_imageSet(imageFolder,varargin(1)); + for i=1:length(imgSetList(3)) + for j=1:imgSetList(3)(i) + imgLocations(j)=imgSetList(4)(i)(1,j); + end + imgSet(1,i)=struct('Description',imgSetList(2)(i),'ImageLocation',imgLocations,'Count',int32(imgSetList(3)(i))); + imgLocations=[]; + end + end +endfunction diff --git a/macros/imageSetToList.bin b/macros/imageSetToList.bin new file mode 100644 index 0000000..09d3aca Binary files /dev/null and b/macros/imageSetToList.bin differ diff --git a/macros/imageSetToList.sci b/macros/imageSetToList.sci new file mode 100644 index 0000000..1e7a461 --- /dev/null +++ b/macros/imageSetToList.sci @@ -0,0 +1,30 @@ +function imgSetList=imageSetToList(imageSet) + if isstruct(imageSet)<> %T then + error(msprintf("Error: The input argument is not of type imageSet.")); + end + fieldNamesActual=["Description";"ImageLocation";"Count"]; + fieldNamesInput=fieldnames(imageSet); + if fieldNamesActual <> fieldNamesInput then + error(msprintf("Error: The input argument is not of type imageSet, Wrong field names")); + end + if size(imageSet) == [1 1] then + locationList=list(); + Desc=imageSet.Description; + Count=int32(imageSet.Count); + for j=1:imageSet.Count + tempMat(1,j)=imageSet.ImageLocation(j); + end + locationList($+1)=tempMat; + else + locationList=list(); + for i=1:size(imageSet.Count) + Desc(i)=imageSet(i).Description; + Count(i)=int32(imageSet(i).Count); + for j=1:imageSet(i).Count + tempMat(1,j)=imageSet(i).ImageLocation(j); + end + locationList($+1)=tempMat; + end + end + imgSetList=list("imageSet",Desc,Count,locationList); +endfunction diff --git a/macros/imboxfilt3.bin b/macros/imboxfilt3.bin new file mode 100644 index 0000000..d464164 Binary files /dev/null and b/macros/imboxfilt3.bin differ diff --git a/macros/imboxfilt3.sci b/macros/imboxfilt3.sci new file mode 100644 index 0000000..cf27438 --- /dev/null +++ b/macros/imboxfilt3.sci @@ -0,0 +1,28 @@ +//1) box_filtered_image = imboxfilt3(input_img); + //In this usage, the default filter size of 3x3 is used. + +// 2) box_filtered_image = imboxfilt3(input_img, filter_height, filter_width); + +function[dstMat] = imboxfilt3(srcImg, varargin) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + select rhs + case 1 then + out = opencv_imboxfilt3(srcMat) + + case 2 then + out = opencv_imboxfilt3(srcMat, varargin(1)) + + case 3 then + out = opencv_imboxfilt3(srcMat, varargin(1), varargin(2)) + end + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imcomplement.bin b/macros/imcomplement.bin new file mode 100644 index 0000000..e01330a Binary files /dev/null and b/macros/imcomplement.bin differ diff --git a/macros/imcomplement.sci b/macros/imcomplement.sci new file mode 100644 index 0000000..426e871 --- /dev/null +++ b/macros/imcomplement.sci @@ -0,0 +1,13 @@ +function[compMat] = imcomplement(srcImg) + + srcMat = mattolist(srcImg) + + out = opencv_imcomplement(srcMat) + + ch = size(out) + + for i=1:ch + compMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imcontour.bin b/macros/imcontour.bin new file mode 100644 index 0000000..41999d6 Binary files /dev/null and b/macros/imcontour.bin differ diff --git a/macros/imcontour.sci b/macros/imcontour.sci new file mode 100644 index 0000000..df41b0f --- /dev/null +++ b/macros/imcontour.sci @@ -0,0 +1,7 @@ +function[contourMat] = imcontour(matA) + + srcMat = mattolist(matA) + + contourMat = opencv_imcontour(srcMat) + +endfunction diff --git a/macros/imcontrast.bin b/macros/imcontrast.bin new file mode 100644 index 0000000..2ef7cb7 Binary files /dev/null and b/macros/imcontrast.bin differ diff --git a/macros/imcontrast.sci b/macros/imcontrast.sci new file mode 100644 index 0000000..2666c0f --- /dev/null +++ b/macros/imcontrast.sci @@ -0,0 +1,7 @@ +function[contrastMat] = imcontrast(srcImg, alpha, beta) + + srcMat = mattolist(srcImg) + + contrastMat = opencv_imcontrast(srcMat, alpha, beta) + +endfunction diff --git a/macros/imcrop.bin b/macros/imcrop.bin new file mode 100644 index 0000000..71a459a Binary files /dev/null and b/macros/imcrop.bin differ diff --git a/macros/imcrop.sci b/macros/imcrop.sci new file mode 100644 index 0000000..ac1cb5f --- /dev/null +++ b/macros/imcrop.sci @@ -0,0 +1,5 @@ +function[cropImg] = imcrop(srcImg, xcoor, ycoor, width, height) + srcMat = mattolist(srcImg) +// disp(srcMat) + cropImg = opencv_imcrop(srcMat, xcoor, ycoor, width, height) +endfunction diff --git a/macros/imcrop.sci~ b/macros/imcrop.sci~ new file mode 100644 index 0000000..7af043f --- /dev/null +++ b/macros/imcrop.sci~ @@ -0,0 +1,5 @@ +function[cropImg] = imcrop(srcImg, xcoor, ycoor, width, height) + srcMat = mattolist(srcImg) + disp(srcMat) + cropImg = opencv_imcrop(srcMat, xcoor, ycoor, width, height) +endfunction diff --git a/macros/imdivide.bin b/macros/imdivide.bin new file mode 100644 index 0000000..b542bc6 Binary files /dev/null and b/macros/imdivide.bin differ diff --git a/macros/imdivide.sci b/macros/imdivide.sci new file mode 100644 index 0000000..11f8ca9 --- /dev/null +++ b/macros/imdivide.sci @@ -0,0 +1,5 @@ +function[divideMat] = imdivide(matA, matB) + + divideMat = opencv_imdivide(matA, matB) + +endfunction diff --git a/macros/imextendedmax.bin b/macros/imextendedmax.bin new file mode 100644 index 0000000..9331ee3 Binary files /dev/null and b/macros/imextendedmax.bin differ diff --git a/macros/imextendedmax.sci b/macros/imextendedmax.sci new file mode 100644 index 0000000..450e290 --- /dev/null +++ b/macros/imextendedmax.sci @@ -0,0 +1,13 @@ +function[dstMat] = imextendedmax(srcImg, Hmax) + + srcMat = mattolist(srcImg) + + out = opencv_imfill(srcMat, Hmax) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imextendedmin.bin b/macros/imextendedmin.bin new file mode 100644 index 0000000..a7ef048 Binary files /dev/null and b/macros/imextendedmin.bin differ diff --git a/macros/imextendedmin.sci b/macros/imextendedmin.sci new file mode 100644 index 0000000..bd7ef80 --- /dev/null +++ b/macros/imextendedmin.sci @@ -0,0 +1,13 @@ +function[dstMat] = imextendedmin(srcImg, Hmin) + + srcMat = mattolist(srcImg) + + out = opencv_imextendedmin(srcMat, Hmin) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imfill.bin b/macros/imfill.bin new file mode 100644 index 0000000..f1dbb7e Binary files /dev/null and b/macros/imfill.bin differ diff --git a/macros/imfill.sci b/macros/imfill.sci new file mode 100644 index 0000000..c8136fb --- /dev/null +++ b/macros/imfill.sci @@ -0,0 +1,20 @@ +function[dstMat] = imfill(srcImg, varargin) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + select rhs + case 1 then + out = opencv_imfill(srcMat) + case 2 then + out = opencv_imfill(srcMat, varargin(1)) + end + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imfindcircles.bin b/macros/imfindcircles.bin new file mode 100644 index 0000000..ac578cb Binary files /dev/null and b/macros/imfindcircles.bin differ diff --git a/macros/imfindcircles.sci b/macros/imfindcircles.sci new file mode 100644 index 0000000..c8f7491 --- /dev/null +++ b/macros/imfindcircles.sci @@ -0,0 +1,25 @@ +function [points, varargout] = imfindcircles(image, Rmin, Rmax) + + [lhs, rhs] = argn(0) + + image_list = mattolist(image) + + select lhs + case 1 then + out_centres = opencv_imfindcircles(image_list, Rmin, Rmax) + + case 2 then + [out_centres radii] = opencv_imfindcircles(image_list, Rmin, Rmax) + + varargout(1) = radii + end + + total_points = size(out_centres(1),'c') + + + for i=1:total_points + points(i, 1) = out_centres(1)(1, i) + points(i, 2) = out_centres(2)(1, i) + end + +endfunction diff --git a/macros/imfuse.bin b/macros/imfuse.bin new file mode 100644 index 0000000..818c7ec Binary files /dev/null and b/macros/imfuse.bin differ diff --git a/macros/imfuse.sci b/macros/imfuse.sci new file mode 100644 index 0000000..b86e8e3 --- /dev/null +++ b/macros/imfuse.sci @@ -0,0 +1,13 @@ +function new_image = imfuse(image1, image2, method, scaling) + + image_list1 = mattolist(image1) + image_list2 = mattolist(image2) + out = opencv_imfuse(image_list1, image_list2, method, scaling) + + sz = size(out) + + for i=1:sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/imgSetToList.bin b/macros/imgSetToList.bin new file mode 100644 index 0000000..b3b7877 Binary files /dev/null and b/macros/imgSetToList.bin differ diff --git a/macros/imgSetToList.sci b/macros/imgSetToList.sci new file mode 100644 index 0000000..fcbc37c --- /dev/null +++ b/macros/imgSetToList.sci @@ -0,0 +1,50 @@ +function imgSetList=imageSetToList(imageSet) + if isstruct(imageSet)<> %T then + error(msprintf("Error: The input argument is not of type imageSet.")); + end + fieldNamesActual=["Description";"ImageLocation";"Count"]; + fieldNamesInput=fieldnames(imageSet); + if fieldNamesActual <> fieldNamesInput then + error(msprintf("Error: The input argument is not of type imageSet, Wrong field names")); + end + if size(imageSet) == [1 1] then + if type(imageSet.Description)<> 10 | size(imageSet.Description) <> [1 1] then + error(msprintf("Error: The input argument is not of type imageSet, Description field must be string")); + end + if type(imageSet.Count)<> 1 | size(imageSet.Count) <> [1 1] then + error(msprintf("Error: The input argument is not of type imageSet, Count field must be integer")); + end + if type(imageSet.ImageLocation)<> 10 & size(imageSet.ImageLocation,'c') <> 1 then + error(msprintf("Error: The input argument is not of type imageSet, ImageLocation must be strings")); + end + locationList=list(); + Desc=imageSet.Description; + Count=int32(imageSet.Count); + for j=1:imageSet.Count + tempMat(1,j)=imageSet.ImageLocation(j); + end + locationList($+1)=tempMat; + else + for i=1:size(imageSet.Count) + if type(imageSet(i).Description)<> 10 | size(imageSet(i).Description) <> [1 1] then + error(msprintf("Error: The input argument is not of type imageSet, Description field must be string")); + end + if type(imageSet(i).Count)<> 1 | size(imageSet(i).Count) <> [1 1] then + error(msprintf("Error: The input argument is not of type imageSet, Count field must be integer")); + end + if type(imageSet(i).ImageLocation)<> 10 & size(imageSet(i).ImageLocation,'c') <> 1 then + error(msprintf("Error: The input argument is not of type imageSet, ImageLocation must be strings")); + end + end + locationList=list(); + for i=1:size(imageSet.Count) + Desc(i)=imageSet(i).Description; + Count(i)=int32(imageSet(i).Count); + for j=1:imageSet(i).Count + tempMat(1,j)=imageSet(i).ImageLocation(j); + end + locationList($+1)=tempMat; + end + end + imgSetList=list("imageSet",Desc,Count,locationList); +endfunction diff --git a/macros/imgaborfit.bin b/macros/imgaborfit.bin new file mode 100644 index 0000000..0061f67 Binary files /dev/null and b/macros/imgaborfit.bin differ diff --git a/macros/imgaborfit.sci b/macros/imgaborfit.sci new file mode 100644 index 0000000..3adfbf8 --- /dev/null +++ b/macros/imgaborfit.sci @@ -0,0 +1,13 @@ +function new_image = imgaborfit(image, wavelength, orientation) + + image_list = mattolist(image) + + out = opencv_imgaborfit(image_list, wavelength, orientation) + + sz = size(out) + + for i=1:sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/imgaussfilt3.bin b/macros/imgaussfilt3.bin new file mode 100644 index 0000000..b21c402 Binary files /dev/null and b/macros/imgaussfilt3.bin differ diff --git a/macros/imgaussfilt3.sci b/macros/imgaussfilt3.sci new file mode 100644 index 0000000..7995f8e --- /dev/null +++ b/macros/imgaussfilt3.sci @@ -0,0 +1,7 @@ +function [outputImg]=imgaussfilt3(inputImage, filter_height, filter_width, sigmaX, sigmaY) + inputList=mattolist(inputImage); + outputList=opencv_imgaussfilt3(inputList, filter_height, filter_width, sigmaX, sigmaY); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/imguidedfilter.bin b/macros/imguidedfilter.bin new file mode 100644 index 0000000..43e2bd6 Binary files /dev/null and b/macros/imguidedfilter.bin differ diff --git a/macros/imguidedfilter.sci b/macros/imguidedfilter.sci new file mode 100644 index 0000000..13f2236 --- /dev/null +++ b/macros/imguidedfilter.sci @@ -0,0 +1,26 @@ +function guidedFiltered_image = imguidedfilter(varargin) + + [lhs, rhs] = argn(0) + + image_list = mattolist(varargin(1)) + + select rhs + case 1 + out = opencv_imguidedfilter(image_list) + + case 2 + out = opencv_imguidedfilter(image_list, varargin(2)) + + case 3 + out = opencv_imguidedfilter(image_list, varargin(2), varargin(3)) + + case 4 + out = opencv_imguidedfilter(image_list, varargin(2), varargin(3), varargin(4)) + end + + sz = size(out) + for i=1:sz + guidedFiltered_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/imhistmatch.bin b/macros/imhistmatch.bin new file mode 100644 index 0000000..5deb479 Binary files /dev/null and b/macros/imhistmatch.bin differ diff --git a/macros/imhistmatch.sci b/macros/imhistmatch.sci new file mode 100644 index 0000000..0792aef --- /dev/null +++ b/macros/imhistmatch.sci @@ -0,0 +1,17 @@ +function [outputImg]=imhistmatch(inputImage,refImage,varargin) + [lhs rhs]=argn(0); + if rhs>3 + error(msprintf(" Too many input arguments")); + end + inputList=mattolist(inputImage); + refList=mattolist(refImage); + select rhs + case 2 then + outputList=opencv_imhistmatch(inputList,refList); + case 3 then + outputList=opencv_imhistmatch(inputList,refList,varargin(1)); + end + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/imhmin.bin b/macros/imhmin.bin new file mode 100644 index 0000000..6d6bcf0 Binary files /dev/null and b/macros/imhmin.bin differ diff --git a/macros/imhmin.sci b/macros/imhmin.sci new file mode 100644 index 0000000..441efce --- /dev/null +++ b/macros/imhmin.sci @@ -0,0 +1,15 @@ +function[dstMat] = imfill(srcImg, Hmin) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + out = opencv_imfill(srcMat, Hmin) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imhmin.sci~ b/macros/imhmin.sci~ new file mode 100644 index 0000000..d57e774 --- /dev/null +++ b/macros/imhmin.sci~ @@ -0,0 +1,15 @@ +unction[dstMat] = imfill(srcImg, H-min) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + out = opencv_imfill(srcMat, H-min) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imimposemin.bin b/macros/imimposemin.bin new file mode 100644 index 0000000..9fd4151 Binary files /dev/null and b/macros/imimposemin.bin differ diff --git a/macros/imimposemin.sci b/macros/imimposemin.sci new file mode 100644 index 0000000..1d0d582 --- /dev/null +++ b/macros/imimposemin.sci @@ -0,0 +1,11 @@ +function[dstMat] = imimposemin(maskImg, markerImg) + + out = opencv_imimposemin(maskImg, markerImg) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/immultiply.bin b/macros/immultiply.bin new file mode 100644 index 0000000..451d045 Binary files /dev/null and b/macros/immultiply.bin differ diff --git a/macros/immultiply.sci b/macros/immultiply.sci new file mode 100644 index 0000000..542d9d5 --- /dev/null +++ b/macros/immultiply.sci @@ -0,0 +1,10 @@ +function[multiplyMat] = immultiply(matA, matB) + + out = opencv_immultiplyt(matA, matB) + + channel = size(out) + + for i = 1: channel + multiplyMat(:,:,i) = out(i) + end +endfunction diff --git a/macros/impixel.bin b/macros/impixel.bin new file mode 100644 index 0000000..7bf530d Binary files /dev/null and b/macros/impixel.bin differ diff --git a/macros/impixel.sci b/macros/impixel.sci new file mode 100644 index 0000000..64afe5f --- /dev/null +++ b/macros/impixel.sci @@ -0,0 +1,5 @@ +function [out]=impixel(image,value1,value2) + image1=mattolist(image); + out=opencv_impixel(image1,value1,value2); + +endfunction; diff --git a/macros/imread.bin b/macros/imread.bin new file mode 100644 index 0000000..2394659 Binary files /dev/null and b/macros/imread.bin differ diff --git a/macros/imread.sci b/macros/imread.sci new file mode 100644 index 0000000..7770a58 --- /dev/null +++ b/macros/imread.sci @@ -0,0 +1,11 @@ +function[dstMat] = imread(matA) + + out = opencv_imread(matA) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = (out(i)) + end + dstMat=double(dstMat) +endfunction diff --git a/macros/imread.sci~ b/macros/imread.sci~ new file mode 100644 index 0000000..bfc3aae --- /dev/null +++ b/macros/imread.sci~ @@ -0,0 +1,11 @@ +function[dstMat] = imread(matA) + + out = opencv_imread(matA) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = double(out(i)) + end + +endfunction diff --git a/macros/imrect.bin b/macros/imrect.bin new file mode 100644 index 0000000..d02e8cd Binary files /dev/null and b/macros/imrect.bin differ diff --git a/macros/imrect.sci b/macros/imrect.sci new file mode 100644 index 0000000..a1ed7fa --- /dev/null +++ b/macros/imrect.sci @@ -0,0 +1,8 @@ +function [output] = imrect(img,pstDataX,pstDataY) + image = mattolist(img); + a = opencv_imrect(image,pstDataX,pstDataY) + d = size(a); + for i=1:size + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/imresize.bin b/macros/imresize.bin new file mode 100644 index 0000000..18dcdb9 Binary files /dev/null and b/macros/imresize.bin differ diff --git a/macros/imresize.sci b/macros/imresize.sci new file mode 100644 index 0000000..6570005 --- /dev/null +++ b/macros/imresize.sci @@ -0,0 +1,8 @@ +function [out]=imresize(image,alpha,beta) + image1=mattolist(image); + a=opencv_imresize(image1,alpha,beta); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end +endfunction; diff --git a/macros/imsharpen.bin b/macros/imsharpen.bin new file mode 100644 index 0000000..5f5f066 Binary files /dev/null and b/macros/imsharpen.bin differ diff --git a/macros/imsharpen.sci b/macros/imsharpen.sci new file mode 100644 index 0000000..4c4a4be --- /dev/null +++ b/macros/imsharpen.sci @@ -0,0 +1,18 @@ +function[dstMat] = imsharpen(SrcImg) + + srcMat = mattolist(SrcImg) +// [rows cols channels]=size(SrcImg); +// imgList=list(); +// for i=1:channels +// imgList(i)=SrcImg(:,:,i); +// end +// disp(imgList(i)) +// out = opencv_imsharpen(imgList) + out = opencv_imsharpen(srcMat) + [rows cols channel] = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imsharpen.sci~ b/macros/imsharpen.sci~ new file mode 100644 index 0000000..4c4a4be --- /dev/null +++ b/macros/imsharpen.sci~ @@ -0,0 +1,18 @@ +function[dstMat] = imsharpen(SrcImg) + + srcMat = mattolist(SrcImg) +// [rows cols channels]=size(SrcImg); +// imgList=list(); +// for i=1:channels +// imgList(i)=SrcImg(:,:,i); +// end +// disp(imgList(i)) +// out = opencv_imsharpen(imgList) + out = opencv_imsharpen(srcMat) + [rows cols channel] = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imshow.bin b/macros/imshow.bin new file mode 100644 index 0000000..fd6d82d Binary files /dev/null and b/macros/imshow.bin differ diff --git a/macros/imshow.sci b/macros/imshow.sci new file mode 100644 index 0000000..ceedd8e --- /dev/null +++ b/macros/imshow.sci @@ -0,0 +1,26 @@ +function[] =imshow(Image) //retrieving list and creating 3 dimensional matrix out of it + dimensions=size(Image) + if dimensions==3 then + [c d]=size(Image(1)); + r=matrix(Image(1),c,d); + g=matrix(Image(2),c,d); + b=matrix(Image(3),c,d); + z(:,:,1)=r; + z(:,:,2)=g; + z(:,:,3)=b; + [NumberOfRows NumberOfColumns NumberOfChannels] = size(z); + NumberOfPixels = NumberOfRows * NumberOfColumns; + MaxGrayValue = 2 ^ 8 - 1; + ColorMap = double(matrix(z, NumberOfPixels, NumberOfChannels)) ... + / MaxGrayValue; + Img = matrix(1 : NumberOfPixels, NumberOfRows, NumberOfColumns); + elseif dimensions==1 then + [c d]=size(Image(1)); + Img=matrix(Image(1),c,d); + //Img=Img'; + MaxUInt8 = 2 ^ 8 - 1; + MaximumGrayValue = MaxUInt8; + ColorMap = graycolormap(double(MaximumGrayValue + 1)); + end; + show(Img,ColorMap); +endfunction diff --git a/macros/imshowpair.bin b/macros/imshowpair.bin new file mode 100644 index 0000000..39a70bd Binary files /dev/null and b/macros/imshowpair.bin differ diff --git a/macros/imshowpair.sci b/macros/imshowpair.sci new file mode 100644 index 0000000..0df086a --- /dev/null +++ b/macros/imshowpair.sci @@ -0,0 +1,8 @@ +function [outputImg]=imshowpair(inputImage1,inputImage2,method,scale) + inputList1=mattolist(inputImage1); + inputList2=mattolist(inputImage2); + outputList=opencv_imshowpair(inputList1,inputList2,method,scale); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/imsubtract.bin b/macros/imsubtract.bin new file mode 100644 index 0000000..22eb98f Binary files /dev/null and b/macros/imsubtract.bin differ diff --git a/macros/imsubtract.sci b/macros/imsubtract.sci new file mode 100644 index 0000000..2edaa1f --- /dev/null +++ b/macros/imsubtract.sci @@ -0,0 +1,5 @@ +function [matC] = imsubtract(matA, matB) + + matC = opencv_imsubtract(matA, matB) + +endfunction diff --git a/macros/imwarp.bin b/macros/imwarp.bin new file mode 100644 index 0000000..92da630 Binary files /dev/null and b/macros/imwarp.bin differ diff --git a/macros/imwarp.sci b/macros/imwarp.sci new file mode 100644 index 0000000..a920ffa --- /dev/null +++ b/macros/imwarp.sci @@ -0,0 +1,8 @@ +function [output] = imwarp(img,matrix) + image = mattolist(img); + a = imwarp(image,matrix) + d = size(a) + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/imwrite.bin b/macros/imwrite.bin new file mode 100644 index 0000000..2315134 Binary files /dev/null and b/macros/imwrite.bin differ diff --git a/macros/imwrite.sci b/macros/imwrite.sci new file mode 100644 index 0000000..24e9100 --- /dev/null +++ b/macros/imwrite.sci @@ -0,0 +1,4 @@ +function imwrite(img,pstData) + image = mattolist(img) + opencv_imwrite(image,pstData) +endfunction diff --git a/macros/ind2gray.bin b/macros/ind2gray.bin new file mode 100644 index 0000000..36816bd Binary files /dev/null and b/macros/ind2gray.bin differ diff --git a/macros/ind2gray.sci b/macros/ind2gray.sci new file mode 100644 index 0000000..d2c8eee --- /dev/null +++ b/macros/ind2gray.sci @@ -0,0 +1,8 @@ +function [output] = ind2gray(img,colormap) + image = mattolist(img); + a = opencv_ind2gray(image,colormap) + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/indexImages.bin b/macros/indexImages.bin new file mode 100644 index 0000000..c1622de Binary files /dev/null and b/macros/indexImages.bin differ diff --git a/macros/indexImages.sci b/macros/indexImages.sci new file mode 100644 index 0000000..8a6dc8c --- /dev/null +++ b/macros/indexImages.sci @@ -0,0 +1,54 @@ +function [imageIndex]=indexImages(imgSet,varargin) +// This function creates image search index +// +// Calling Sequence +// imageIndex = indexImages(imgSet, bagOfFeatures, Name, Value... ); +// +// Parameters +// imgSet: Collection of images with their location and labels +// bagOfFeatures: Bag of Visual Words +// Verbose [Optional Input Argument]: Enable Progress display to Screen +// SaveFeatureLocations [Optional Input Argument]: A boolean value indicating whether feature locations has to be saved or not. Values: [True(default)|False] +// imageIndex: Search Index Object containing the mapping of visual vocabulary to each image in the image set +// +// Description +// Creates a search Index Object that has been mapped to visual words that can be used for retrieve Images +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// imageindex = indexImages(trainingSet, bag); +// +// With Optional Arguments: +// imageindex = indexImages(trainingSet, bag, "Verbose", 1); +// +// Authors +// Umang Agrawal +// Rohit Suri +// Sridhar Reddy + + [lhs rhs]=argn(0); + if rhs>6 + error(msprintf(" Too many input arguments")); + end + imageSetList=imageSetStructToList(imageSet); + if modulo(lhs,2)==0 + bagList=bagStructToList(varargin(1)); + select rhs + case 1 then + [imageIndexList]=opencv_indexImages(imgSetList); + case 2 then + [imageIndexList]=opencv_indexImages(imgSetList,bagList); + case 3 then + [imageIndexList]=opencv_indexImages(imgSetList,varargin(1),varargin(2)); + case 4 then + [imageIndexList]=opencv_indexImages(imgSetList,bagList,varargin(2),varargin(3)); + case 5 then + [imageIndexList]=opencv_indexImages(imgSetList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [imageIndexList]=opencv_indexImages(imgSetList,bagList,varargin(2),varargin(3),varargin(4),varargin(5)); + end + end + +endfunction diff --git a/macros/indexImages.sci~ b/macros/indexImages.sci~ new file mode 100644 index 0000000..567a90d --- /dev/null +++ b/macros/indexImages.sci~ @@ -0,0 +1,53 @@ +function [imageIndex]=indexImages(imgSet,varargin) +// This function creates image search index +// +// Calling Sequence +// imageIndex = indexImages(imgSet, bagOfFeatures, Name, Value... ); +// +// Parameters +// imgSet: Collection of images with their location and labels +// bagOfFeatures: Bag of Visual Words +// Verbose [Optional Input Argument]: Enable Progress display to Screen +// SaveFeatureLocations [Optional Input Argument]: A boolean value indicating whether feature locations has to be saved or not. Values: [True(default)|False] +// imageIndex: Search Index Object containing the mapping of visual vocabulary to each image in the image set +// +// Description +// Creates a search Index Object that has been mapped to visual words that can be used for retrieve Images +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// imageindex = indexImages(trainingSet, bag); +// +// With Optional Arguments: +// imageindex = indexImages(trainingSet, bag, "Verbose", 1); +// +// Authors +// Umang Agrawal +// Rohit Suri +// Sridhar Reddy + + [lhs rhs]=argn(0); + if rhs>6 + error(msprintf(" Too many input arguments")); + end + imageSetList=imageSetStructToList(imageSet); + if modulo(lhs,2)==0 + bagList=bagStructToList(varargin(1)); + select rhs + case 1 then + [imageIndexList]=opencv_indexImages(imgSetList); + case 2 then + [imageIndexList]=opencv_indexImages(imgSetList,bagList); + case 3 then + [imageIndexList]=opencv_indexImages(imgSetList,varargin(1),varargin(2)); + case 4 then + [imageIndexList]=opencv_indexImages(imgSetList,bagList,varargin(2),varargin(3)); + case 5 then + [imageIndexList]=opencv_indexImages(imgSetList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [imageIndexList]=opencv_indexImages(imgSetList,bagList,varargin(2),varargin(3),varargin(4),varargin(5)); + end + +endfunction diff --git a/macros/integralFilter.bin b/macros/integralFilter.bin new file mode 100644 index 0000000..e382416 Binary files /dev/null and b/macros/integralFilter.bin differ diff --git a/macros/integralFilter.sci b/macros/integralFilter.sci new file mode 100644 index 0000000..1e7e19f --- /dev/null +++ b/macros/integralFilter.sci @@ -0,0 +1,29 @@ +function [out]=integralFilter(intimage,bbox,weights,filterSize) +// Integral Image based Filter. +// +// Calling Sequence +// filter = integralFilter(intimage,bbox,weights,filterSize); +// +// Parameters +// intimage: Integral Image, which can be obtained from the function integralImage. +// bbox: Bounding box of the filter object, which can be obtained from integralKernel function. +// weight: Weights of the bounding box, which can be obtained from integralKernel function. +// filterSize: Size of the filter, which can be obtained from integralKernel function. +// +// Description +// This function filters image using box filters and integral images. +// +// Examples +// i = imread("lena.jpg"); +// intImg = integralImage(i); +// kernel = integralKernel([2 2 11 11], 1/51); +// filter = integralKernel(intImg, kernel.bbox, kernel.weights, kernel.filterSize); +// +// Authors +// Tanmay Chaudhari + + inputimage1=mattolist(inputimage); + a=opencv_integralFilter(inputimage1,bbox,weights,filterSize); + out(:,:,1)=a(1); + +endfunction diff --git a/macros/integralImage.bin b/macros/integralImage.bin new file mode 100644 index 0000000..38b5e6c Binary files /dev/null and b/macros/integralImage.bin differ diff --git a/macros/integralImage.sci b/macros/integralImage.sci new file mode 100644 index 0000000..00801cb --- /dev/null +++ b/macros/integralImage.sci @@ -0,0 +1,20 @@ +function[dstMat] = integralImage(srcImg, varargin) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + select rhs + case 1 then + out = opencv_integralImage(srcMat) + case 3 then + out = opencv_integralImage(srcMat, varargin(1)) + end + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/integralImage.sci~ b/macros/integralImage.sci~ new file mode 100644 index 0000000..cc528a0 --- /dev/null +++ b/macros/integralImage.sci~ @@ -0,0 +1,20 @@ +unction[dstMat] = integralImage(srcImg, varargin) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + select rhs + case 1 then + out = opencv_integralImage(srcMat) + case 3 then + out = opencv_integralImage(srcMat, varargin(1)) + end + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/integralKernel.bin b/macros/integralKernel.bin new file mode 100644 index 0000000..e1ba577 Binary files /dev/null and b/macros/integralKernel.bin differ diff --git a/macros/integralKernel.sci b/macros/integralKernel.sci new file mode 100644 index 0000000..7276e27 --- /dev/null +++ b/macros/integralKernel.sci @@ -0,0 +1,27 @@ +function [ kernel ] = integralKernel(bbox, weights) +// This function creates a filter to be used with integral images. +// +// Calling Sequence +// results = intergralKernel(bbox, weight); +// +// Parameters +// results: A structure kernel which contains bounding boxes, weights, filter coefficients, center of filter, size of filter, orientation of filter +// bboxes: bounding boxes that define the filter +// weights: weights corresponding to each of the bbox +// +// Description +// Integral kernel creates box filter which can be used with integral images. +// +// Examples +// bbox = [1 2 3 4; 5 6 7 8]; +// weights = [10 11]; +// results = integralKernel(bbox, weights); +// +// Authors +// Tanmay Chaudhari + + [t1 t2 t3 t4 t5] = opencv_integralKernel(bbox, weights) + + kernel = struct("bbox",t1(1),"weights",t2,"coefficients",t3(1),"centre",t4(1),"filterSize",t5) + +endfunction diff --git a/macros/invertedIndexImages.bin b/macros/invertedIndexImages.bin new file mode 100644 index 0000000..14a3ee8 Binary files /dev/null and b/macros/invertedIndexImages.bin differ diff --git a/macros/invertedIndexImages.sci b/macros/invertedIndexImages.sci new file mode 100644 index 0000000..a92b728 --- /dev/null +++ b/macros/invertedIndexImages.sci @@ -0,0 +1,32 @@ +function [invertedImageIndexStruct]=invertedIndexImages(imgSet,bagOfFeatures) +// This function creates image search index +// +// Calling Sequence +// imageIndex = invertedIndexImages(imgSet, bagOfFeatures ); +// +// Parameters +// imgSet: Collection of images with their location and labels +// bagOfFeatures: Bag of Visual Words +// SaveFeatureLocations [Optional Input Argument]: A boolean value inicating whether feature locations has to be saved or not. Values: [True(default)|False] +// imageIndex: Search Index Object containing the mapping of visual vocabulary to each image in the image set +// +// Description +// Creates a search Index Object that has been mapped to visual words that can be used for retrieve Images +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// imageindex = invertedIndexImages(trainingSet, bag); +// +// With Optional Arguments: +// imageindex = invertedIndexImages(trainingSet, bag, "SaveFeatureLocations", 1); +// +// Authors +// Umang Agrawal +// Rohit Suri +// Sridhar Reddy + + invertedImageIndexList=indexImages(imgSet,bagOfFeatures); + invertedImageIndexStruct=struct('Type','invertedImageIndex','ImageLocation',invertedImageIndexList(5), 'ImageWords',invertedImageIndexList(2), 'WordFrequency',invertedImageIndexList(3),'BagOfFeatures',bagOfFeatures,) +endfunction diff --git a/macros/isEpipoleInImage.bin b/macros/isEpipoleInImage.bin new file mode 100644 index 0000000..d9a3155 Binary files /dev/null and b/macros/isEpipoleInImage.bin differ diff --git a/macros/isEpipoleInImage.sci b/macros/isEpipoleInImage.sci new file mode 100644 index 0000000..b2d48d0 --- /dev/null +++ b/macros/isEpipoleInImage.sci @@ -0,0 +1,48 @@ +function [isepi, varargout ] = isEpipoleInImage(fundamental_matrix, imagesize) +// Find whether image contains epipole. +// +// Calling Sequence +// isepi = isEpipoleInImage(F, imagesize) +// [isepi, epipole] = isEpipoleInImage(F, imagesize) +// +// Parameters +// F : A 3 * 3 fundamental matrix computed from stereo images. It should be double or single +// imagesize : The size of the image +// isepi : Logical value true / false denoting whether the image contains epipole +// epipole : Location of the epipole. It is 1 * 2 vector. +// +// Description +// The function determines whether the image with fundamental matrix F contains the epipole or not. It also gives the position of the epipole. +// +// Examples +// F = estimateFundamentalMatrix(matchedPoints1, matchedPoints2) +// imageSize = [200 300] +// isepi = isEpipoleInImage(F, imagesize) +// [isepi, epipole] = isEpipoleInImage(F, imagesize) +// +// Authors +// Suraj Prakash + + [ lhs, rhs ] = argn(0) + + if lhs > 2 then + error(msprintf("Too many output arguments")); + end + /// If there is more than one output parameter + [rows cols] = size(fundamental_matrix) + if rows ~= 3 | cols ~=3 then + error(msprintf("Invalid size of fundamental matrix\n")); + end + [rows1 col2] = size(imagesize) + if rows1 ~=1 | cols ~= 2 then + error(msprintf("Invalid image size matrix\n")); + end + if lhs == 2 then + [isepi, temp ] = opencv_isEpipoleInImage(fundamental_matrix, imagesize); + varargout(1) = temp; + /// if there is only one output parameter + else + isepi = opencv_isEpipoleInImage(fundamental_matrix, imagesize); + end + +endfunction diff --git a/macros/lab2double.bin b/macros/lab2double.bin new file mode 100644 index 0000000..7b4d0db Binary files /dev/null and b/macros/lab2double.bin differ diff --git a/macros/lab2double.sci b/macros/lab2double.sci new file mode 100644 index 0000000..83203a3 --- /dev/null +++ b/macros/lab2double.sci @@ -0,0 +1,7 @@ +function [output] = lab2double(pstData) + a = opencv_lab2double(pstData); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/lab2rgb.bin b/macros/lab2rgb.bin new file mode 100644 index 0000000..a837573 Binary files /dev/null and b/macros/lab2rgb.bin differ diff --git a/macros/lab2rgb.sci b/macros/lab2rgb.sci new file mode 100644 index 0000000..622d442 --- /dev/null +++ b/macros/lab2rgb.sci @@ -0,0 +1,7 @@ +function [output] = lab2rgb(pstData) + a = opencv_lab2rgb(pstData); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/lab2uint16.bin b/macros/lab2uint16.bin new file mode 100644 index 0000000..04b6f9a Binary files /dev/null and b/macros/lab2uint16.bin differ diff --git a/macros/lab2uint16.sci b/macros/lab2uint16.sci new file mode 100644 index 0000000..14cb352 --- /dev/null +++ b/macros/lab2uint16.sci @@ -0,0 +1,7 @@ +function [output] = lab2uint16(pstData) + a = opencv_lab2uint16(pstData); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/lab2uint8.bin b/macros/lab2uint8.bin new file mode 100644 index 0000000..300e6b0 Binary files /dev/null and b/macros/lab2uint8.bin differ diff --git a/macros/lab2uint8.sci b/macros/lab2uint8.sci new file mode 100644 index 0000000..d0408b5 --- /dev/null +++ b/macros/lab2uint8.sci @@ -0,0 +1,7 @@ +function [output] = lab2uint8(pstData) + a = opencv_lab2uint8(pstData); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/lab2xyz.bin b/macros/lab2xyz.bin new file mode 100644 index 0000000..b5a6e73 Binary files /dev/null and b/macros/lab2xyz.bin differ diff --git a/macros/lab2xyz.sci b/macros/lab2xyz.sci new file mode 100644 index 0000000..ae2cd7b --- /dev/null +++ b/macros/lab2xyz.sci @@ -0,0 +1,20 @@ +function [output] = lab2xyz(vartype,varargin) + [lhs rhs] = argn(0); + if(rhs>3) + eror(msprintf("Too many input arguments")); + end + select rhs + case 1 then + a = opencv_lab2xyz(vartype); + case 2 then + a = opencv_lab2xyz(vartype,varargin(0)); + case 3 then + a = opencv_lab2xyz(vartype,varargin(0),varargin(1)); + end + + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction + diff --git a/macros/laplacian.bin b/macros/laplacian.bin new file mode 100644 index 0000000..62a2bb8 Binary files /dev/null and b/macros/laplacian.bin differ diff --git a/macros/laplacian.sci b/macros/laplacian.sci new file mode 100644 index 0000000..5cd9b04 --- /dev/null +++ b/macros/laplacian.sci @@ -0,0 +1,7 @@ +function outputImg= laplacian(inputImage , depth , ksize , scale, delta) + inputList=mattolist(inputImage); + outputList=opencv_laplacian(inputList , depth , ksize , scale, delta) + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/lib b/macros/lib new file mode 100644 index 0000000..29fe244 Binary files /dev/null and b/macros/lib differ diff --git a/macros/line.bin b/macros/line.bin new file mode 100644 index 0000000..cb709f1 Binary files /dev/null and b/macros/line.bin differ diff --git a/macros/line.sci b/macros/line.sci new file mode 100644 index 0000000..df1d19f --- /dev/null +++ b/macros/line.sci @@ -0,0 +1,7 @@ +function outputImg= line(inputImage , x1 , y1 , x2,y2,r_value,g_value,b_value,thickness,linetype,shift) + inputList=mattolist(inputImage); + outputList=opencv_line(inputList , x1 , y1 , x2,y2,r_value,g_value,b_value,thickness,linetype,shift) + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/lineToBorderPoints.bin b/macros/lineToBorderPoints.bin new file mode 100644 index 0000000..25b050b Binary files /dev/null and b/macros/lineToBorderPoints.bin differ diff --git a/macros/lineToBorderPoints.sci b/macros/lineToBorderPoints.sci new file mode 100644 index 0000000..eab84ef --- /dev/null +++ b/macros/lineToBorderPoints.sci @@ -0,0 +1,29 @@ +function [points] = lineToBorderPoints(lines,isize) + +// Points of intersection of line(s) with the image border +// +// Calling Sequence +// points = lineToBorderPoints(lines,isize) +// +// Parameters +// lines : It is a MX3 matrix. If a line is represented by A*x + B*y + C = 0, then each row is of the form [A B C] and M is the number of lines. +// isize : It is the size of the image. It is of the form as that returned by the function size. +// points : It is a MX4 matrix. It returns the points of intersection of the line with the image border. Each row is of the form [x1,y1,x2,y2] where (x1,y1) and (x2,y2) are the two points of intersection. If a given line does not intersect the image border, the function returns [-1 -1 -1 -1]. +// +// Description +// The function calculates the points of intersection of one or more lines with the image border. +// +// Examples +// // Load an image +// I = imread('rice.png'); +// // Define a line : 2*x + y = 300 +// line = [2 1 -300]; +// // Calculate the points of intersection +// points = lineToBorderPoints(line,size(I(1))) +// +// Authors +// Asmita Bhar +// + + points = opencv_lineToBorderPoints(lines,isize) +endfunction diff --git a/macros/localMaximaFinder.bin b/macros/localMaximaFinder.bin new file mode 100644 index 0000000..7fff1fd Binary files /dev/null and b/macros/localMaximaFinder.bin differ diff --git a/macros/localMaximaFinder.sci b/macros/localMaximaFinder.sci new file mode 100644 index 0000000..45e0806 --- /dev/null +++ b/macros/localMaximaFinder.sci @@ -0,0 +1,118 @@ +function [location]=localMaximaFinder(inputMatrix,neighborhood,maxNoOfMaxima,threshold) +// Returns local maxima in input matrices +// +// Calling Sequence +// location=localMaximaFinder(inputMatrix,neighborhood,maxNoOfMaxima,threshold) +// +// Parameters +// inputMatrix: 2-D input matrix +// neighborhood: It specifies the neighborhood around maxima, function zeros out the values inside the block. Neighborhood is a two element vector,first one indicates number of rows in neighborhood,second one indicates columns, of odd intergers. +// maxNoOfMaxima: maximum number of local maxima you want to find. +// threshold: It specifies minimum value of the local maxima. +// +// Description +// localMaximaFinder returns the M-by-2 matrix where M is the number of local maxima and each row represents one local maximum +// +// Examples +// I=imread("lena.jpeg"); +// grayImage=rgb2gray(I); +// location=localMaximaFinder(grayImage); +// mat=[1 6 3;4 8 2;9 7 5]; +// loc=localMaximaFinder(mat,[1 3],4,3); + + [lhs,rhs]=argn(0) + if rhs<4 then + error(msprintf(" Not enough input arguments")) + elseif rhs>4 then + error(msprintf(" Too many input arguments to the function")) + elseif lhs>1 then + error(msprintf(" Too many output arguments")) + end + + [iRows iCols]=size(inputMatrix) + [nRows nCols]=size(neighborhood) + if ~isreal(inputMatrix) | ~isreal(neighborhood) + error(msprintf(" Wrong input argument,complex matrix is not expected")) + elseif ~nRows==1 | ~nCols==2 + error(msprintf("Neighborhood property must be a vector with 2 columns")) + end + + nh_x=neighborhood(1,1); + nh_y=neighborhood(1,2); + if modulo(nh_x,2)==0 | modulo(nh_y,2)==0 + error(msprintf("-Neighborhood property must be odd")) + elseif ~nh_x<=iRows | ~nh_y<=iCols + error(msprintf("Each dimension of the NeighborhoodSize property value must be less than or equal to the corresponding dimension of the input matrix")) + end + + nh_x=floor(nh_x/2); + nh_y=floor(nh_y/2); + count=0; + index=1; + k=1; + while countmax_data + max_data=inputMatrix(i,j); + max_xIn=i; + max_yIn=j; + end + end + end + if threshold<=max_data + location(k,1)=max_yIn; + location(k,2)=max_xIn; + k=k+1; + + //to zero-out the values around the neighborhood + if max_xIn-nh_x<1 then + start_x=1 + else + start_x=max_xIn-nh_x + end + if max_yIn-nh_y<1 then + start_y=1 + else + start_y=max_yIn-nh_y + end + if max_xIn+nh_x>iRows then + end_x=iRows + else + end_x=max_xIn+nh_x + end + if max_yIn+nh_y>iCols then + end_y=iCols + else + end_y=max_yIn+nh_y + end + for i=start_x:end_x + for j=start_y:end_y + inputMatrix(i,j)=0; + end + end + //to check the existence of non-zero element + nonZeroCount=0; + for i=1:iRows + for j=1:iCols + if ~inputMatrix(i,j)==0 then + nonZeroCount=nonZeroCount+1; + break; + end + end + if nonZeroCount==1 then + break; + end + end + if nonZeroCount==0 then + break; + end + count=count+1; + + else + break; //if maximum number in input matrix is less than threshold + end + end +endfunction diff --git a/macros/makecform.bin b/macros/makecform.bin new file mode 100644 index 0000000..d12a095 Binary files /dev/null and b/macros/makecform.bin differ diff --git a/macros/makecform.sci b/macros/makecform.sci new file mode 100644 index 0000000..ac51464 --- /dev/null +++ b/macros/makecform.sci @@ -0,0 +1,4 @@ +function[out_string] = makecform(srcImg) + srcMat = mattolist(srcImg) + out_string = opencv_makecform(srcMat) +endfunction diff --git a/macros/matchFeatures.bin b/macros/matchFeatures.bin new file mode 100644 index 0000000..c137e32 Binary files /dev/null and b/macros/matchFeatures.bin differ diff --git a/macros/matchFeatures.sci b/macros/matchFeatures.sci new file mode 100644 index 0000000..649e68a --- /dev/null +++ b/macros/matchFeatures.sci @@ -0,0 +1,62 @@ +function [indexPairs,varargout]=matchFeatures(features1,features2,varargin) +// This function is used to find the corresponding matches between two features sets +// +// Calling Sequence +// [ indexPairs ] = matchFeatures( features1, features2, Name, Value... ) +// [ indexPairs matchMetric ] = matchFeatures( features1, features2, Name, Value... ) +// +// Parameters +// features1: Feature Set 1, a set of M1-by-N Matrix, M1 corresponding to number of features and N corresponds to length of each feature vector +// features2: Feature Set 2, a set of M2-by-N Matrix, M2 corresponding to number of features and N corresponds to length of each feature vector +// Method [Optional Input Argument]: Method of matching to be used. Values: ['Exhaustive' (default) | 'Approximate'] +// MatchThreshold [Optional Input Argument]: Matching Threshold for selecting the percentage of strongest matches. Values in range [0 100], default - 10.0 +// Unique [Optional Input Argument]: Boolean value for selecting only unique matches between features. Default-False(0) +// Metric [Optional Input Argument]: Metric to be used for matching features. Values: ['SSD'(default)|'SAD'|'Hamming'|'Hamming_2'] +// indexPairs: P-by-2 matrix containing the indices of corresponding features matched between two input feature sets +// matchMetric: P-by-1 Vector containing the distance metric between matched Features +// +// Description +// MatchFeatures function takes in the Feature Descriptors value of two images as its input and finds the best match between each feature vector of the first image to that of the second image and returns the corresponding indices of each feature matrix +// +// Examples +// image_1 = imread('sample1.jpg'); +// image_2 = imread('sample2.jpg'); +// points_1 = detectFASTFeatures(image_1); +// points_2 = detectFASTFeatures(image_2); +// [ f1 vpts_1 ] = extractFeatures(image_1, points_1); +// [ f2 vpts_2 ] = extractFeatures(image_2, points_2); +// indexPairs = matchFeatures(f1, f2); +// matchedPoints_1 = vpts_1(indexPairs(:,1)); +// matchedPoints_2 = vpts_2(indexPairs(:,2)); +// +// With Optional Arguments: +// [indexPairs matchMetric] = matchFeatures(f1, f2, "Metric", "SAD"); +// +// Authors +// Umang Agrawal +// Sridhar Reddy + + [lhs rhs]=argn(0); + if lhs>2 + error(msprintf(" Too many output arguments")); + elseif rhs>10 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs,2)<>0 + error(msprintf("Either Argument Name or its Value missing")); + end + select rhs-2 + case 0 then + [indexPairs, matchmetric]=opencv_matchFeatures(features1,features2); + case 2 then + [indexPairs, matchmetric]=opencv_matchFeatures(features1,features2,varargin(1),varargin(2)); + case 4 then + [indexPairs, matchmetric]=opencv_matchFeatures(features1,features2,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [indexPairs, matchmetric]=opencv_matchFeatures(features1,features2,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + case 8 then + [indexPairs, matchmetric]=opencv_matchFeatures(features1,features2,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8)); + end + if lhs==2 then + varargout(1)=matchmetric; + end +endfunction diff --git a/macros/mattolist.bin b/macros/mattolist.bin new file mode 100644 index 0000000..cc09fd5 Binary files /dev/null and b/macros/mattolist.bin differ diff --git a/macros/mattolist.sci b/macros/mattolist.sci new file mode 100644 index 0000000..8795ebd --- /dev/null +++ b/macros/mattolist.sci @@ -0,0 +1,9 @@ +function [imgList]=mattolist(imgMat) + [rows cols channels]=size(imgMat); + imgList=list(); + for i=1:channels + imgList($+1)=imgMat(:,:,i); +// disp(imgList(i)) + end + +endfunction diff --git a/macros/mattolist.sci~ b/macros/mattolist.sci~ new file mode 100644 index 0000000..77a6b2c --- /dev/null +++ b/macros/mattolist.sci~ @@ -0,0 +1,9 @@ +function [imgList]=mattolist(imgMat) + [rows cols channels]=size(imgMat); + imgList=list(); + for i=1:channels + imgList($+1)=imgMat(:,:,i); + disp(imgList(i)) + end + +endfunction diff --git a/macros/mean.bin b/macros/mean.bin new file mode 100644 index 0000000..9d43669 Binary files /dev/null and b/macros/mean.bin differ diff --git a/macros/mean.sci b/macros/mean.sci new file mode 100644 index 0000000..0b3bdf9 --- /dev/null +++ b/macros/mean.sci @@ -0,0 +1,4 @@ +function [out]=mean1(image) + image1=mattolist(image); + out=opencv_mean1(image1); +endfunction; diff --git a/macros/medianblur.bin b/macros/medianblur.bin new file mode 100644 index 0000000..85c2ccc Binary files /dev/null and b/macros/medianblur.bin differ diff --git a/macros/medianblur.sci b/macros/medianblur.sci new file mode 100644 index 0000000..128edcd --- /dev/null +++ b/macros/medianblur.sci @@ -0,0 +1,7 @@ +function outputImg= medianblur(inputImage,ksize) + inputList=mattolist(inputImage); + outputList=opencv_medianblur(inputList,ksize) + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/minAreaRect.bin b/macros/minAreaRect.bin new file mode 100644 index 0000000..5216ab9 Binary files /dev/null and b/macros/minAreaRect.bin differ diff --git a/macros/minAreaRect.sci b/macros/minAreaRect.sci new file mode 100644 index 0000000..a0a48ad --- /dev/null +++ b/macros/minAreaRect.sci @@ -0,0 +1,4 @@ +function [out]=integralKernel(InputArraypoints) + [t1 t2 t3 t4 t5]= opencv_integralKernel(InputArraypoints); + out=struct("size1",t1,"size2",t2,"center1",t3,"center2",t4,"angle",t5); +endfunction; diff --git a/macros/minimumenclosingcirlce.bin b/macros/minimumenclosingcirlce.bin new file mode 100644 index 0000000..f11df6e Binary files /dev/null and b/macros/minimumenclosingcirlce.bin differ diff --git a/macros/minimumenclosingcirlce.sci b/macros/minimumenclosingcirlce.sci new file mode 100644 index 0000000..61aee6b --- /dev/null +++ b/macros/minimumenclosingcirlce.sci @@ -0,0 +1,4 @@ +function [out]=minimumenclosingcirlce(InputArraypoints) + [t1 t2 t3]= opencv_minimumenclosingcirlce(InputArraypoints); + out=struct("centerx",t1,"centery",t2,"radius",t3); +endfunction; diff --git a/macros/montage.bin b/macros/montage.bin new file mode 100644 index 0000000..e653df7 Binary files /dev/null and b/macros/montage.bin differ diff --git a/macros/montage.sci b/macros/montage.sci new file mode 100644 index 0000000..dbed292 --- /dev/null +++ b/macros/montage.sci @@ -0,0 +1,8 @@ +function [out]=montage(DataR,DataB,DataG) + a=opencv_montage(DataR,DataB,DataG); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/morphologyEx.bin b/macros/morphologyEx.bin new file mode 100644 index 0000000..a77b193 Binary files /dev/null and b/macros/morphologyEx.bin differ diff --git a/macros/morphologyEx.sci b/macros/morphologyEx.sci new file mode 100644 index 0000000..ef1a93b --- /dev/null +++ b/macros/morphologyEx.sci @@ -0,0 +1,15 @@ +function new_image = morphologyEx(image, operation, kernel, anchorX, anchorY, iterations, borderType) + + + image_list = mattolist(image) + + + out = opencv_morphologyEx(image_list, operation, kernel, anchorX, anchorY, iterations, borderType) + + sz = size(out) + + for i=1:sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/names b/macros/names new file mode 100644 index 0000000..74f5605 --- /dev/null +++ b/macros/names @@ -0,0 +1,219 @@ +BRISKPoints +CascadeObjectDetector +DCT +Deinterlacer +FFT +ForegroundDetector +IDCT +IFFT +KalmanFilter +SURFPoints +TemplateMatcher +VideoReader +adapthisteq +affine2d +approxpolyDP +arclength +arrowedline +autoCorrelator +bagOfFeatures +bagStructToList +bbox2points +bboxOverlapRatio +bilateralFilter +binaryFeatures +blobAnalysis +blur +borderInterpolate +boundingRect +boxfilter +bundleAdjustment +bwlookup +bwulterode +cameraMatrix +cameraParameters +canny +checkCCE +checkCEE +checkCIE +checkSCE +checkStereoParam +circle +classifierToList +clipline +contourarea +convexhull +convmtx2 +convolver +copymakeborder +corner +cornerEigenValsAndVecs +cornerHarris +cornerMinEigenVal +crossCorrelator +cvHoughLines +cvMaximum +cvMean +cvMedian +cvMinimum +cvStandardDeviation +cvVariance +cv_imread +cvtColor +dctmtx +decorrstretch +demosaic +detectBRISKFeatures +detectCheckerboardPoints +detectFASTFeatures +detectHarrisFeatures +detectMSERFeatures +detectMinEigenFeatures +detectSURFFeatures +dilate +displayErrors +ellipse2poly +erode +estimateFundamentalMatrix +estimateGeometricTransform +estimateUncalibratedRectification +evaluate +evaluateImageRetrieval +extractFeatures +extractHOGFeatures +extractLBPFeatures +fftshift +fillconvexpoly +filter +findcontours +fitellipse +fsamp2 +ftrans2 +gabor +gaussianblur +genCheckerboardPoints +geometricshearer +getStructuringElement +getTextSize +getgaussiankernel +getkerneltype +getrectsubpix +getrotationmatrix2d +goodfeaturestotrack +grayconnected +graycoprops +graydiffweight +graydist +has_frame +histeq +houghcircles +houghlines +houghlinesp +ifftshift +im2double +imadd +imageSet +imageSetToList +imboxfilt3 +imcomplement +imcontour +imcontrast +imcrop +imdivide +imextendedmax +imextendedmin +imfill +imfindcircles +imfuse +imgSetToList +imgaborfit +imgaussfilt3 +imguidedfilter +imhistmatch +imhmin +imimposemin +immultiply +impixel +imread +imrect +imresize +imsharpen +imshow +imshowpair +imsubtract +imwarp +imwrite +ind2gray +indexImages +integralFilter +integralImage +integralKernel +invertedIndexImages +isEpipoleInImage +lab2double +lab2rgb +lab2uint16 +lab2uint8 +lab2xyz +laplacian +line +lineToBorderPoints +localMaximaFinder +makecform +matchFeatures +mattolist +mean +medianblur +minAreaRect +minimumenclosingcirlce +montage +morphologyEx +ntsc2rgb +ocr +opencv_imread +opticalFlowFarneback +partition +peopleDetector +predict +puttext +pyrDown +pyrMeanShiftFiltering +pyrUp +pyramid +readForegroundMask +readframe +rectangle +rectifyStereoImages +regionfill +retrieveImages +rgb2lab +rgb2ntsc +rgb2xyz +roifill +roipoly +rotationVectorToMatrix +scharr +selectStrongestBbox +sepFilter2D +show +showMatchedFeatures +showReprojectionErrors +sobel +ssim +stereoParameters +threshold +trainCascadeObjectDetector +trainImageCategoryClassifier +triangulatePoints +undistort +undistortImage +undistortPoints +viscircle +warpaffine +watershed +whitepoint +wiener2 +xyz2double +xyz2lab +xyz2rgb +xyz2uint16 diff --git a/macros/ntsc2rgb.bin b/macros/ntsc2rgb.bin new file mode 100644 index 0000000..25404e6 Binary files /dev/null and b/macros/ntsc2rgb.bin differ diff --git a/macros/ntsc2rgb.sci b/macros/ntsc2rgb.sci new file mode 100644 index 0000000..9752bc7 --- /dev/null +++ b/macros/ntsc2rgb.sci @@ -0,0 +1,7 @@ +function [output] = ntsc2rgb(pstDataR,pstDataG,pstDataB) + a = opencv_ntsc2rgb(pstDataR,pstDataG,pstDataB); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/ocr.bin b/macros/ocr.bin new file mode 100644 index 0000000..3675d04 Binary files /dev/null and b/macros/ocr.bin differ diff --git a/macros/ocr.sci b/macros/ocr.sci new file mode 100644 index 0000000..7e0ed5b --- /dev/null +++ b/macros/ocr.sci @@ -0,0 +1,27 @@ +function [ txt ] = ocr(image) +// This function is used to identify the text in an image. +// +// Calling Sequence +// results = ocr(image); +// +// Parameters +// results: OCR Text Struct which contains text, characterBoundingBoxes, words and wordConfidences +// image: Input image to the OCR +// +// Description +// OCR stands for Optical Character Recognition. It is used for scanning documents which contain text and to convert them into documents that can be edited. +// +// Examples +// image = imread('sample.jpg'); +// results = ocr(image); +// +// Authors +// Rohit Suri + + image_list = mattolist(image) + + [ text, characterBoundingBoxes, words, WordConfidences ] = opencv_ocr(image_list) + + txt = struct("Text", text, "characterBoundingBoxes", characterBoundingBoxes, "Words", words, "WordConfidences", WordConfidences) + +endfunction diff --git a/macros/opencv_imread.bin b/macros/opencv_imread.bin new file mode 100644 index 0000000..5e5b482 Binary files /dev/null and b/macros/opencv_imread.bin differ diff --git a/macros/opencv_imread.sci b/macros/opencv_imread.sci new file mode 100644 index 0000000..3dd72d9 --- /dev/null +++ b/macros/opencv_imread.sci @@ -0,0 +1,11 @@ +function[dstMat] = opencv_imread(matA) + + out = opencv_imread(matA) + + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/opticalFlowFarneback.bin b/macros/opticalFlowFarneback.bin new file mode 100644 index 0000000..cfb63fd Binary files /dev/null and b/macros/opticalFlowFarneback.bin differ diff --git a/macros/opticalFlowFarneback.sci b/macros/opticalFlowFarneback.sci new file mode 100644 index 0000000..1bc2ccf --- /dev/null +++ b/macros/opticalFlowFarneback.sci @@ -0,0 +1,21 @@ +function opticalFlowFarneback(varargin) + [lhs rhs] = argn(0); + if(rhs>12) + error(msprintf("Too many input arguments")); + end + if(rhs==0) then + opencv_opticalFlowFarneback(); + elseif (rhs==2) then + opencv_opticalFlowFarneback(varargin(0),varargin(1)); + elseif(rhs==4) then + opencv_opticalFlowFarneback(varargin(0),varargin(1),varargin(2),varargin(3)); + elseif(rhs==6) then + opencv_opticalFlowFarneback(varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5)); + elseif(rhs==8) then + opencv_opticalFlowFarneback(varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7)); + elseif(rhs==10) then + opencv_opticalFlowFarneback(varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8),varargin(9)); + elseif(rhs==12) then + opencv_opticalFlowFarneback(varargin(0),varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8),varargin(9),varargin(10),varargin(11)); + end +endfunction diff --git a/macros/partition.bin b/macros/partition.bin new file mode 100644 index 0000000..83c9bbe Binary files /dev/null and b/macros/partition.bin differ diff --git a/macros/partition.sci b/macros/partition.sci new file mode 100644 index 0000000..b830597 --- /dev/null +++ b/macros/partition.sci @@ -0,0 +1,52 @@ +function [varargout]=partition(imageSet,groupSizesOrPercentages,varargin) +// This function is used to split an imageSet. +// +// Calling Sequence +// [set1 set2 ... setN] = partition(imgSet, groupSizes) +// [set1 set2 ... setN] = partition(imgSet, groupPercentages) +// [set1 set2 ... setN] = partition(..., method) +// +// Parameters +// set: Output imageSet structure +// imgSet: Input imageSet to be split +// groupSizes: Matrix that specifies size of output sets +// groupPercentages: Matrix that specifies percentage content of each output set +// method: (Optional) Determines whether split is 'sequential' or 'randomized' (Default: 'sequential') +// +// Description +// This function splits an imageSet into two or more imageSets on the basis of the groupSizes or groupPercentages provided. +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[5], 'randomized'); +// +// Authors +// Rohit Suri + + [lhs rhs]=argn(0); + if rhs>3 + error(msprintf(" Too many input arguments")); + end + cols=size(groupSizesOrPercentages,'c'); + if lhs>cols+1 then + error(msprintf(" Too many output arguments")); + end + imgSetList=imageSetToList(imageSet); + //sets=list();` + select rhs + case 2 then + sets=opencv_partition(imgSetList,groupSizesOrPercentages); + case 3 then + sets=opencv_partition(imgSetList,groupSizesOrPercentages,varargin(1)); + end + for i=1:lhs + for j=1:size(sets(i)(4)(1),'r') + imgLocations(j)=sets(i)(4)(1)(j,1); + end + varargout(i)=struct('Description',sets(i)(2),'ImageLocation',imgLocations,'Count',int32(sets(i)(3))); + imgLocations=[] + end +endfunction diff --git a/macros/peopleDetector.bin b/macros/peopleDetector.bin new file mode 100644 index 0000000..ed37ffb Binary files /dev/null and b/macros/peopleDetector.bin differ diff --git a/macros/peopleDetector.sci b/macros/peopleDetector.sci new file mode 100644 index 0000000..1770ae3 --- /dev/null +++ b/macros/peopleDetector.sci @@ -0,0 +1,79 @@ +function bboxes = peopleDetector(image, varargin) +// Detects people in an image +// +// Calling Sequence +// [bboxes] = peopleDetector(image) +// [bboxes] = peopleDetector(image, ["Parameter1", value1, ["Parameter2", value2 ... ]]) +// +// Parameters +// image : input image +// hitThreshold : threshold for distance between features and SVM classifying plane. Default value is 0. +// winStride : Window stride. Multiple of block stride. Default value (8, 8) +// padding : Default value (16, 16). +// scale : Coefficient of the detection window increase. Default value 1.05 +// finalThreshold : Coefficient to regulate the similarity threshold. Some people may cover more boxex. To regulate that it is used. Default value 2. +// useMeanshiftGrouping : Default value false. +// bboxes : M * 4 matrix denoting M bounding boxes for detected people +// +// Description +// The peopleDetector function detects standing people in the image using the Histogram of Oriented Gradient (HOG) features and a trained Support Vector Machine +// (SVM) classifier. It detects standing peole in the image. It returns M * 4 matrix having M detected people in the image. Each row of matrix contains +// [x, y, width, height] field. x, y represent the upper left corner point of the bounding box. width and height represent the width and height of the bounding box +// respectively. +// +// Examples +// I = imread('standing.jpg') +// [bboxes] = peopleDetector(I) +// I = imread('standing.jpg') +// [bboxes] = peopleDetector(I, "scale", 1.02) +// +// Authors +// Suraj Prakash + + + + [ lhs rhs ] = argn(0) + + if lhs > 1 then + error(msprintf("Too many output arguments")) + end + + if rhs > 13 then + error(msprintf("Too many input arguments")) + end + + if modulo(rhs, 2) <> 1 then + error(msprintf("Wrong number of input arguments")) + end + + image_list = mattolist(image) + + if rhs == 1 then + + bboxes = opencv_peopleDetector(image_list) + + elseif rhs == 3 then + + bboxes = opencv_peopleDetecotr(image_list, varargin(1), varargin(2)) + + elseif rhs == 5 then + + bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4)) + + elseif rhs == 7 then + + bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + + elseif rhs == 9 then + + bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varagin(7), varagin(8)) + + elseif rhs == 11 then + + bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10)) + + elseif rhs == 13 then + bboxes = opencv_peopleDetector(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10), varargin(11), varargin(12)) + end + +endfunction diff --git a/macros/predict.bin b/macros/predict.bin new file mode 100644 index 0000000..5ebccdc Binary files /dev/null and b/macros/predict.bin differ diff --git a/macros/predict.sci b/macros/predict.sci new file mode 100644 index 0000000..99a701e --- /dev/null +++ b/macros/predict.sci @@ -0,0 +1,32 @@ +function classifierDescription = predict(Categoryclassifier, image) +// This function is used to predict the category of an image using an image classifier. +// +// Calling Sequence +// label = predict(classifier, image) +// +// Parameters +// label: Evaluated label of the input image +// classifier: Image category classifier +// image: Input image +// +// Description +// This function predicts the category of an image based on the category classifier provided. +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// categoryClassifier = trainImageCategoryClassifier(trainingSet, bag); +// image = imread('sample.jpg'); +// label = predict(categoryClassifier, image); +// +// Authors +// Rohit Suri +// Umang Agrawal + + image_list = mattolist(image) + Categoryclassifier_list = classifierToList(Categoryclassifier); + + classifierDescription = opencv_predict(Categoryclassifier_list, image_list) + +endfunction diff --git a/macros/puttext.bin b/macros/puttext.bin new file mode 100644 index 0000000..4658fc0 Binary files /dev/null and b/macros/puttext.bin differ diff --git a/macros/puttext.sci b/macros/puttext.sci new file mode 100644 index 0000000..06a0c2b --- /dev/null +++ b/macros/puttext.sci @@ -0,0 +1,31 @@ +function new_image = puttext(image, x1, y1, fontscale, r_value, g_value, b_value, text, fontface, varargin) + + /// varargin(1) = thickness = 1, + /// varargin(2) = linetype = 8, + /// varargin(3) = bottomleft = false + + [ lhs, rhs ] = argn(0) + + image_list = mattolist(image) + + select rhs + case 9 then + out = opencv_puttext(image_list, x1, y1, fontscale, r_value, g_value, b_value, text, fontface) + + case 10 then + out = opencv_puttext(image_list, x1, y1, fontscale, r_value, g_value, b_value, text, fontface, varargin(1)) + + case 11 then + out = opencv_puttext(image_list, x1, y1, fontscale, r_value, g_value, b_value, text, fontface, varargin(1), varargin(2)) + + case 12 then + out = opencv_puttext(image_list, x1, y1, fontscale, r_value, g_value, b_value, text, fontface, varargin(1), varargin(2), varargin(3)) + end + + sz = size(out) + + for i = 1 : sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/pyrDown.bin b/macros/pyrDown.bin new file mode 100644 index 0000000..c6031e3 Binary files /dev/null and b/macros/pyrDown.bin differ diff --git a/macros/pyrDown.sci b/macros/pyrDown.sci new file mode 100644 index 0000000..999adb8 --- /dev/null +++ b/macros/pyrDown.sci @@ -0,0 +1,9 @@ +function [out]=pyrDown(input_image, dstsize_width, distsize_height, border) + input_image1=mattolist(input_image); + a=opencv_pyrDown(input_image1, dstsize_width, distsize_height, border); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/pyrMeanShiftFiltering.bin b/macros/pyrMeanShiftFiltering.bin new file mode 100644 index 0000000..7edaf41 Binary files /dev/null and b/macros/pyrMeanShiftFiltering.bin differ diff --git a/macros/pyrMeanShiftFiltering.sci b/macros/pyrMeanShiftFiltering.sci new file mode 100644 index 0000000..63b6d8e --- /dev/null +++ b/macros/pyrMeanShiftFiltering.sci @@ -0,0 +1,8 @@ +function [outputImg]=pyrMeanShiftFiltering(inputImage,sp,sr) + inputList=mattolist(inputImage); + outputList=opencv_pyrMeanShiftFiltering(inputList,sp,sr); + + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/pyrMeanShiftFiltering.sci~ b/macros/pyrMeanShiftFiltering.sci~ new file mode 100644 index 0000000..02d6e7e --- /dev/null +++ b/macros/pyrMeanShiftFiltering.sci~ @@ -0,0 +1,8 @@ +function [outputImg]=pyrMeanShiftFiltering(inputImage,sp,sr) + inputList=mattolist(inputImage); + outputList=opencv_pyrMeanShiftFiltering(inputList,sp,sr); + end + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/pyrUp.bin b/macros/pyrUp.bin new file mode 100644 index 0000000..41ad975 Binary files /dev/null and b/macros/pyrUp.bin differ diff --git a/macros/pyrUp.sci b/macros/pyrUp.sci new file mode 100644 index 0000000..b84b0b9 --- /dev/null +++ b/macros/pyrUp.sci @@ -0,0 +1,9 @@ +function [out]=pyrUp(input_image, dstsize_width, distsize_height, border) + input_image1=mattolist(input_image); + a=opencv_pyrUp(input_image1, dstsize_width, distsize_height, border); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/pyramid.bin b/macros/pyramid.bin new file mode 100644 index 0000000..7615a51 Binary files /dev/null and b/macros/pyramid.bin differ diff --git a/macros/pyramid.sci b/macros/pyramid.sci new file mode 100644 index 0000000..29d6c1e --- /dev/null +++ b/macros/pyramid.sci @@ -0,0 +1,35 @@ +function [out]=pyramid(inputimage,direction,level) +// Computes the pyramid reduction or expansion of input_image +// +// Calling Sequence +// output_image=pyramid(inputimage,direction,level); +// +// Parameters +// +// inputimage : image matrix on which pyramid reduction or expansion has to be applied +// Direction : An input string 'expand' for expand or 'reduce' for reduce the image matrix +// level : It is specify number of times want to expand or reduce the dimension of an image matrix. +// output_image : Expanded or reduced form of image after pyramid reduction or expansion +// +// Description +// This function computes the pyramid reduction or expansion of input image.It uses Gaussian pyramid to downsamples and upsamples the image pixels.In reduction it reduces the size of image (if m*n matrix image) by m/pow(2,level) and n/pow(2,level).In expansion it expands the size of image (if m*n matrix image) by m*pow(2,level) and n*pow(2,level).Where pow(x,y)=x^y. +// +// Examples +// inputimage1 = imread('lena.jpeg'); +// a=opencv_pyramid(inputimage1,'expand',2); +// imshow(a); +// +// Authors: +// +//Diwakar Bhardwaj + + + + inputimage1=mattolist(inputimage); + a=opencv_pyramid(inputimage1,direction,level); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/readForegroundMask.bin b/macros/readForegroundMask.bin new file mode 100644 index 0000000..a962df3 Binary files /dev/null and b/macros/readForegroundMask.bin differ diff --git a/macros/readForegroundMask.sci b/macros/readForegroundMask.sci new file mode 100644 index 0000000..12db37e --- /dev/null +++ b/macros/readForegroundMask.sci @@ -0,0 +1,37 @@ +function varargout=readForegroundMask(varargin) +// This function computes the foreground mask for the input image. +// +// Calling Sequence +// mask = readForegroundMask(image) +// +// Parameters +// mask: the computed foreground mask. +// image : the input image +// +// +// Description +// This function returns the foreground mask for an image using Gaussian Mixed Models. +// +// Examples +// myStr = ForegroundDetector() +// myVideo = VideoReader('sample.mp4'); +// while hasFrame() +// frame = readFrame(); +// mask = readForegroundMask(frame); +// end +// +// +// Authors +// Shashank Shekhar +[lhs,rhs]=argn(0) + if rhs<1 then + error(msprintf(" Not enough input arguments")) + end + if rhs>1 then + error(msprintf(" Too many input arguments to the function")) + end + if lhs>1 then + error(msprintf(" Too many output arguments")) + end + varargout(1) = ocv_readForegroundMask(varargin(1)); +endfunction diff --git a/macros/readframe.bin b/macros/readframe.bin new file mode 100644 index 0000000..1ae7401 Binary files /dev/null and b/macros/readframe.bin differ diff --git a/macros/readframe.sci b/macros/readframe.sci new file mode 100644 index 0000000..747c4de --- /dev/null +++ b/macros/readframe.sci @@ -0,0 +1,23 @@ +function [out]=readFrame(input) +// This function is used to return the next frame pointed to by the CurrentTime Property of A VideoReader Struct. +// +// Calling Sequence +// results = readFrame(); +// +// Parameters +// results: Frame of video. +// +// Description +// This function checks whether there is a next frame to grab in the video read through VideoReader and returns the frame. +// +// Examples +// videoStr = VideoReader('sample.mp4'); +// if(hasFrame()) then +// frame = readFrame() +// end +// +// Authors +// Shashank Shekhar +// Tanmay Chaudhari + out=ocv_readFrame(); +endfunction; diff --git a/macros/rectangle.bin b/macros/rectangle.bin new file mode 100644 index 0000000..892d563 Binary files /dev/null and b/macros/rectangle.bin differ diff --git a/macros/rectangle.sci b/macros/rectangle.sci new file mode 100644 index 0000000..b8c6857 --- /dev/null +++ b/macros/rectangle.sci @@ -0,0 +1,9 @@ +function [out]=rectangle(input_image , x_coordinate_of_point1 , y_coordinate_point1, x_coordinate_of_point2 , y_coordinate_point2 , r_value,g_value,b_value,thickness,linetype,shift) + input_image1=mattolist(input_image); + a=opencv_rectangle(input_image , x_coordinate_of_point1 , y_coordinate_point1, x_coordinate_of_point2 , y_coordinate_point2 , r_value,g_value,b_value,thickness,linetype,shift); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + +endfunction; diff --git a/macros/rectifyStereoImages.bin b/macros/rectifyStereoImages.bin new file mode 100644 index 0000000..c96dc09 Binary files /dev/null and b/macros/rectifyStereoImages.bin differ diff --git a/macros/rectifyStereoImages.sci b/macros/rectifyStereoImages.sci new file mode 100644 index 0000000..a6f16fe --- /dev/null +++ b/macros/rectifyStereoImages.sci @@ -0,0 +1,59 @@ +function [out]=rectifyStereoImages(input_image1,cameramatrix1,Distortion_coff1,input_image2,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector) +// Rectify the pair of image taken by stereo camera +// +// Calling Sequence +// [left_image,right_image] = rectifyStereoImages (input_image1,cameramatrix1,Distortion_coff1,input_image2,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector) +// left_image = rectified input_image1 +// right_image = rectified input_image2 +// +// Parameters +// +// input_image1 : First Stereo image on which rectification has to be performed +// Cameramatrix1 : An input matrix of 3*3 +// Distortion_coff1 : A vector of size 4 ,5 or 8 +// input_image2 : Second Stereo image on which rectification has to be performed +// Cameramatrix2 : An input matrix of 3*3 +// Distortion_coff2 : A vector of size 4 ,5 or 8 +// Translationvector : A vector of size 3 +// Rotationvector : An input matrix of 3*3 +// left_image : rectified input_image1 +// right_image : rectfied input_image2 +// +// Description +// This function first undistorted and then rectified the both image1 and image2.It uses camera parameters,respective rotation,translation of both camera. +// +// Examples +// a = imread('lena.jpeg') +// cameramatrix1 = [1 0 1;0 1 1;0 0 1]; +// Distortion_coff1 = [200 200 400 300]; +// b = imread('lena.jpeg') +// cameramatrix2 = [2 0 1;0 1 1;0 0 1]; +// Distortion_coff2 = [205 240 410 370]; +// Translationvector = [1 2 3]; +// Rotationvector = [0 0 2;1 4 5;6 7 8]; +// [left_image,right_image] = rectifyStereoImages(a,cameramatrix1,Distortion_coff1,b,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector); +// imshow(left_image); +// imshow(right_image); +// +// Authors. +// +//Diwakar Bhardwaj + + input_image3=mattolist(input_image1); + input_image4=mattolist(input_image2); + [left_image right_image]=rectifyStereoImages(input_image3,cameramatrix1,Distortion_coff1,input_image4,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector); + + dimension1=size(left_image) + for i = 1:dimension1 + left_out(:,:,i)=left_image(i); + end + + dimension2=size(right_image) + for j = 1:dimension2 + right_out(:,:,j)=right_image(j); + end + + + out=struct("left_rectifyimage",left_out,"right_rectifyimage",right_out); + +endfunction; diff --git a/macros/rectifyStereoImages.sci~ b/macros/rectifyStereoImages.sci~ new file mode 100644 index 0000000..2cd9c54 --- /dev/null +++ b/macros/rectifyStereoImages.sci~ @@ -0,0 +1,59 @@ +function [out]=rectifyStereoImages(input_image1,cameramatrix1,Distortion_coff1,input_image2,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector) +// Rectify the pair of image taken by stereo camera +// +// Calling Sequence +// [left_image,right_image] = rectifyStereoImages (input_image1,cameramatrix1,Distortion_coff1,input_image2,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector) +// left_image = rectified input_image1 +// right_image = rectified input_image2 +// +// Parameters +// +// input_image1 : First Stereo image on which rectification has to be performed +// Cameramatrix1 : An input matrix of 3*3 +// Distortion_coff1 : A vector of size 4 ,5 or 8 +// input_image2 : Second Stereo image on which rectification has to be performed +// Cameramatrix2 : An input matrix of 3*3 +// Distortion_coff2 : A vector of size 4 ,5 or 8 +// Translationvector : A vector of size 3 +// Rotationvector : An input matrix of 3*3 +// left_image : rectified input_image1 +// right_image : rectfied input_image2 +// +// Description +// This function first undistorted and then rectified the both image1 and image2.It uses camera parameters,respective rotation,translation of both camera. +// +// Examples +// a = imread('lena.jpeg') +// cameramatrix1 = [1 0 1;0 1 1;0 0 1]; +// Distortion_coff1 = [200 200 400 300]; +// b = imread('lena.jpeg') +// cameramatrix2 = [2 0 1;0 1 1;0 0 1]; +// Distortion_coff2 = [205 240 410 370]; +// Translationvector = [1 2 3]; +// Rotationvector = [0 0 2;1 4 5;6 7 8]; +// [left_image,right_image] = rectifyStereoImages(a,cameramatrix1,Distortion_coff1,b,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector); +// imshow(left_image); +// imshow(right_image); +// +// Authors. +// +//Diwakar Bhardwaj + + input_image3=mattolist(input_image1); + input_image4=mattolist(input_image2); + [left_image right_image]=rectifyStereoImages(input_image3,cameramatrix1,Distortion_coff1,input_image4,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector); + + dimension1=size(left_image) + for i = 1:dimension1 + left_out(:,:,i)=left_image(i); + end + + dimension2=size(right_image) + for j = 1:dimension2 + right_out(:,:,j)=right_image(j); + end + + + out=struct("left_rectifyimage",left_out,"right_rectifyimage",right_out); + +endfunction;* diff --git a/macros/regionfill.bin b/macros/regionfill.bin new file mode 100644 index 0000000..fa7e696 Binary files /dev/null and b/macros/regionfill.bin differ diff --git a/macros/regionfill.sci b/macros/regionfill.sci new file mode 100644 index 0000000..74c4779 --- /dev/null +++ b/macros/regionfill.sci @@ -0,0 +1,18 @@ +function[dstImg] = regionfill(srcImg, varargin) + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + if rhs == 2 then + out = opencv_regionfill(srcMat, varargin(1)) + elseif rhs == 2 then + out = opencv_regionfill(srcMat, varargin(1), varargin(2)) + end + + channels = size(out) + + for i = 1:channels + dstImg(:,:,i) = out(i) + end +endfunction diff --git a/macros/retrieveImages.bin b/macros/retrieveImages.bin new file mode 100644 index 0000000..a0cd3bf Binary files /dev/null and b/macros/retrieveImages.bin differ diff --git a/macros/retrieveImages.sci b/macros/retrieveImages.sci new file mode 100644 index 0000000..0c8f8a2 --- /dev/null +++ b/macros/retrieveImages.sci @@ -0,0 +1,103 @@ +function [varargout] = retrieveImages(QueryImage, IndexImage, varargin) +// This function searches for similar image in a given image set +// +// Calling Sequence +// [imageID Score ImageWord] = retrieveImage(QueryImage, IndexImage, Name, Value... ) +// +// Parameters +// QueryImage: The query image, for which the similar image has to be found. Can be a grayscale or a RGB image +// IndexImage: imageIndex object that contains the data set of all the images to be compared +// NumResults [Optional Input Argument]: Maximum number of results to be returned. Value: any integer (20 default) +// ROI [Optional Input Argument]: Query Image search region. Format [ x y width height ]. Default: [1 1 size(Image,2) size(Image,1)] +// imageID: M-by-1 vector consisting of Ranked Index of retrieved Images +// Score: M-by-1 vector containing the similarity metric in the range 0 to 1 +// ImageWord: Visual Word mapped object for the Query Image +// +// Description +// Returns the indices corresponding to Images within imageIndex that are visually similar to the query Image +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// imageindex = indexImages(trainingSet, bag); +// queryImage = imread('sample.jpg'); +// imageIDs = retrieveImages(queryImage, imageindex); +// +// With Optional Arguments: +// imageIDs = retrieveImages(queryImage, imageindex, "NumResults", 10); +// +// Authors +// Umang Agrawal +// Rohit Suri + + /// varargout(1) = index + /// varargout(2) = score + /// varargout(3) = Imageword + + [ lhs rhs ] = argn(0) + + if rhs > 6 then + error(msprintf("Too many input arguments")) + end + + if lhs > 3 then + error(msprintf("Too many output arguments")) + end + + QueryImage_list = mattolist(QueryImage) + + if lhs == 1 then + select rhs + case 2 then + + index = opencv_retrieveImages(QueryImage_list, IndexImage) + + case 4 then + + index = opencv_retrieveImages(QueryImage_list, IndexImage, varargin(1), varargin(2)) + + case 6 then + index = opencv_retrieveImages(QueryImage_list, IndexImage, varargin(1), varargin(2), varagin(3), varargin(4)) + end + + varargout(1) = index + + elseif lhs == 2 then + select rhs + case 2 then + + [index, score] = opencv_retrieveImages(QueryImage_list, IndexImage) + + case 4 then + + [index, score] = opencv_retrieveImages(QueryImage_list, IndexImage, varargin(1), varargin(2)) + + case 6 then + [index, score] = opencv_retrieveImages(QueryImage_list, IndexImage, varargin(1), varargin(2), varagin(3), varargin(4)) + end + + varargout(1) = temp + varargout(2) = score + + elseif lhs == 3 then + select rhs + case 2 then + + [index, score, Imageword] = opencv_retrieveImages(QueryImage_list, IndexImage) + + case 4 then + + [index, score, Imageword] = opencv_retrieveImages(QueryImage_list, IndexImage, varargin(1), varargin(2)) + + case 6 then + [index, score, Imageword] = opencv_retrieveImages(QueryImage_list, IndexImage, varargin(1), varargin(2), varagin(3), varargin(4)) + end + + varargout(1) = index + varargout(2) = score + del = struct("WordIndex", Imageword(1), "Location", Imageword(2), "Vocab_size", Imageword(3), "Count", Imageword(4)) + varargout(3) = del + end + +endfunction diff --git a/macros/rgb2lab.bin b/macros/rgb2lab.bin new file mode 100644 index 0000000..9c4261a Binary files /dev/null and b/macros/rgb2lab.bin differ diff --git a/macros/rgb2lab.sci b/macros/rgb2lab.sci new file mode 100644 index 0000000..0035bd5 --- /dev/null +++ b/macros/rgb2lab.sci @@ -0,0 +1,10 @@ +//Author- Sridhar Reddy +//inputImage should be matrix +function [outputImg]=rgb2lab(inputImage) + inputList=mattolist(inputImage); + outputList=opencv_rgb2lab(inputList); + disp(outputList(2)); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/rgb2ntsc.bin b/macros/rgb2ntsc.bin new file mode 100644 index 0000000..9fd18dd Binary files /dev/null and b/macros/rgb2ntsc.bin differ diff --git a/macros/rgb2ntsc.sci b/macros/rgb2ntsc.sci new file mode 100644 index 0000000..6fdcede --- /dev/null +++ b/macros/rgb2ntsc.sci @@ -0,0 +1,8 @@ +function [output] = rgb2ntsc(img) + image = mattolist(img); + a = opencv_rgb2ntsc(image); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/rgb2xyz.bin b/macros/rgb2xyz.bin new file mode 100644 index 0000000..9ab02d5 Binary files /dev/null and b/macros/rgb2xyz.bin differ diff --git a/macros/rgb2xyz.sci b/macros/rgb2xyz.sci new file mode 100644 index 0000000..b15d2ce --- /dev/null +++ b/macros/rgb2xyz.sci @@ -0,0 +1,8 @@ +function [output] = rgb2xyz(img) + image = mattolist(img); + a = opencv_rgb2xyz(image); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/roifill.bin b/macros/roifill.bin new file mode 100644 index 0000000..cf3fa1b Binary files /dev/null and b/macros/roifill.bin differ diff --git a/macros/roifill.sci b/macros/roifill.sci new file mode 100644 index 0000000..b69288a --- /dev/null +++ b/macros/roifill.sci @@ -0,0 +1,28 @@ +function new_image = roifill(image, mask_orig, varargin) + + [ lhs, rhs ] = argn(0) + + if rhs < 2 then + error(msprintf("Too less input arguments")) + elseif rhs > 3 then + error(msprintf("Too many input arguments")) + end + + + image_list = mattolist(image) + + + select rhs + case 2 + out = opencv_roifill(image_list, mask_orig) + + case 3 + out = opencv_roifill(image_list, mask_orig, varargin(1)) + end + + sz = size(out) + for i=1 : sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/roipoly.bin b/macros/roipoly.bin new file mode 100644 index 0000000..770dca6 Binary files /dev/null and b/macros/roipoly.bin differ diff --git a/macros/roipoly.sci b/macros/roipoly.sci new file mode 100644 index 0000000..4323812 --- /dev/null +++ b/macros/roipoly.sci @@ -0,0 +1,13 @@ +function[dstImg] = roipoly(srcImg, col_list, row_list) + + srcMat = mattolist(srcImg) + + out = opencv_roipoly(srcMat, col_list, row_list) + + channels = size(out) + + for i = 1:channels + dstImg(:,:,i) = out(i) + end + +endfunction diff --git a/macros/rotationVectorToMatrix.bin b/macros/rotationVectorToMatrix.bin new file mode 100644 index 0000000..252c686 Binary files /dev/null and b/macros/rotationVectorToMatrix.bin differ diff --git a/macros/rotationVectorToMatrix.sci b/macros/rotationVectorToMatrix.sci new file mode 100644 index 0000000..abe97f9 --- /dev/null +++ b/macros/rotationVectorToMatrix.sci @@ -0,0 +1,24 @@ +function [ matrix ] = rotationVectorToMatrix(vector) +// Returns rotation matrix. +// +// Calling Sequence +// matrix = rotationVectorToMatrix(vector); +// +// Parameters +// matrix: rotation matrix +// vector: 3-D rotation vector +// +// Description +// Converts rotation vector to rotation matrix. +// +// Examples +// vector = pi/4 * [1, 2, 3]; +// matrix = rotationVectorToMatrix(vector); +// +// Authors +// Tanmay Chaudhari + + a=opencv_rotationVectorToMatrix(vector); + matrix(:,:,1)=a(1); + +endfunction diff --git a/macros/scharr.bin b/macros/scharr.bin new file mode 100644 index 0000000..f803903 Binary files /dev/null and b/macros/scharr.bin differ diff --git a/macros/scharr.sci b/macros/scharr.sci new file mode 100644 index 0000000..53d1903 --- /dev/null +++ b/macros/scharr.sci @@ -0,0 +1,13 @@ +function new_image = scharr(image, ddepth, dx, dy, scale, delta) + + image_list = mattolist(image) + + out = opencv_scharr(image_list, ddepth, dx, dy, scale, delta) + + sz = size(out) + + for i=1:sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/selectStrongestBbox.bin b/macros/selectStrongestBbox.bin new file mode 100644 index 0000000..c9c3502 Binary files /dev/null and b/macros/selectStrongestBbox.bin differ diff --git a/macros/selectStrongestBbox.sci b/macros/selectStrongestBbox.sci new file mode 100644 index 0000000..57c3ac4 --- /dev/null +++ b/macros/selectStrongestBbox.sci @@ -0,0 +1,147 @@ +function [selectedBbox,selectedScore,varargout]=selectStrongestBbox(bBox,score,varargin) +// Selecting strongest bounding boxes +// +// Calling Sequence +// [selectedBbox, selectedScore]= selectStrongestBbox(bBox,score); +// [selectedBbox, selectedScore]= selectStrongestBbox(bBox,score,Name,Value); +// [selectedBbox, selectedScore, selectionIndex]= selectStrongestBbox(bBox,score,Name,Value); +// +// Parameters +// bBox: Each row represents one bounding box specified as [x y width height]; +// score: Confidence Score of bounding box +// varargin: optinal (Name,Value) pair arguments +// Optional arguments can be +// +// RatioType: method to compute the ratio of the intersection area between two boxes and it is specified as string, possile values are 'Union', 'Min' +// overlapThreshold: It specifies maximum overlap ratio, if overlap ration is more than this corresponding bounding box will be removed, possible range is 0 to 1 +// +// +// Description +// Returns strongest bounding boxes as per the given RatioType and OverlapThreshold and additionally it returns the index of the selected boxes +// +// Examples +// box(1)=[100 200 150 140]; +// box(2)=[100 180 140 160]; +// score(1)=0.03; +// score(2)=0.05; +// [sb ss]=selectStrongestBbox(box,score); + + [lhs,rhs]=argn(0) + + if rhs<2 then + error(msprintf(" Not enough input arguments")) + elseif rhs>6 then + error(msprintf(" Too many input arguments to the function")) + elseif lhs<2 then + error(msprintf(" Not enough output arguments")) + elseif lhs>3 then + error(msprintf(" Too many output arguments")) + end + [bBoxRows bBoxCols]=size(bBox); + [scoreRows scoreCols]=size(score); + if ~bBoxCols==4 then + error(msprintf("bounding box matrix must be M*4")) + elseif ~scoreCols==1 then + error(msprintf(" score matrix must be M*1")) + elseif ~bBoxRows==scoreRows then + error(msprintf(" The number of bounding boxes and scores should be same")) + elseif ~isreal(bBox) + error(msprintf(" Wrong input argument,complex matrix is not expected")) + end + for i=1:bBoxRows + if bBox(i,3)<0 | bBox(i,4)<0 + error(msprintf(" The width and height of the bounded box must be positive")) + end + end + + ratioType=1; + overlapThreshold=0.5; + for i=1:2:rhs-2 + if strcmpi(varargin(i),"RatioType")==0 then + i=i+1; + if strcmpi(varargin(i),'union')== 0 then + ratioType=1;//1-union + elseif strcmpi(varargin(i),'min')==0 then + ratioType=0;//0-min + else + error(msprintf(" wrong value for the ratio type,it must be union or min")) + end + elseif strcmpi(varargin(i),'OverlapThreshold')==0 then + i=i+1; + if 0<=varargin(i) & varargin(i)<=1 then + overlapThreshold=varargin(i) + else + error(msprintf("wrong value for the overlapThreshold,it must be in between 0 and 1")) + end + else + error(msprintf(_(" Wrong value for input argument"))); + end + end + for i=1:bBoxRows + index(i)=i; + end + score_temp=score(:,1); + for i=1:bBoxRows + for j=i+1:bBoxRows + if score_temp(i,1) overlapThreshold + selection(j) = 0; + end + end + end + end + end + k=1; + for i=1:bBoxRows + if selection(i) + selectionIndex(k)=i; + indexOriginal(k)=index(i); + k=k+1; + end + end + //disp(indexOriginal); + [selectedIndexRows selectedIndexRows]=size(selectionIndex) + for i=1:selectedIndexRows + for j=i+1:selectedIndexRows + if indexOriginal(i)>indexOriginal(j) + temp=indexOriginal(i); + indexOriginal(i)=indexOriginal(j) + indexOriginal(j)=temp; + end + end + end + //disp("original indexes"); + //disp(indexOriginal); + selectedBbox=bBox(indexOriginal,:); + selectedScore=score(indexOriginal,:); + varargout(1)=indexOriginal; +endfunction diff --git a/macros/sepFilter2D.bin b/macros/sepFilter2D.bin new file mode 100644 index 0000000..b6fd319 Binary files /dev/null and b/macros/sepFilter2D.bin differ diff --git a/macros/sepFilter2D.sci b/macros/sepFilter2D.sci new file mode 100644 index 0000000..2edfee1 --- /dev/null +++ b/macros/sepFilter2D.sci @@ -0,0 +1,13 @@ +function new_image = sepFilter2D(image, ddepth, kernel_x, kernel_y, anchor_x, anchor_y, delta, border) + + image_list = mattolist(image) + + out = opencv_sepFilter2D(image_list, ddepth, kernel_x, kernel_y, anchor_x, anchor_y, delta, border) + + sz = size(out) + + for i = 1 : sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/show.bin b/macros/show.bin new file mode 100644 index 0000000..960ba2d Binary files /dev/null and b/macros/show.bin differ diff --git a/macros/show.sci b/macros/show.sci new file mode 100644 index 0000000..aec4db8 --- /dev/null +++ b/macros/show.sci @@ -0,0 +1,18 @@ + function[] =show(Img,ColorMap) + FigureHandle = gcf(); + drawlater(); + FigureHandle.color_map = ColorMap + FigureHandle.background = -2; // sets the background to white + FigureHandle.figure_name = "Title"; + [NumberOfRows NumberOfColumns] = size(Img); + FigureHandle.axes_size = [NumberOfColumns NumberOfRows]; + delete(gca()); // previous image is deleted + Diagram = gca(); + [NumberOfRows NumberOfColumns] = size(Img); + Diagram.data_bounds = [1, 1; NumberOfColumns, NumberOfRows]; + Diagram.axes_visible = ['off' 'off' 'off']; + Diagram.isoview = 'on'; + Options = '082'; // Box is drawn around image. + Matplot(Img, Options); + drawnow(); + endfunction diff --git a/macros/showMatchedFeatures.bin b/macros/showMatchedFeatures.bin new file mode 100644 index 0000000..edf37bc Binary files /dev/null and b/macros/showMatchedFeatures.bin differ diff --git a/macros/showMatchedFeatures.sci b/macros/showMatchedFeatures.sci new file mode 100644 index 0000000..f1608f5 --- /dev/null +++ b/macros/showMatchedFeatures.sci @@ -0,0 +1,10 @@ +function [output] = showMatchedFeatuers(image1,image2) + img1 = mattolist(image1); + img2 = mattolist(image2); + a = opencv_showMatchedFeatures(img1,img2) + d = size(a); + for i = 1:d + output(:,:,i) = a(i); + end +endfunction + diff --git a/macros/showReprojectionErrors.bin b/macros/showReprojectionErrors.bin new file mode 100644 index 0000000..2652cc1 Binary files /dev/null and b/macros/showReprojectionErrors.bin differ diff --git a/macros/showReprojectionErrors.sci b/macros/showReprojectionErrors.sci new file mode 100644 index 0000000..94a77af --- /dev/null +++ b/macros/showReprojectionErrors.sci @@ -0,0 +1,57 @@ +function showReprojectionErrors(cameraParameters) +// It displays reprojection errors using bar graph +// +// Calling Sequence +// showReprojectionErrors(cameraParameters) +// +// Parameters +// cameraParameters: cameraParameters strucure +// +// Description +// It visualizes reprojection errors for a single camera or stereo pair if cameras. +// +// Examples +// showReprojectionErrors(cameraParameters); + [lhs rhs]=argn(0) + if rhs<1 then + error(msprintf("Need atleat one argument")); + elseif ~isstruct(cameraParameters) then + error(msprintf("Given input is not a valid cameraParameters structure")); + end + + ReprojectionErrors=cameraParameters.ReprojectionErrors; + if ~isempty(ReprojectionErrors) then + ReprojectedPoints=cameraParameters.ReprojectedPoints; + ReprojectionErrors=cameraParameters.ReprojectionErrors; + ImagePoints=ReprojectedPoints-ReprojectionErrors; + totalErr=0; + rvRows=size(cameraParameters.RotationVectors,'r'); + wpRows=size(cameraParameters.WorldPoints,'r'); + for i=1:rvRows + err=0; + for j=1:wpRows + err=err+((ReprojectedPoints(j,1,i)-ImagePoints(j,1,i))+(ReprojectedPoints(j,2,i)-ImagePoints(j,2,i))).^2; + end + totalErr=totalErr+err; + errors(i)=sqrt(err/wpRows); + end + totalPoints=rvRows*wpRows; + meanReprojectionError=sqrt(totalErr/totalPoints); + end + da=gda(); + da.title.text="Mean Reprojection Errors per Image" + da.title.foreground = 12; + da.title.font_size = 5; + da.x_label.text="Images"; + da.x_label.font_style = 3; + da.x_label.font_size = 3; + da.x_label.foreground = 3; + da.x_location = "bottom"; + da.y_label.text="Mean Reprojection Errors"; + da.y_label.font_style = 3; + da.y_label.font_size = 3; + da.y_label.foreground = 3; + da.y_location = "left"; + a=gca(); + bar(a,errors,0.2); //0.2- width of each bar +endfunction diff --git a/macros/sobel.bin b/macros/sobel.bin new file mode 100644 index 0000000..75cca5c Binary files /dev/null and b/macros/sobel.bin differ diff --git a/macros/sobel.sci b/macros/sobel.sci new file mode 100644 index 0000000..17af909 --- /dev/null +++ b/macros/sobel.sci @@ -0,0 +1,13 @@ +function new_image = sobel(image, ddepth, dx, dy, ksize, scale, delta) + + image_list = mattolist(image) + + out = opencv_sobel(image_list, ddepth, dx, dy, ksize, scale, delta) + + sz = size(out) + + for i=1:sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/ssim.bin b/macros/ssim.bin new file mode 100644 index 0000000..698d61d Binary files /dev/null and b/macros/ssim.bin differ diff --git a/macros/ssim.sci b/macros/ssim.sci new file mode 100644 index 0000000..5972816 --- /dev/null +++ b/macros/ssim.sci @@ -0,0 +1,7 @@ +function[ssim_val] = ssim(srcImg, reference) + + srcMat = mattolist(srcImg) + + ssim_val = opencv_ssim(srcMat, reference) + +endfunction diff --git a/macros/stereoParameters.bin b/macros/stereoParameters.bin new file mode 100644 index 0000000..48889dc Binary files /dev/null and b/macros/stereoParameters.bin differ diff --git a/macros/stereoParameters.sci b/macros/stereoParameters.sci new file mode 100644 index 0000000..502217c --- /dev/null +++ b/macros/stereoParameters.sci @@ -0,0 +1,85 @@ +function varargout=stereoParameters(varargin) + [lhs rhs]=argn(0); + if lhs<1 then + error(msprintf(" Not enough output arguments. One expected")) + elseif lhs>1 then + error(msprintf(" Too many onput arguments. One expected")) + end + + if rhs~=4 then + error(msprintf(" Invalid number of input arguments")) + end + + EssentialMatrix = []; + FundamentalMatrix = []; + MeanReprojectionError = 0; + NumPatterns = 0; + WorldPoints=[]; + WorldUnits='mm'; + + cameraParam1 = varargin(1); + cameraParam2 = varargin(2); + + if ~isstruct(cameraParam1) then + error(msprintf(" A Camera Parameter object expected")) + end + + if ~isstruct(cameraParam2) then + error(msprintf(" A Camera Parameter object expected")) + end + + xz = fieldnames(cameraParam1); + [size1 zz] = size(xz); + xz = fieldnames(cameraParam2); + [size2 zz] = size(xz); + + if size1~=18 | size2~=18 then + error(msprintf(" Invalid Camera Parameter Object")) + end + + checkCameraParam(cameraParam1); + checkCameraParam(cameraParam2); + // checking third input + + rotationOfCam = varargin(3); + [type3] = type(rotationOfCam); + + if type3~=1 | ~isreal(rotationOfCam) then + error(msprintf(" Invalid Input Argument. A real 3x3 Matrix expected.")) + end + + [rows cols] = size(rotationOfCam); + + if rows*cols~=9 then + error(msprintf(" Invalid Input Argument. A 3x3 Matrix expected.")) + end + + // checking 4th input + + translationOfCam = varargin(4); + [type4] = type(translationOfCam); + + if type4~=1 | ~isreal(translationOfCam) then + error(msprintf(" Invalid Input Argument. A real list of 3 expected. ")) + end + + [rows cols] = size(translationOfCam); + + if rows*cols~=3 then + error(msprintf(" Invalid Input Argument. A 3x3 Matrix expected.")) + end + + + g = translationOfCam; + gx = [0 -g(3) g(2); g(3) 0 -g(1); -g(2) g(1) 0]; + EssentialMatrix = rotationOfCam*gx; + FundamentalMatrix = cameraParam1.IntrinsicMatrix \ EssentialMatrix / (cameraParam2.IntrinsicMatrix)'; + MeanReprojectionError = (cameraParam1.meanReprojectionError + cameraParam2.meanReprojectionError)/2; + NumPatterns = cameraParam1.NumPatterns; + WorldPoints = cameraParam1.WorldPoints; + WorldUnits = cameraParam1.WorldUnits; + + varargout(1) = struct('CameraParameters1', cameraParam1, 'CameraParameters2', cameraParam2, 'RotationOfCamera2', rotationOfCam, 'TranslationOfCamera2', translationOfCam, 'FundamentalMatrix', FundamentalMatrix, 'EssentialMatrix', EssentialMatrix,'MeanReprojectionError', MeanReprojectionError, 'NumPatterns', NumPatterns, 'WorldPoints', WorldPoints, 'WorldUnits', WorldUnits); + +endfunction + diff --git a/macros/threshold.bin b/macros/threshold.bin new file mode 100644 index 0000000..c62923a Binary files /dev/null and b/macros/threshold.bin differ diff --git a/macros/threshold.sci b/macros/threshold.sci new file mode 100644 index 0000000..d9439d6 --- /dev/null +++ b/macros/threshold.sci @@ -0,0 +1,7 @@ +function [outputImg,res]= threshold(inputImage, threshold_value, max_value,thresholdType) + inputList=mattolist(inputImage); + [outputList,res]=opencv_threshold(inputList, threshold_value, max_value,thresholdType) + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/trainCascadeObjectDetector.bin b/macros/trainCascadeObjectDetector.bin new file mode 100644 index 0000000..d103c5d Binary files /dev/null and b/macros/trainCascadeObjectDetector.bin differ diff --git a/macros/trainCascadeObjectDetector.sci b/macros/trainCascadeObjectDetector.sci new file mode 100644 index 0000000..eb5369a --- /dev/null +++ b/macros/trainCascadeObjectDetector.sci @@ -0,0 +1,163 @@ +function trainCascadeObjectDetector(outputFolder,positiveInstances,negativeImages,varargin) +// It creates trained cascade XML file +// +// Calling Sequence +// trainCascadeObjectDetector(outputFolder,positiveInstances,"negativeImages") +// +// Parameters +// outputFolder: Folder name to store trained cascade (cascade.xml) and intermediate files +// positiveInstances: Array of structure with image file names (including path) and an M-by-4 matrix of bounding box. +// negativeImages: path to a negative images folder +// numPos: number of positive samples. Default-30 +// numNeg: number of negative samples. Default- 20 +// nsplits: number of splits. Default- 2 +// numStages: number of cascade stages to be trained. Default- 30 +// featureType: Type of features to be used, possible types are HAAR, LBP, HOG. Default-HAAR +// minHitRate: Minimal desired hit rate for each stage of the classifier and value in the range 0 and 1 inclusive. Default- 0.995 +// maxFalseAlarmRate: Maximal desired false alarm rate for each stage of the classifier and value in the range 0 and 1 inclusive. Default- 0.5 +// w: Width of training samples (in pixels). Default-25 +// h: Height of training samples (in pixels). Default-25 +// +// Description +// It produces dataset of positive samples in a file with .vec extension and negative samples are enumerated in a special text file in +// which each line contains an image filename of negative sample image. Negative images must not contain detected objects. +// +// By using these two files trainCascade will create cascade.xml file inside the outputFolder which is used to detect objects in an image. +// +// Examples +// positiveInstances(1)=struct("path",'image1.jpg',"bbox",[1 5 2 3]); +// positiveInstances(2)=struct("path",'image2.jpg',"bbox",[2 7 4 2]); +// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder"); +// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder","numStages",35,"featureType","HOG"); +// trainCascadeObjectDetector("data",positiveInstances,"negativeImagesFolder","minHitRate",0.9,"numPos",8,"numNeg",5); + + [lhs rhs]=argn(0); + if rhs<3 then + error(msprintf(" Not enough input arguments")) + elseif rhs>21 then + error(msprintf(" Too many input arguments to the function")) + elseif modulo(rhs-3,2) + error(msprintf(" wrong number of input arguments,name-value pairs not macthed")) + end + + //validating variables + if ~isdir(outputFolder) then + error(msprintf(" wrong input argument #1, existing directory expected")) + elseif ~isdir(negativeImages) + error(msprintf(" wrong input argument #3,existing directory expected")) + end + + //default values + numPos=30; + numNeg=20; + numStages=30; + nsplits=2; + featureType="HAAR" + minHitRate=0.995 + maxFalseAlarmRate=0.5 + w=25 + h=25 + + for i=1:2:rhs-3 + if strcmpi(varargin(i),"numPos")==0 then + i=i+1; + numPos=varargin(i); + if numPos<0 then + error(msprintf(" numPos value must be positive")) + end + elseif strcmpi(varargin(i),'numNeg')==0 then + i=i+1; + numNeg=varargin(i); + if numNeg<0 then + error(msprintf(" numNeg value must be positive")) + end + + elseif strcmpi(varargin(i),'numStages')==0 then + i=i+1; + numStages=varargin(i); + if numStages<0 then + error(msprintf(" numStages value must be positive")) + end + + elseif strcmpi(varargin(i),'nslits')==0 then + i=i+1; + nsplits=varargin(i); + if nsplits<0 then + error(msprintf(" nsplits value must be positive")) + end + + elseif strcmpi(varargin(i),'featureType')==0 then + i=i+1; + featureType=varargin(i); + if strcmpi(featureType,'haar') & strcmpi(featureType,'lbp') & strcmpi(featureType,'hog') + error(msprintf(" wrong input argument #%d,featureType not matched",i)); + end + + elseif strcmpi(varargin(i),'minHitRate')==0 then + i=i+1; + minHitRate=varargin(i); + if minHitRate<0 | minHitRate>1 then + error(msprintf(" minHitRate value must lie in between 0 and 1")) + end + + elseif strcmpi(varargin(i),'maxFalseAlarmRate')==0 then + i=i+1; + maxFalseAlarmRate=varargin(i); + if maxFalseAlarmRate<0 | minFalseRate>1 then + error(msprintf(" maxFalseAlarmRate value must lie in between 0 and 1")) + end + + elseif strcmpi(varargin(i),'w')==0 then + i=i+1; + w=varargin(i); + if h<0 then + error(msprintf(" w value must be positive")) + end + + elseif strcmpi(varargin(i),'h')==0 then + i=i+1; + h=varargin(i); + if h<0 then + error(msprintf(" h value must be positive")) + end + else + error(msprintf(_(" Wrong value for input argument #%d",i))); + end + end + + [nRows noOfPositiveInstances]=size(positiveInstances); + fields=fieldnames(positiveInstances); + fd = mopen('positive.txt','wt'); + for i=1:noOfPositiveInstances + mfprintf(fd,'%s 1',getfield(fields(1),positiveInstances(i))); + boxVals=getfield(fields(2),positiveInstances(i)); + for j=1:4 + mfprintf(fd,' %d',boxVals(j)); + end + mfprintf(fd,'\n'); + end + mclose(fd); + + disp("Creating positive samples:"); + cmd=sprintf("opencv_createsamples -info positive.txt -num%d -vec positive.vec -w %d -h %d",numPos,w,h); + unix_w(cmd); + if isdir(negativeImages) + if getos()=="Linux" + temp=strcat(["ls ",negativeImages]) + elseif getos()=="Windows" + temp=strcat(["dir ",negativeImages]) + end + s=unix_g(temp); + [noOfFilesInFolder noOfCols]=size(s); + fd = mopen('negative.txt','wt'); + for i=1:noOfFilesInFolder + [path,fname,extension]=fileparts(s(i)) + if ~strcmp(extension,".jpg") | ~strcmp(extension,".jpeg") | ~strcmp(extension,".png") | ~strcmp(extension,".bmp") | ~strcmp(extension,".pgm") | ~strcmp(extension,".JPG") | ~strcmp(extension,".JPEG") | ~strcmp(extension,".PNG") | ~strcmp(extension,".BMP") | ~strcmp(extension,".PGM") + mfprintf(fd,'%s/%s\n',negativeImages,s(i)); + end + end + end + disp("Training Cascade:"); + cmd=sprintf("opencv_traincascade -data %s -vec positive.vec -bg negative.txt -numPos %d -numNeg %d -numStages %d -nsplits %d -featureType %s -minHitRate %d -maxFalseAlarmRate %d -w %d -h %d",outputFolder,numPos,numNeg,numStages,nsplits,featureType,minHitRate,maxFalseAlarmRate,w,h); + unix_w(cmd); +endfunction; diff --git a/macros/trainImageCategoryClassifier.bin b/macros/trainImageCategoryClassifier.bin new file mode 100644 index 0000000..01ca87f Binary files /dev/null and b/macros/trainImageCategoryClassifier.bin differ diff --git a/macros/trainImageCategoryClassifier.sci b/macros/trainImageCategoryClassifier.sci new file mode 100644 index 0000000..46ccce8 --- /dev/null +++ b/macros/trainImageCategoryClassifier.sci @@ -0,0 +1,31 @@ +function classifier = trainImageCategoryClassifier(imgSets, bag) +// This function is used to train an image classifier. +// +// Calling Sequence +// classifier = trainImageCategoryClassifier(imgSets, bag) +// +// Parameters +// classifier: Image category classifier +// imgSets: Input imageSet to train the classifier on +// bag: The bagOfFeatures of the imageSet provided +// +// Description +// This function trains an image category classifier which can be used to predict categories of images given to it as input using the predict() function. +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// categoryClassifier = trainImageCategoryClassifier(trainingSet, bag); +// +// Authors +// Rohit Suri +// Umang Agrawal + bag_list = bagStructToList(bag); + + imgSets_list = imageSetToList(imgSets); + temp = opencv_trainImageCategoryClassifier(imgSets_list, bag_list); + + classifier = struct("ClassifierLocation", temp(2), "BagofFeaturesLocation", temp(3), "Description", temp(4)) + +endfunction diff --git a/macros/triangulatePoints.bin b/macros/triangulatePoints.bin new file mode 100644 index 0000000..e7d3d33 Binary files /dev/null and b/macros/triangulatePoints.bin differ diff --git a/macros/triangulatePoints.sci b/macros/triangulatePoints.sci new file mode 100644 index 0000000..5b56061 --- /dev/null +++ b/macros/triangulatePoints.sci @@ -0,0 +1,40 @@ +function [worldPoints] = triangulatePoints(projMat1, projMat2, points1, points2) +// Returns the worldPoint coordinates of the feature points. +// +// Calling Sequence +// [worldPoints] = triangulatePoints(projMat1, projMat2, points1, points2) +// +// Parameters +// projMat1: 3X4 projection matrix for the first Camera matrix +// projMat2: 3X4 projection matrix for the second Camera matrix +// points1: 2xN array of feature points in the 1st image. In case of c++ version it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1 +// points2: 2xN array of feature points in the 2nd image. In case of c++ version it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1 +// worldPoints: 4XN array or matrix of reconstructed points +// +// Description +// Returns 4D location of the matched feature points from the two projected matrices. The 4D location of the feature points is reconstructed using triangulation. +// +// Examples +// [projMat1] = [12 21 21 19; 34 12 0 2; 112 431 890 32.1] +// [projMat2] = [16 17 32 1; 64 90 12 11; 123 43.5 895 9.8] +// [points1] = [1 2 3 4;5 6 7 8] +// [points2] = [32 1 3 5; 9 8 3 4] +// [worldPoints] = triangulatePoints(projMat1, projMat2, points1, points2) +// +// +// Authors +// Deepshikha + +// code for the above function +// + + output = opencv_triangulatePoints(projMat1, projMat2, points1, points2) + + channels = size(output) + + for i = 1:channels + worldPoints(:,:,1) = output(1) + end + +endfunction + diff --git a/macros/undistort.bin b/macros/undistort.bin new file mode 100644 index 0000000..83a9564 Binary files /dev/null and b/macros/undistort.bin differ diff --git a/macros/undistort.sci b/macros/undistort.sci new file mode 100644 index 0000000..53fc8c6 --- /dev/null +++ b/macros/undistort.sci @@ -0,0 +1,7 @@ +function [outputImg]=undistort(inputImage,cameraMatrix,distCoeffActual,newCameraMatrix) + inputList=mattolist(inputImage); + outputList=opencv_undistort(inputList,cameraMatrix,distCoeffActual,newCameraMatrix); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/undistortImage.bin b/macros/undistortImage.bin new file mode 100644 index 0000000..d315543 Binary files /dev/null and b/macros/undistortImage.bin differ diff --git a/macros/undistortImage.sci b/macros/undistortImage.sci new file mode 100644 index 0000000..9fccd42 --- /dev/null +++ b/macros/undistortImage.sci @@ -0,0 +1,68 @@ +function new_image = undistortImage(image, cameramatrix, varargin) +// Transforms an image to compensate for lens distortion. +// +// Calling Sequence +// newimage = undistortImage(image, cameramatrix) +// newimage = undistortImage(image, cameramatrix, ["Parameter1", value1,["Parameter2", value2]]) +// +// Parameters +// image : Distorted input image +// cameramatrix : Input 3 * 3 cameramatrix +// distcoeffs : Input vector of distortion coefficients of 4, 5, or 8 elements. If the vector is empty, the zero distortion coefficients are assumed. +// newCameraMatrix : Camera matrix of the distorted image. By default, it is same as cameramatrix. +// newimage : Corrected image that has same size and type as original image +// +// Description +// The function returns a newimage containing the input image with lens distortion removed. +// +// Examples +// J = undistortImage(I, cameramatrix) +// J = undistortImage(I, cameramatrix, "distcoeffs", distcoeffs) +// +// Authors +// Suraj Prakash + + [ lhs rhs ] = argn(0) + + if lhs > 1 then + error(msprintf("Too many output arguments")) + end + + if rhs > 6 then + error(msprintf("Too many input arguments")) + end + + if rhs < 2 then + error(msprintf("Too less input arguments")) + end + + if modulo(rhs, 2) <> 0 + error(msprintf("Incorrect input arguments")) + end + + image_list = mattolist(image) + + if rhs == 2 then + + temp = opencv_undistortImage(image_list, cameramatrix) + + elseif rhs == 4 then + + temp = opencv_undistortImage(image_list, cameramatrix, varargin(1), varargin(2)) + + elseif rhs == 6 then + + temp = opencv_undistortImage(image_list, cameramatrix, varargin(1), varargin(2), varargin(3), varargin(4)) + + else + error(msprintf("Error in input arguments")) + end + + sz = size(temp) + + for i=1:sz + new_image(:, :, i) = temp(i) + end + + +endfunction diff --git a/macros/undistortPoints.bin b/macros/undistortPoints.bin new file mode 100644 index 0000000..1c7f495 Binary files /dev/null and b/macros/undistortPoints.bin differ diff --git a/macros/undistortPoints.sci b/macros/undistortPoints.sci new file mode 100644 index 0000000..39b13d5 --- /dev/null +++ b/macros/undistortPoints.sci @@ -0,0 +1,37 @@ +function [idealPoints] = undistortPoints(observedPoints, camMat, disCoefMat, rectMat, newCamMat) +// Returns the ideal point coordinates from the observed point coordinates +// +// Calling Sequence +// [idealPoints] = undistortPoints(observedPoints, camMat, disCoefMat, rectMat, newCamMat) +// +// Parameters +// observedPoints: 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2). +// camMat: \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} +// disCoefMat: Input vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +// rectMat: Rectification transformation in the object space (3x3 matrix). If the matrix is empty, the identity transformation is used. +//newCamMat: New camera matrix (3x3) or new projection matrix (3x4. If the matrix is empty, the identity new camera matrix is used. +// idealPoints: ideal point coordinates matrix. If matrix newCamMat is identity or omitted, idealPoints will contain normalized point coordinates. +// +// Description +// Returns the ideal points coordinates from the observed point coordinates after undistortion and reverse perpective transformation. +// +// Examples +// [observedPoints] = [1 2 3 4; 4 3 2 1] +// [camMat] = [450 0 231; 0 876.3 87.1; 0 0 1] +// [disCoefMat] = [3 2 0 9] +// [rectMat] = [1 0 0; 0 1 0; 0 0 1] +// [newCamMat] = [] +// [idealPoints] = undistortPoints(observedPoints, camMat, disCoefMat, rectMat, newCamMat) +// +// Authors +// Deepshikha + + output = opencv_undistortPoints(srcMat, camMat, disCoefMat, rectMat, newCamMat) + + channels = size(output) + + for i = 1:channels // for i channel image + idealPoints(:,:,i) = output(i) + end + +endfunction diff --git a/macros/viscircle.bin b/macros/viscircle.bin new file mode 100644 index 0000000..e02aa39 Binary files /dev/null and b/macros/viscircle.bin differ diff --git a/macros/viscircle.sci b/macros/viscircle.sci new file mode 100644 index 0000000..b84e7e5 --- /dev/null +++ b/macros/viscircle.sci @@ -0,0 +1,27 @@ +function [out]=viscircles(input1,input2,input3,varargin) + inputn=mattolist(input1); + [lhs rhs] = argn(0) + if rhs>5 then + error(msprintf("Too many input arguments")) + elseif rhs==5 then + a=opencv_viscircles(inputn,input2,input3,varargin(1),varargin(2)); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + elseif rhs==4 then + a=opencv_viscircles(inputn,input2,input3,varargin(1)); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + else + a=opencv_viscircles(inputn,input2,input3); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end + end, + +endfunction; + diff --git a/macros/warpaffine.bin b/macros/warpaffine.bin new file mode 100644 index 0000000..b9a7aef Binary files /dev/null and b/macros/warpaffine.bin differ diff --git a/macros/warpaffine.sci b/macros/warpaffine.sci new file mode 100644 index 0000000..9067959 --- /dev/null +++ b/macros/warpaffine.sci @@ -0,0 +1,8 @@ +function [out]=warpaffine(src,M,size_x,size_y ,flag,borderMode) + image1=mattolist(src); + a=opencv_warpaffine(image1,M,size_x,size_y ,flag, borderMode); + dimension=size(a) + for i = 1:dimension + out(:,:,i)=a(i); + end +endfunction; diff --git a/macros/watershed.bin b/macros/watershed.bin new file mode 100644 index 0000000..8bdca62 Binary files /dev/null and b/macros/watershed.bin differ diff --git a/macros/watershed.sci b/macros/watershed.sci new file mode 100644 index 0000000..6291739 --- /dev/null +++ b/macros/watershed.sci @@ -0,0 +1,11 @@ +function[dstImg] = watershed(srcImg) + + srcMat = mattolist(srcImg) + out = opencv_watershed(srcMat) + + channels = size(out) + + for i = 1 : channels + dstImg(:,:,i) = out(i) + end +endfunction diff --git a/macros/whitepoint.bin b/macros/whitepoint.bin new file mode 100644 index 0000000..e312c99 Binary files /dev/null and b/macros/whitepoint.bin differ diff --git a/macros/whitepoint.sci b/macros/whitepoint.sci new file mode 100644 index 0000000..6f5d8af --- /dev/null +++ b/macros/whitepoint.sci @@ -0,0 +1,10 @@ +function[xyz] = whitepoint(input_string) + + out = whitepoint(input_string) + + channels = size(out) + + for i = 1:channels + xyz(:,:,1) = out(1) + end +endfunction diff --git a/macros/wiener2.bin b/macros/wiener2.bin new file mode 100644 index 0000000..1ee5316 Binary files /dev/null and b/macros/wiener2.bin differ diff --git a/macros/wiener2.sci b/macros/wiener2.sci new file mode 100644 index 0000000..cde19cd --- /dev/null +++ b/macros/wiener2.sci @@ -0,0 +1,7 @@ +function [outputImg]=wiener2(inputImage,filtsize,sigma) + inputList=mattolist(inputImage); + outputList=opencv_wiener2(inputList,filtsize,sigma); + for i=1:size(outputList) + outputImg(:,:,i)=outputList(i) + end +endfunction diff --git a/macros/xyz2double.bin b/macros/xyz2double.bin new file mode 100644 index 0000000..c2ccf14 Binary files /dev/null and b/macros/xyz2double.bin differ diff --git a/macros/xyz2double.sci b/macros/xyz2double.sci new file mode 100644 index 0000000..d03c1e9 --- /dev/null +++ b/macros/xyz2double.sci @@ -0,0 +1,7 @@ +function [output] = xyz2double(pstData) + a = opencv_xyz2double(pstData); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/xyz2lab.bin b/macros/xyz2lab.bin new file mode 100644 index 0000000..7a0780d Binary files /dev/null and b/macros/xyz2lab.bin differ diff --git a/macros/xyz2lab.sci b/macros/xyz2lab.sci new file mode 100644 index 0000000..1654604 --- /dev/null +++ b/macros/xyz2lab.sci @@ -0,0 +1,21 @@ +function [output] = xyz2lab(vartype,varargin) + [lhs rhs] = argn(0); + if(rhs>3) + error(msprintf("Too many input argumnets")); + end + + select rhs + case 1 then + a = opencv_xyz2lab(vartype); + case 2 then + a = opencv_xyz2lab(vartype,varargin(0)); + case 3 then + a = opencv_xyz2lab(vartype,varargin(0),varargin(1)); + end + + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction + diff --git a/macros/xyz2rgb.bin b/macros/xyz2rgb.bin new file mode 100644 index 0000000..980d866 Binary files /dev/null and b/macros/xyz2rgb.bin differ diff --git a/macros/xyz2rgb.sci b/macros/xyz2rgb.sci new file mode 100644 index 0000000..d557939 --- /dev/null +++ b/macros/xyz2rgb.sci @@ -0,0 +1,8 @@ +function [output] = xyz2rgb(img) + image = mattolist(img); + a = opencv_xyz2rgb(image); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/macros/xyz2uint16.bin b/macros/xyz2uint16.bin new file mode 100644 index 0000000..c9b3aee Binary files /dev/null and b/macros/xyz2uint16.bin differ diff --git a/macros/xyz2uint16.sci b/macros/xyz2uint16.sci new file mode 100644 index 0000000..12e07b2 --- /dev/null +++ b/macros/xyz2uint16.sci @@ -0,0 +1,7 @@ +function [output] = xyz2uint16(pstData) + a = opencv_xyz2uint16(pstData); + d = size(a); + for i=1:d + output(:,:,i) = a(i); + end +endfunction diff --git a/sci_gateway/builder_gateway.sce b/sci_gateway/builder_gateway.sce new file mode 100644 index 0000000..654d1e4 --- /dev/null +++ b/sci_gateway/builder_gateway.sce @@ -0,0 +1,13 @@ +function builder_gateway() + + sci_gateway_dir = get_absolute_file_path("builder_gateway.sce"); + languages = ["cpp"]; + + tbx_builder_gateway_lang(languages,sci_gateway_dir); + tbx_build_gateway_loader(languages,sci_gateway_dir); + tbx_build_gateway_clean(languages,sci_gateway_dir); + + endfunction + + builder_gateway() + clear builder_gateway; // remove builder_gateway on stack diff --git a/sci_gateway/cleaner_gateway.sce b/sci_gateway/cleaner_gateway.sce new file mode 100644 index 0000000..03aedaf --- /dev/null +++ b/sci_gateway/cleaner_gateway.sce @@ -0,0 +1,15 @@ +// This file is released under the 3-clause BSD license. See COPYING-BSD. +// Generated by builder_gateway.sce: Please, do not edit this file + +sci_gateway_dir = get_absolute_file_path("cleaner_gateway.sce"); +current_dir = pwd(); + +chdir(sci_gateway_dir); +if ( isdir("cpp") ) then + chdir("cpp"); + exec("cleaner.sce"); + mdelete("cleaner.sce"); +end + +chdir(current_dir); +clear sci_gateway_dir current_dir; diff --git a/sci_gateway/cpp/builder_gateway_cpp.sce b/sci_gateway/cpp/builder_gateway_cpp.sce new file mode 100644 index 0000000..1f16d47 --- /dev/null +++ b/sci_gateway/cpp/builder_gateway_cpp.sce @@ -0,0 +1,364 @@ +// This file is released under the 3-clause BSD license. See COPYING-BSD. + + +mode(-1) +lines(0) + +toolbox_title = "FOSSEE_Image_Processing_Toolbox"; + +Build_64Bits = %t; + + +path_builder = get_absolute_file_path('builder_gateway_cpp.sce'); +Names = [ +// "raw_dctmtx", "dctmtx"; +// "raw_multithresh", "multithresh"; +// "raw_applycform", "opencv_applycform"; + //"raw_arrowedline", "opencv_arrowedline"; + "raw_blur", "opencv_blur"; +// "raw_boundingRect", "opencv_boundingRect"; +// "raw_bwconvhull", "opencv_bwconvhull"; +// "raw_bwdistgeodesic", "opencv_bwdistgeodesic"; +// "raw_bwlookup", "opencv_bwlookup"; +// "raw_canny", "opencv_canny"; + "raw_circle", "opencv_circle"; +// "raw_clipline", "opencv_clipline"; +// "raw_convexhull", "opencv_convexhull"; +// "raw_convmtx2", "opencv_convmtx2"; +// "raw_copymakeborder", "opencv_copymakeborder"; +// "raw_corner", "opencv_corner"; +// "raw_cornerEigenValsAndVecs", "opencv_cornerEigenValsAndVecs"; +// "raw_cornerHarris", "opencv_cornerHarris"; +// "raw_cornerMinEigenVal", "opencv_cornerMinEigenVal"; +// "raw_cvtColor", "opencv_cvtColor"; +// "raw_demosaic", "opencv_demosaic"; + "raw_dilate", "opencv_dilate"; +// "raw_ellipse", "opencv_ellipse"; +// "raw_ellipse2poly", "opencv_ellipse2poly"; + "raw_erode", "opencv_erode"; +// "raw_fftshift", "opencv_fftshift"; +// "raw_fillconvexpoly", "opencv_fillconvexpoly"; +// "raw_filter2D", "opencv_filter2D"; +// "raw_findContours", "opencv_findContours"; +// "raw_fsamp2", "opencv_fsamp2"; + +// "raw_gabor", "opencv_gabor"; +// "raw_gaussianblur", "opencv_gaussianblur"; +// "raw_getStructuringElement", "opencv_getStructuringElement"; +// "getTextSize", "opencv_getTextSize"; +// "raw_getgaussiankernel", "opencv_getgaussiankernel"; +// "raw_getrectsubpix", "opencv_getrectsubpix"; +// "raw_getrotationmatrix2D", "opencv_getrotationmatrix2D"; +// "raw_goodfeaturestotrack", "opencv_goodfeaturestotrack"; + +// "raw_houghcircles", "opencv_houghcircles"; +// "raw_houghlines", "opencv_houghlines"; +// "raw_houghlinesp", "opencv_houghlinesp"; +// "raw_ifftshift", "opencv_ifftshift"; +// "raw_im2double", "opencv_im2double"; +// "raw_imabsdiff", "opencv_imabsdiff"; +// "raw_imadd", "opencv_imadd"; +// "raw_imattributes", "opencv_imattributes"; +// "raw_imboxfilt3", "opencv_imboxfilt3"; +// "raw_imcomplement", "opencv_imcomplement"; +// "raw_imcontour", "opencv_imcontour"; +// "raw_imcontrast", "opencv_imcontrast"; + "raw_imcrop", "opencv_imcrop"; +// "raw_imdivide", "opencv_imdivide"; +// "raw_imextendedmax", "opencv_imextendedmax"; +// "raw_imextendedmin", "opencv_imextendedmin"; +// "raw_imfill", "opencv_imfill"; +// "raw_imfindcircles", "opencv_imfindcircles"; +// "raw_imfuse", "opencv_imfuse"; +// "raw_imgaborfilt", "opencv_imgaborfilt"; +// "raw_imgaussfilt3", "opencv_imgaussfilt3"; +// "raw_imguidedfilter", "opencv_imguidedfilter"; +// "raw_imhmax", "opencv_imhmax"; +// "raw_imhmin", "opencv_imhmin"; +// "raw_imimposemin", "opencv_imimposemin"; +// "raw_imlincomb", "opencv_imlincomb"; +// "raw_immultiply", "opencv_immultiply"; + "raw_impixel", "opencv_impixel"; +// "raw_impyramid", "opencv_impyramid"; + "raw_imread", "opencv_imread"; +// "raw_imrect", "opencv_imrect"; + "raw_imresize", "opencv_imresize"; + "raw_imsharpen", "opencv_imsharpen"; +// "raw_imshowpair", "opencv_imshowpair"; +// "raw_imsubtract", "opencv_imsubtract"; +// "raw_imwarp", "opencv_imwarp"; + "raw_imwrite", "opencv_imwrite"; +// "raw_ind2gray", "opencv_ind2gray"; +// "raw_ind2rgb", "opencv_ind2rgb"; +// "raw_lab2double", "opencv_lab2double"; +// "raw_lab2rgb", "opencv_lab2rgb"; +// "raw_lab2uint16", "opencv_lab2uint16"; +// "raw_lab2uint8", "opencv_lab2uint8"; +// "raw_lab2xyz", "opencv_lab2xyz"; +// "raw_laplacian", "opencv_laplacian"; +// "raw_line", "opencv_line"; + +// "raw_medianblur", "opencv_medianblur"; +// "raw_montage", "opencv_montage"; +// "raw_morphologyEx", "opencv_morphologyEx"; +// "raw_ntsc2rgb", "opencv_ntsc2rgb"; +// "raw_puttext", "opencv_puttext"; +// "raw_pyrDown", "opencv_pyrDown"; +// "raw_pyrUp", "opencv_pyrUp"; +// "raw_rectangle", "opencv_rectangle"; +// "raw_regionfill", "opencv_regionfill"; +// "raw_rgb2lab", "opencv_rgb2lab"; +// "raw_rgb2ntsc", "opencv_rgb2ntsc"; +// "raw_rgb2xyz", "opencv_rgb2xyz"; +// "raw_roifill", "opencv_roifill"; +// "raw_roipoly", "opencv_roipoly"; +// "raw_scharr", "opencv_scharr"; +// "raw_sepFilter2D", "opencv_sepFilter2D"; +// "raw_sobel", "opencv_sobel"; +// "raw_ssim", "opencv_ssim"; +// "raw_threshold", "opencv_threshold"; +// "raw_undistort", "opencv_undistort"; +// "raw_viscircles", "opencv_viscircles"; +// "raw_watershed", "opencv_watershed"; +// "raw_whitepoint", "opencv_whitepoint"; +// "raw_wiener2", "opencv_wiener2"; +// "raw_xyz2double", "opencv_xyz2double"; +// "raw_xyz2lab", "opencv_xyz2lab"; +// "raw_xyz2rgb", "opencv_xyz2rgb"; +// "raw_xyz2uint16", "opencv_xyz2uint16"; + +// "deconvlucy", "opencv_deconvlucy"; +// "imhistmatch", "opencv_imhistmatch"; +// "graycoprops", "opencv_graycoprops"; +// "graydiffweight", "opencv_graydiffweight"; +// "decorrstretch", "opencv_decorrstretch"; +// "adaptf", "opencv_adapthisteq"; +// "affine2d", "opencv_affine2d"; + + +// "approxpolyDP","approxpolyDP"; +// "arclenght","arclenght"; +// "bilateralfilter","bilateralfilter"; +// "borderInterpolate","borderInterpolate"; +// "boxfilter","boxfilter"; +// "contourarea","contourarea"; +// "boxfilter","boxfilter"; +// "fitellipse","fitellipse"; +// "getkerneltype","getkerneltype"; +// "histeq","histeq"; + "raw_imrotate","opencv_imrotate"; +// "mean1","mean1"; +// "minAreaRect","minAreaRect"; +// "minimumenclosingcirlce","minimumenclosingcirlce"; +// "pyrMeanShiftFiltering","pyrMeanShiftFiltering"; + "raw_rgb2gray","opencv_rgb2gray"; +// "warpaffine","warpaffine"; + + + + ]; + +Files = [ +// "dctmtx.cpp", + +// "multithresh.cpp", +// "opencv_applycform.cpp", + //"opencv_arrowedline.cpp", + "opencv_blur.cpp", +// "opencv_boundingRect.cpp", +// "opencv_bwconvhull.cpp", +// "opencv_bwdistgeodesic.cpp", +// "opencv_bwlookup.cpp", +// "opencv_canny.cpp", + "opencv_circle.cpp", +// "opencv_clipline.cpp", +// "opencv_convexhull.cpp", +// "opencv_convmtx2.cpp", +// "opencv_copymakeborder.cpp", +// "opencv_corner.cpp", +// "opencv_cornerEigenValsAndVecs.cpp", +// "opencv_cornerHarris.cpp", +// "opencv_cornerMinEigenVal.cpp", +// "opencv_cvtColor.cpp", +// "opencv_demosaic.cpp", + "opencv_dilate.cpp", + "opencv_ellipse.cpp", +// "opencv_ellipse2poly.cpp", + "opencv_erode.cpp", +// "opencv_fftshift.cpp", +// "opencv_fillconvexpoly.cpp", +// "opencv_filter2D.cpp", +// "opencv_findContours.cpp", +// "opencv_fsamp2.cpp", + +// "opencv_gabor.cpp", +// "opencv_gaussianblur.cpp", +// "opencv_getStructuringElement.cpp", +// "opencv_getTextSize.cpp", +// "opencv_getgaussiankernel.cpp", +// "opencv_getrectsubpix.cpp", +// "opencv_getrotationmatrix2D.cpp", +// "opencv_goodfeaturestotrack.cpp", + +// "opencv_houghcircles.cpp", +// "opencv_houghlines.cpp", +// "opencv_houghlinesp.cpp", +// "opencv_ifftshift.cpp", +// "opencv_im2double.cpp", +// "opencv_imabsdiff.cpp", +// "opencv_imadd.cpp", +// "opencv_imattributes.cpp", +// "opencv_imboxfilt3.cpp", +// "opencv_imcomplement.cpp", +// "opencv_imcontour.cpp", +// "opencv_imcontrast.cpp", + "opencv_imcrop.cpp", +// "opencv_imdivide.cpp", +// "opencv_imextendedmax.cpp", +// "opencv_imextendedmin.cpp", +// "opencv_imfill.cpp", +// "opencv_imfindcircles.cpp", +// "opencv_imfuse.cpp", +// "opencv_imgaborfilt.cpp", +// "opencv_imgaussfilt3.cpp", +// "opencv_imguidedfilter.cpp", +// "opencv_imhmax.cpp", +// "opencv_imhmin.cpp", +// "opencv_imimposemin.cpp", +// "opencv_imlincomb.cpp", +// "opencv_immultiply.cpp", + "opencv_impixel.cpp", +// "opencv_impyramid.cpp", + "opencv_imread.cpp", +// "opencv_imrect.cpp", + "opencv_imresize.cpp", + "opencv_imsharpen.cpp", +// "opencv_imshowpair.cpp", +// "opencv_imsubtract.cpp", +// "opencv_imwarp.cpp", + "opencv_imwrite.cpp", +// "opencv_ind2gray.cpp", +// "opencv_ind2rgb.cpp", +// "opencv_lab2double.cpp", +// "opencv_lab2rgb.cpp", +// "opencv_lab2uint16.cpp", +// "opencv_lab2uint8.cpp", +// "opencv_lab2xyz.cpp", +// "opencv_laplacian.cpp", +// "opencv_line.cpp", + +// "opencv_medianblur.cpp", +// "opencv_montage.cpp", +// "opencv_morphologyEx.cpp", +// "opencv_ntsc2rgb.cpp", +// "opencv_puttext.cpp", +// "opencv_pyrDown.cpp", +// "opencv_pyrUp.cpp", +// "opencv_rectangle.cpp", +// "opencv_regionfill.cpp", +// "opencv_rgb2lab.cpp", +// "opencv_rgb2ntsc.cpp", +// "opencv_rgb2xyz.cpp", +// "opencv_roifill.cpp", +// "opencv_roipoly.cpp", +// "opencv_scharr.cpp", +// "opencv_sepFilter2D.cpp", +// "opencv_sobel.cpp", +// "opencv_ssim.cpp", +// "opencv_threshold.cpp", +// "opencv_undistort.cpp", +// "opencv_viscircles.cpp", +// "opencv_watershed.cpp", +// "opencv_whitepoint.cpp", +// "opencv_wiener2.cpp", +// "opencv_xyz2double.cpp", +// "opencv_xyz2lab.cpp", +// "opencv_xyz2rgb.cpp", +// "opencv_xyz2uint16.cpp", + + +// "opencv_deconvlucy.cpp", +// "opencv_integralImage.cpp", +// "opencv_imhistmatch.cpp", +// "opencv_graycoprops.cpp", +// "opencv_graydiffweight.cpp", +// "opencv_decorrstretch.cpp", +// "opencv_adapthisteq.cpp", +// "opencv_affine2d.cpp", + + "rgb2gray.cpp", +// "warpaffine.cpp", +// "opencv_pyrMeanShiftFiltering.cpp", +// "opencv_minimumenclosingcirlce.cpp", +// "opencv_minAreaRect.cpp", +// "opencv_mean1.cpp", + "opencv_imrotate.cpp", +// "opencv_getkerneltype.cpp", +// "opencv_fitellipse.cpp", +// "opencv_contourarea.cpp", +// "opencv_boxfilter.cpp", +// "opencv_borderInterpolate.cpp", +// "opencv_bilateralfilter.cpp", +// "opencv_arclength.cpp", +// "opencv_approxpolyDP.cpp", +// "opencv_histeq.cpp", + + + + ]; + + copyfile("/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/sci_gateway/cpp/common.h",TMPDIR); + copyfile("/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/sci_gateway/cpp/common.cpp",TMPDIR); + + [a, opt] = getversion(); + Version = opt(2); + + if getos()=="Windows" then +// third_dir = path_builder+filesep()+'..'+filesep()+'..'+filesep()+'thirdparty'; +// lib_base_dir = third_dir + filesep() + 'windows' + filesep() + 'lib' + filesep() + Version + filesep(); +// inc_base_dir = third_dir + filesep() + 'windows' + filesep() + 'include' + filesep() + 'coin'; +// C_Flags=['-D__USE_DEPRECATED_STACK_FUNCTIONS__ -w -I '+path_builder+' '+ '-I '+inc_base_dir+' '] +// Linker_Flag = [lib_base_dir+"libClp.lib "+lib_base_dir+"libCgl.lib "+lib_base_dir+"libOsi.lib "+lib_base_dir+"libOsiClp.lib "+lib_base_dir+"libCoinUtils.lib "+lib_base_dir+"libSymphony.lib "+lib_base_dir+"IpOptFSS.lib "+lib_base_dir+"IpOpt-vc10.lib "] + +else + third_dir = path_builder+'..'+filesep()+'..'+filesep()+'thirdparty'; + lib_base_dir = third_dir + filesep() + 'linux' + filesep() + 'lib' + filesep() + Version + filesep(); + inc_base_dir = third_dir + filesep() + 'linux' + filesep() + 'include' + filesep() + 'opencv'; + incl_base_dir = third_dir + filesep() + 'linux' + filesep() + 'include' + filesep() + 'opencv2'; + +// C_Flags=["-D__USE_DEPRECATED_STACK_FUNCTIONS__ -w -fpermissive -I"+path_builder+"-I"+inc_base_dir+" "+"-I"+incl_base_dir+" -Wl,-rpath= "+lib_base_dir+" "+"-L"+lib_base_dir+"libopencv_core"+" "+"-L"+lib_base_dir+"libopencv_highgui"+" "+"-L"+lib_base_dir+"libopencv_imgproc" ] + +// export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:"+lib_base_dir +// unix_s("export LD_LIBRARY_PATH="+lib_base_dir); + +// C_Flags=["-I"+inc_base_dir+" "+"-I"+incl_base_dir]; +// Linker_Flag = ["-L"+lib_base_dir+"libopencv_core"+" "+"-L"+lib_base_dir+"libopencv_highgui"+" "+"-L"+lib_base_dir+"libopencv_imgproc" ]; + +// Linker_Flag = ["-L/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/thirdparty/linux/lib/x64 -lopencv_core -L/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/thirdparty/linux/lib/x64 -lopencv_imgproc -L/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/thirdparty/linux/lib/x64 -lopencv_highgui" ] + + +tools_path = path_builder + "../../thirdparty/linux/"; +unix_s("export LD_LIBRARY_PATH="+tools_path+"lib/"+Version+filesep()+" "); +C_Flags=["-w -fpermissive -I"+tools_path+"include/opencv -I"+tools_path+"include/opencv2 -Wl,-rpath="+tools_path+"lib/"+Version+filesep()+" "] + +Linker_Flag = ["-L"+tools_path+"lib/"+Version+"-lopencv_shape -lopencv_stitching -lopencv_objdetect -lopencv_superres -lopencv_videostab -lopencv_calib3d -lopencv_features2d -lopencv_highgui -lopencv_video -lopencv_photo -lopencv_ml -lopencv_imgproc -lopencv_flann -lopencv_core "] + +end + +tbx_build_gateway(toolbox_title,Names,Files,get_absolute_file_path("builder_gateway_cpp.sce"), [], Linker_Flag, C_Flags,"g++"); + +clear toolbox_title Names Files Linker_Flag C_Flags; + +//function builder_gw_cpp() +// WITHOUT_AUTO_PUTLHSVAR = %t; +// copyfile("/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/sci_gateway/cpp/common.h",TMPDIR); +// copyfile("/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/sci_gateway/cpp/common.cpp",TMPDIR); +//tbx_build_gateway("skeleton_imageprocessing", .. +// names, .. +// files, .. +// get_absolute_file_path("builder_gateway_cpp.sce"),[],["-L/usr/local/lib -lopencv_core -L/usr/local/lib -lopencv_highgui -L/usr/lib -lopencv_imgproc -L/usr/lib -lopencv_calib3d -L/usr/lib -lopencv_video -L/usr/lib -lopencv_nonfree -L/usr/lib -lopencv_ml -L/usr/lib -lopencv_objdetect"],["-I/usr/include/scilab -I/usr/include/opencv -I/usr/include/opencv2 "],"g++ "); +//endfunction + +//builder_gw_cpp(); +//clear builder_gw_cpp; // remove builder_gw_cpp on stack + diff --git a/sci_gateway/cpp/builder_gateway_cpp.sce~ b/sci_gateway/cpp/builder_gateway_cpp.sce~ new file mode 100644 index 0000000..c736288 --- /dev/null +++ b/sci_gateway/cpp/builder_gateway_cpp.sce~ @@ -0,0 +1,364 @@ +// This file is released under the 3-clause BSD license. See COPYING-BSD. + + +mode(-1) +lines(0) + +toolbox_title = "FOSSEE_Image_Processing_Toolbox"; + +Build_64Bits = %t; + + +path_builder = get_absolute_file_path('builder_gateway_cpp.sce'); +Names = [ +// "raw_dctmtx", "dctmtx"; +// "raw_multithresh", "multithresh"; +// "raw_applycform", "opencv_applycform"; + //"raw_arrowedline", "opencv_arrowedline"; + "raw_blur", "opencv_blur"; +// "raw_boundingRect", "opencv_boundingRect"; +// "raw_bwconvhull", "opencv_bwconvhull"; +// "raw_bwdistgeodesic", "opencv_bwdistgeodesic"; +// "raw_bwlookup", "opencv_bwlookup"; +// "raw_canny", "opencv_canny"; + "raw_circle", "opencv_circle"; +// "raw_clipline", "opencv_clipline"; +// "raw_convexhull", "opencv_convexhull"; +// "raw_convmtx2", "opencv_convmtx2"; +// "raw_copymakeborder", "opencv_copymakeborder"; +// "raw_corner", "opencv_corner"; +// "raw_cornerEigenValsAndVecs", "opencv_cornerEigenValsAndVecs"; +// "raw_cornerHarris", "opencv_cornerHarris"; +// "raw_cornerMinEigenVal", "opencv_cornerMinEigenVal"; +// "raw_cvtColor", "opencv_cvtColor"; +// "raw_demosaic", "opencv_demosaic"; + "raw_dilate", "opencv_dilate"; +// "raw_ellipse", "opencv_ellipse"; +// "raw_ellipse2poly", "opencv_ellipse2poly"; + "raw_erode", "opencv_erode"; +// "raw_fftshift", "opencv_fftshift"; +// "raw_fillconvexpoly", "opencv_fillconvexpoly"; +// "raw_filter2D", "opencv_filter2D"; +// "raw_findContours", "opencv_findContours"; +// "raw_fsamp2", "opencv_fsamp2"; + +// "raw_gabor", "opencv_gabor"; +// "raw_gaussianblur", "opencv_gaussianblur"; +// "raw_getStructuringElement", "opencv_getStructuringElement"; +// "getTextSize", "opencv_getTextSize"; +// "raw_getgaussiankernel", "opencv_getgaussiankernel"; +// "raw_getrectsubpix", "opencv_getrectsubpix"; +// "raw_getrotationmatrix2D", "opencv_getrotationmatrix2D"; +// "raw_goodfeaturestotrack", "opencv_goodfeaturestotrack"; + +// "raw_houghcircles", "opencv_houghcircles"; +// "raw_houghlines", "opencv_houghlines"; +// "raw_houghlinesp", "opencv_houghlinesp"; +// "raw_ifftshift", "opencv_ifftshift"; +// "raw_im2double", "opencv_im2double"; +// "raw_imabsdiff", "opencv_imabsdiff"; +// "raw_imadd", "opencv_imadd"; +// "raw_imattributes", "opencv_imattributes"; +// "raw_imboxfilt3", "opencv_imboxfilt3"; +// "raw_imcomplement", "opencv_imcomplement"; +// "raw_imcontour", "opencv_imcontour"; +// "raw_imcontrast", "opencv_imcontrast"; + "raw_imcrop", "opencv_imcrop"; +// "raw_imdivide", "opencv_imdivide"; +// "raw_imextendedmax", "opencv_imextendedmax"; +// "raw_imextendedmin", "opencv_imextendedmin"; +// "raw_imfill", "opencv_imfill"; +// "raw_imfindcircles", "opencv_imfindcircles"; +// "raw_imfuse", "opencv_imfuse"; +// "raw_imgaborfilt", "opencv_imgaborfilt"; +// "raw_imgaussfilt3", "opencv_imgaussfilt3"; +// "raw_imguidedfilter", "opencv_imguidedfilter"; +// "raw_imhmax", "opencv_imhmax"; +// "raw_imhmin", "opencv_imhmin"; +// "raw_imimposemin", "opencv_imimposemin"; +// "raw_imlincomb", "opencv_imlincomb"; +// "raw_immultiply", "opencv_immultiply"; + "raw_impixel", "opencv_impixel"; +// "raw_impyramid", "opencv_impyramid"; + "raw_imread", "opencv_imread"; +// "raw_imrect", "opencv_imrect"; + "raw_imresize", "opencv_imresize"; + "raw_imsharpen", "opencv_imsharpen"; +// "raw_imshowpair", "opencv_imshowpair"; +// "raw_imsubtract", "opencv_imsubtract"; +// "raw_imwarp", "opencv_imwarp"; + "raw_imwrite", "opencv_imwrite"; +// "raw_ind2gray", "opencv_ind2gray"; +// "raw_ind2rgb", "opencv_ind2rgb"; +// "raw_lab2double", "opencv_lab2double"; +// "raw_lab2rgb", "opencv_lab2rgb"; +// "raw_lab2uint16", "opencv_lab2uint16"; +// "raw_lab2uint8", "opencv_lab2uint8"; +// "raw_lab2xyz", "opencv_lab2xyz"; +// "raw_laplacian", "opencv_laplacian"; +// "raw_line", "opencv_line"; + +// "raw_medianblur", "opencv_medianblur"; +// "raw_montage", "opencv_montage"; +// "raw_morphologyEx", "opencv_morphologyEx"; +// "raw_ntsc2rgb", "opencv_ntsc2rgb"; +// "raw_puttext", "opencv_puttext"; +// "raw_pyrDown", "opencv_pyrDown"; +// "raw_pyrUp", "opencv_pyrUp"; +// "raw_rectangle", "opencv_rectangle"; +// "raw_regionfill", "opencv_regionfill"; +// "raw_rgb2lab", "opencv_rgb2lab"; +// "raw_rgb2ntsc", "opencv_rgb2ntsc"; +// "raw_rgb2xyz", "opencv_rgb2xyz"; +// "raw_roifill", "opencv_roifill"; +// "raw_roipoly", "opencv_roipoly"; +// "raw_scharr", "opencv_scharr"; +// "raw_sepFilter2D", "opencv_sepFilter2D"; +// "raw_sobel", "opencv_sobel"; +// "raw_ssim", "opencv_ssim"; +// "raw_threshold", "opencv_threshold"; +// "raw_undistort", "opencv_undistort"; +// "raw_viscircles", "opencv_viscircles"; +// "raw_watershed", "opencv_watershed"; +// "raw_whitepoint", "opencv_whitepoint"; +// "raw_wiener2", "opencv_wiener2"; +// "raw_xyz2double", "opencv_xyz2double"; +// "raw_xyz2lab", "opencv_xyz2lab"; +// "raw_xyz2rgb", "opencv_xyz2rgb"; +// "raw_xyz2uint16", "opencv_xyz2uint16"; + +// "deconvlucy", "opencv_deconvlucy"; +// "imhistmatch", "opencv_imhistmatch"; +// "graycoprops", "opencv_graycoprops"; +// "graydiffweight", "opencv_graydiffweight"; +// "decorrstretch", "opencv_decorrstretch"; +// "adaptf", "opencv_adapthisteq"; +// "affine2d", "opencv_affine2d"; + + +// "approxpolyDP","approxpolyDP"; +// "arclenght","arclenght"; +// "bilateralfilter","bilateralfilter"; +// "borderInterpolate","borderInterpolate"; +// "boxfilter","boxfilter"; +// "contourarea","contourarea"; +// "boxfilter","boxfilter"; +// "fitellipse","fitellipse"; +// "getkerneltype","getkerneltype"; +// "histeq","histeq"; + "raw_imrotate","opencv_imrotate"; +// "mean1","mean1"; +// "minAreaRect","minAreaRect"; +// "minimumenclosingcirlce","minimumenclosingcirlce"; +// "pyrMeanShiftFiltering","pyrMeanShiftFiltering"; + "raw_rgb2gray","opencv_rgb2gray"; +// "warpaffine","warpaffine"; + + + + ]; + +Files = [ +// "dctmtx.cpp", + +// "multithresh.cpp", +// "opencv_applycform.cpp", + //"opencv_arrowedline.cpp", + "opencv_blur.cpp", +// "opencv_boundingRect.cpp", +// "opencv_bwconvhull.cpp", +// "opencv_bwdistgeodesic.cpp", +// "opencv_bwlookup.cpp", +// "opencv_canny.cpp", + "opencv_circle.cpp", +// "opencv_clipline.cpp", +// "opencv_convexhull.cpp", +// "opencv_convmtx2.cpp", +// "opencv_copymakeborder.cpp", +// "opencv_corner.cpp", +// "opencv_cornerEigenValsAndVecs.cpp", +// "opencv_cornerHarris.cpp", +// "opencv_cornerMinEigenVal.cpp", +// "opencv_cvtColor.cpp", +// "opencv_demosaic.cpp", + "opencv_dilate.cpp", + "opencv_ellipse.cpp", +// "opencv_ellipse2poly.cpp", + "opencv_erode.cpp", +// "opencv_fftshift.cpp", +// "opencv_fillconvexpoly.cpp", +// "opencv_filter2D.cpp", +// "opencv_findContours.cpp", +// "opencv_fsamp2.cpp", + +// "opencv_gabor.cpp", +// "opencv_gaussianblur.cpp", +// "opencv_getStructuringElement.cpp", +// "opencv_getTextSize.cpp", +// "opencv_getgaussiankernel.cpp", +// "opencv_getrectsubpix.cpp", +// "opencv_getrotationmatrix2D.cpp", +// "opencv_goodfeaturestotrack.cpp", + +// "opencv_houghcircles.cpp", +// "opencv_houghlines.cpp", +// "opencv_houghlinesp.cpp", +// "opencv_ifftshift.cpp", +// "opencv_im2double.cpp", +// "opencv_imabsdiff.cpp", +// "opencv_imadd.cpp", +// "opencv_imattributes.cpp", +// "opencv_imboxfilt3.cpp", +// "opencv_imcomplement.cpp", +// "opencv_imcontour.cpp", +// "opencv_imcontrast.cpp", + "opencv_imcrop.cpp", +// "opencv_imdivide.cpp", +// "opencv_imextendedmax.cpp", +// "opencv_imextendedmin.cpp", +// "opencv_imfill.cpp", +// "opencv_imfindcircles.cpp", +// "opencv_imfuse.cpp", +// "opencv_imgaborfilt.cpp", +// "opencv_imgaussfilt3.cpp", +// "opencv_imguidedfilter.cpp", +// "opencv_imhmax.cpp", +// "opencv_imhmin.cpp", +// "opencv_imimposemin.cpp", +// "opencv_imlincomb.cpp", +// "opencv_immultiply.cpp", + "opencv_impixel.cpp", +// "opencv_impyramid.cpp", + "opencv_imread.cpp", +// "opencv_imrect.cpp", + "opencv_imresize.cpp", + "opencv_imsharpen.cpp", +// "opencv_imshowpair.cpp", +// "opencv_imsubtract.cpp", +// "opencv_imwarp.cpp", + "opencv_imwrite.cpp", +// "opencv_ind2gray.cpp", +// "opencv_ind2rgb.cpp", +// "opencv_lab2double.cpp", +// "opencv_lab2rgb.cpp", +// "opencv_lab2uint16.cpp", +// "opencv_lab2uint8.cpp", +// "opencv_lab2xyz.cpp", +// "opencv_laplacian.cpp", +// "opencv_line.cpp", + +// "opencv_medianblur.cpp", +// "opencv_montage.cpp", +// "opencv_morphologyEx.cpp", +// "opencv_ntsc2rgb.cpp", +// "opencv_puttext.cpp", +// "opencv_pyrDown.cpp", +// "opencv_pyrUp.cpp", +// "opencv_rectangle.cpp", +// "opencv_regionfill.cpp", +// "opencv_rgb2lab.cpp", +// "opencv_rgb2ntsc.cpp", +// "opencv_rgb2xyz.cpp", +// "opencv_roifill.cpp", +// "opencv_roipoly.cpp", +// "opencv_scharr.cpp", +// "opencv_sepFilter2D.cpp", +// "opencv_sobel.cpp", +// "opencv_ssim.cpp", +// "opencv_threshold.cpp", +// "opencv_undistort.cpp", +// "opencv_viscircles.cpp", +// "opencv_watershed.cpp", +// "opencv_whitepoint.cpp", +// "opencv_wiener2.cpp", +// "opencv_xyz2double.cpp", +// "opencv_xyz2lab.cpp", +// "opencv_xyz2rgb.cpp", +// "opencv_xyz2uint16.cpp", + + +// "opencv_deconvlucy.cpp", +// "opencv_integralImage.cpp", +// "opencv_imhistmatch.cpp", +// "opencv_graycoprops.cpp", +// "opencv_graydiffweight.cpp", +// "opencv_decorrstretch.cpp", +// "opencv_adapthisteq.cpp", +// "opencv_affine2d.cpp", + + "rgb2gray.cpp", +// "warpaffine.cpp", +// "opencv_pyrMeanShiftFiltering.cpp", +// "opencv_minimumenclosingcirlce.cpp", +// "opencv_minAreaRect.cpp", +// "opencv_mean1.cpp", + "opencv_imrotate.cpp", +// "opencv_getkerneltype.cpp", +// "opencv_fitellipse.cpp", +// "opencv_contourarea.cpp", +// "opencv_boxfilter.cpp", +// "opencv_borderInterpolate.cpp", +// "opencv_bilateralfilter.cpp", +// "opencv_arclength.cpp", +// "opencv_approxpolyDP.cpp", +// "opencv_histeq.cpp", + + + + ]; + + copyfile("/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/sci_gateway/cpp/common.h",TMPDIR); + copyfile("/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/sci_gateway/cpp/common.cpp",TMPDIR); + + [a, opt] = getversion(); + Version = opt(2); + + if getos()=="Windows" then +// third_dir = path_builder+filesep()+'..'+filesep()+'..'+filesep()+'thirdparty'; +// lib_base_dir = third_dir + filesep() + 'windows' + filesep() + 'lib' + filesep() + Version + filesep(); +// inc_base_dir = third_dir + filesep() + 'windows' + filesep() + 'include' + filesep() + 'coin'; +// C_Flags=['-D__USE_DEPRECATED_STACK_FUNCTIONS__ -w -I '+path_builder+' '+ '-I '+inc_base_dir+' '] +// Linker_Flag = [lib_base_dir+"libClp.lib "+lib_base_dir+"libCgl.lib "+lib_base_dir+"libOsi.lib "+lib_base_dir+"libOsiClp.lib "+lib_base_dir+"libCoinUtils.lib "+lib_base_dir+"libSymphony.lib "+lib_base_dir+"IpOptFSS.lib "+lib_base_dir+"IpOpt-vc10.lib "] + +else + third_dir = path_builder+'..'+filesep()+'..'+filesep()+'thirdparty'; + lib_base_dir = third_dir + filesep() + 'linux' + filesep() + 'lib' + filesep() + Version + filesep(); + inc_base_dir = third_dir + filesep() + 'linux' + filesep() + 'include' + filesep() + 'opencv'; + incl_base_dir = third_dir + filesep() + 'linux' + filesep() + 'include' + filesep() + 'opencv2'; + +// C_Flags=["-D__USE_DEPRECATED_STACK_FUNCTIONS__ -w -fpermissive -I"+path_builder+"-I"+inc_base_dir+" "+"-I"+incl_base_dir+" -Wl,-rpath= "+lib_base_dir+" "+"-L"+lib_base_dir+"libopencv_core"+" "+"-L"+lib_base_dir+"libopencv_highgui"+" "+"-L"+lib_base_dir+"libopencv_imgproc" ] + +// export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:"+lib_base_dir +// unix_s("export LD_LIBRARY_PATH="+lib_base_dir); + +// C_Flags=["-I"+inc_base_dir+" "+"-I"+incl_base_dir]; +// Linker_Flag = ["-L"+lib_base_dir+"libopencv_core"+" "+"-L"+lib_base_dir+"libopencv_highgui"+" "+"-L"+lib_base_dir+"libopencv_imgproc" ]; + +// Linker_Flag = ["-L/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/thirdparty/linux/lib/x64 -lopencv_core -L/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/thirdparty/linux/lib/x64 -lopencv_imgproc -L/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/thirdparty/linux/lib/x64 -lopencv_highgui" ] + +unix_s("export LD_LIBRARY_PATH="+tools_path+"lib/"+Version+filesep()+" "); +tools_path = path_builder + "../../thirdparty/linux/"; + +C_Flags=["-w -fpermissive -I"+tools_path+"include/opencv -I"+tools_path+"include/opencv2 -Wl,-rpath="+tools_path+"lib/"+Version+filesep()+" "] + +Linker_Flag = ["-L"+tools_path+"lib/"+Version+"-lopencv_shape -lopencv_stitching -lopencv_objdetect -lopencv_superres -lopencv_videostab -lopencv_calib3d -lopencv_features2d -lopencv_highgui -lopencv_video -lopencv_photo -lopencv_ml -lopencv_imgproc -lopencv_flann -lopencv_core "] + +end + +tbx_build_gateway(toolbox_title,Names,Files,get_absolute_file_path("builder_gateway_cpp.sce"), [], Linker_Flag, C_Flags,"g++"); + +clear toolbox_title Names Files Linker_Flag C_Flags; + +//function builder_gw_cpp() +// WITHOUT_AUTO_PUTLHSVAR = %t; +// copyfile("/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/sci_gateway/cpp/common.h",TMPDIR); +// copyfile("/home/fossee/Desktop/FOSSEE_Image_Processing_Toolbox/sci_gateway/cpp/common.cpp",TMPDIR); +//tbx_build_gateway("skeleton_imageprocessing", .. +// names, .. +// files, .. +// get_absolute_file_path("builder_gateway_cpp.sce"),[],["-L/usr/local/lib -lopencv_core -L/usr/local/lib -lopencv_highgui -L/usr/lib -lopencv_imgproc -L/usr/lib -lopencv_calib3d -L/usr/lib -lopencv_video -L/usr/lib -lopencv_nonfree -L/usr/lib -lopencv_ml -L/usr/lib -lopencv_objdetect"],["-I/usr/include/scilab -I/usr/include/opencv -I/usr/include/opencv2 "],"g++ "); +//endfunction + +//builder_gw_cpp(); +//clear builder_gw_cpp; // remove builder_gw_cpp on stack + diff --git a/sci_gateway/cpp/cleaner.sce b/sci_gateway/cpp/cleaner.sce new file mode 100644 index 0000000..4afc35c --- /dev/null +++ b/sci_gateway/cpp/cleaner.sce @@ -0,0 +1,22 @@ +// This file is released under the 3-clause BSD license. See COPYING-BSD. +// Generated by builder.sce : Please, do not edit this file +// cleaner.sce +// ------------------------------------------------------ +curdir = pwd(); +cleaner_path = get_file_path('cleaner.sce'); +chdir(cleaner_path); +// ------------------------------------------------------ +if fileinfo('loader.sce') <> [] then + mdelete('loader.sce'); +end +// ------------------------------------------------------ +if fileinfo('libFOSSEE_Image_Processing_Toolbox.so') <> [] then + mdelete('libFOSSEE_Image_Processing_Toolbox.so'); +end +// ------------------------------------------------------ +if fileinfo('libFOSSEE_Image_Processing_Toolbox.c') <> [] then + mdelete('libFOSSEE_Image_Processing_Toolbox.c'); +end +// ------------------------------------------------------ +chdir(curdir); +// ------------------------------------------------------ diff --git a/sci_gateway/cpp/common.cpp b/sci_gateway/cpp/common.cpp new file mode 100644 index 0000000..c19f58a --- /dev/null +++ b/sci_gateway/cpp/common.cpp @@ -0,0 +1,890 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +//takes the matrix type as parameter and returns a string of the type +string type2str(int type) +{ + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + switch ( depth ) + { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + return r; +} + +//takes matrix type as parameter and returns no. of channels of the image matrix +int no_of_channels(int type) +{ + uchar chans = 1 + (type >> CV_CN_SHIFT); + return chans; +} + +//function used to retrieve image matrix from Scilab environment +/* + We need to retrieve image from the Scilab environment + We do this by getting a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int retrieveImage(Mat &image,int pos) +{ + + SciErr sciErr; + int iRows=0,iCols=0,i,j,k=0; + int *piAddr = NULL; + int *piAddrChild = NULL; + int iPrec = 0,iItem = 0; + + //retrieving number of items in the list and type of data(integer/float) + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + if(iItem==3) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC3); + + /*Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + if(iItem==3) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + //i+iRows*j + } + } + } + else + { + short unsigned int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC1); + + //Assigning matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT16: //for signed integer 16 + { + if(iItem==3) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + } + } + else //for floating point/ double precision values + { + if(iItem==3) + { + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + double *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + } + return 1; +} + +//function that returns an image matrix to the Scilab environment +/* + What we now need, is to pass the image matrix to the Scilab environment. + We do this by passing a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int returnImage(char *checker,Mat img,int pos) +{ + int i,j,k=0; + int *piAddrNew = NULL; + SciErr sciErr; + int num=no_of_channels(img.type()); //num now contains no. of channels of the image + //creating the list that will be passed to the Scilab enviroment + if(num==3) + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 3, &piAddrNew); + else + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 1, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(strcmp(checker,"8U")==0) //for Unsigned Integer 8 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *b = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r [i+img.rows*j]=intensity.val[2]; + g [i+img.rows*j]=intensity.val[1]; + b [i+img.rows*j]=intensity.val[0]; + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"16U")==0) //for Unsigned Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *b = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + } + else if(strcmp(checker,"16S")==0) //for Signed Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *b = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"32S")==0) //for Signed Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *b = (int *)malloc(img.rows * img.cols * sizeof(int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + + else if(strcmp(checker,"32F")==0) //for Float 32 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + else if(strcmp(checker,"64F")==0) //For Float 64 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + +} diff --git a/sci_gateway/cpp/common.cpp~ b/sci_gateway/cpp/common.cpp~ new file mode 100644 index 0000000..c19f58a --- /dev/null +++ b/sci_gateway/cpp/common.cpp~ @@ -0,0 +1,890 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +//takes the matrix type as parameter and returns a string of the type +string type2str(int type) +{ + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + switch ( depth ) + { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + return r; +} + +//takes matrix type as parameter and returns no. of channels of the image matrix +int no_of_channels(int type) +{ + uchar chans = 1 + (type >> CV_CN_SHIFT); + return chans; +} + +//function used to retrieve image matrix from Scilab environment +/* + We need to retrieve image from the Scilab environment + We do this by getting a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int retrieveImage(Mat &image,int pos) +{ + + SciErr sciErr; + int iRows=0,iCols=0,i,j,k=0; + int *piAddr = NULL; + int *piAddrChild = NULL; + int iPrec = 0,iItem = 0; + + //retrieving number of items in the list and type of data(integer/float) + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + if(iItem==3) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC3); + + /*Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + if(iItem==3) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + //i+iRows*j + } + } + } + else + { + short unsigned int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC1); + + //Assigning matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT16: //for signed integer 16 + { + if(iItem==3) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + } + } + else //for floating point/ double precision values + { + if(iItem==3) + { + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + double *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + } + return 1; +} + +//function that returns an image matrix to the Scilab environment +/* + What we now need, is to pass the image matrix to the Scilab environment. + We do this by passing a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int returnImage(char *checker,Mat img,int pos) +{ + int i,j,k=0; + int *piAddrNew = NULL; + SciErr sciErr; + int num=no_of_channels(img.type()); //num now contains no. of channels of the image + //creating the list that will be passed to the Scilab enviroment + if(num==3) + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 3, &piAddrNew); + else + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 1, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(strcmp(checker,"8U")==0) //for Unsigned Integer 8 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *b = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r [i+img.rows*j]=intensity.val[2]; + g [i+img.rows*j]=intensity.val[1]; + b [i+img.rows*j]=intensity.val[0]; + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"16U")==0) //for Unsigned Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *b = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + } + else if(strcmp(checker,"16S")==0) //for Signed Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *b = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"32S")==0) //for Signed Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *b = (int *)malloc(img.rows * img.cols * sizeof(int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + + else if(strcmp(checker,"32F")==0) //for Float 32 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + else if(strcmp(checker,"64F")==0) //For Float 64 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + +} diff --git a/sci_gateway/cpp/common.h b/sci_gateway/cpp/common.h new file mode 100644 index 0000000..5de6cf7 --- /dev/null +++ b/sci_gateway/cpp/common.h @@ -0,0 +1,9 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +string type2str(int type); +int no_of_channels(int type); +int retrieveImage(Mat &image,int pos); +int returnImage(char *checker,Mat img,int pos); + diff --git a/sci_gateway/cpp/dctmtx.cpp b/sci_gateway/cpp/dctmtx.cpp new file mode 100644 index 0000000..17683d2 --- /dev/null +++ b/sci_gateway/cpp/dctmtx.cpp @@ -0,0 +1,96 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : return_n*n_matrix=dctmtx(double n) */ + +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define PI 3.14 + + int dctmtx(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows=0; + int iCols=0; + int piRows=0; + int piCols=0; + int* piAddr2=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + int* piLen=NULL; + char **val; + int i,j; + int error; + double n; + Mat img,dst; + //checking input and output argument + CheckInputArgument(pvApiCtx,1,1); + CheckOutputArgument(pvApiCtx,1,1); + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of first argument + error=getScalarDouble(pvApiCtx,piAddr1,&n) ; + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + int k=0; + double *mat; + int s=n; + mat=(double *)malloc(sizeof(double)*n*n); + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define REF_X 95.047; // Observer= 2°, Illuminant= D65 + #define REF_Y 100.000; + #define REF_Z 108.883; + + struct Color + { + float R,G,B,X,Y,Z,L,a,b; + }; + + Color lab2xyz1(float l, float a, float b) + { + float y = (l + 16) / 116; + float x = a / 500 + y; + float z = y - b / 200; + if(pow(y,3) > 0.008856) + { + y = pow(y,3); + } + else + { + y =(y-16/116)/7.787; + } + if (pow(x,3) > 0.008856) + { + x = pow(x,3); + } + else + { + x = (x-16/116)/7.787; + } + if (pow(z,3) > 0.008856) + { + z = pow(z,3); + } + else + { + z =(z-16/ 116 ) / 7.787; + } + + Color xyz; + xyz.X= x * REF_X; + xyz.Y = y * REF_Y; + xyz.Z = z * REF_Z; + return xyz; + + } + + Color lab2xyz2(float l, float a, float b,char *s) + { + double REF_X1; + double REF_Y1; + double REF_Z1; + //if else condition for checking various input arguments and assign values accordingly + if(!strcmp(s,"a")) + { + REF_X1=1.0985; + REF_Y1= 1.0000; + REF_Z1=0.3558; + } + else if(!strcmp(s,"c")) + { + REF_X1=0.9807; + REF_Y1= 1.0000; + REF_Z1=1.1822; + } + else if(!strcmp(s,"d50")) + { + REF_X1=0.9642; + REF_Y1= 1.0000; + REF_Z1=0.8251; + } + else if(!strcmp(s,"d65")) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(!strcmp(s,"icc")) + { + REF_X1=0.962; + REF_Y1=1.000; + REF_Z1= 0.8249; + } + else if(!strcmp(s,"e")) + { + REF_X1=1.000; + REF_Y1=1.000; + REF_Z1=1.000; + } + float y = (l + 16) / 116; + float x = a / 500 + y; + float z = y - b / 200; + if (pow(y,3) > 0.008856) + { + y=pow(y,3); + } + else + { + y=(y-16/116)/7.787; + } + if(pow(x,3) > 0.008856) + { + x=pow(x,3); + } + else + { + x=(x-16/116)/7.787; + } + if (pow(z,3) > 0.008856) + { + z=pow(z,3); + } + else + { + z=(z-16/116)/7.787; + } + Color xyz; + xyz.X= x * REF_X1; + xyz.Y = y * REF_Y1; + xyz.Z = z * REF_Z1; + return xyz; + } + int lab2xyz(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows=0; + int iCols=0; + int piRows=0; + int piCols=0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + int* piLen=NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img,dst; + float R,G,B; + int k=0; + int *piAddrNew = NULL; + double *pstDataR=NULL; + double *pstDataG=NULL; + double *pstDataB=NULL; + double *r,*g,*b; + int m=0; + //checking output argument + CheckOutputArgument(pvApiCtx,1,1); + //retrive address of the list + sciErr=getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr=getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + r=(double *)malloc(sizeof(double)*iRows*iCols); + g=(double *)malloc(sizeof(double)*iRows*iCols); + b=(double *)malloc(sizeof(double)*iRows*iCols); + for(int i=0;i +#include +#include +#include +static int direct_gateway(char *fname,void F(void)) { F();return 0;}; +extern Gatefunc opencv_blur; +extern Gatefunc opencv_circle; +extern Gatefunc opencv_dilate; +extern Gatefunc opencv_erode; +extern Gatefunc opencv_imcrop; +extern Gatefunc opencv_impixel; +extern Gatefunc opencv_imread; +extern Gatefunc opencv_imresize; +extern Gatefunc opencv_imsharpen; +extern Gatefunc opencv_imwrite; +extern Gatefunc opencv_imrotate; +extern Gatefunc opencv_rgb2gray; +static GenericTable Tab[]={ + {(Myinterfun)sci_gateway,opencv_blur,"raw_blur"}, + {(Myinterfun)sci_gateway,opencv_circle,"raw_circle"}, + {(Myinterfun)sci_gateway,opencv_dilate,"raw_dilate"}, + {(Myinterfun)sci_gateway,opencv_erode,"raw_erode"}, + {(Myinterfun)sci_gateway,opencv_imcrop,"raw_imcrop"}, + {(Myinterfun)sci_gateway,opencv_impixel,"raw_impixel"}, + {(Myinterfun)sci_gateway,opencv_imread,"raw_imread"}, + {(Myinterfun)sci_gateway,opencv_imresize,"raw_imresize"}, + {(Myinterfun)sci_gateway,opencv_imsharpen,"raw_imsharpen"}, + {(Myinterfun)sci_gateway,opencv_imwrite,"raw_imwrite"}, + {(Myinterfun)sci_gateway,opencv_imrotate,"raw_imrotate"}, + {(Myinterfun)sci_gateway,opencv_rgb2gray,"raw_rgb2gray"}, +}; + +int C2F(libFOSSEE_Image_Processing_Toolbox)() +{ + Rhs = Max(0, Rhs); + if (*(Tab[Fin-1].f) != NULL) + { + if(pvApiCtx == NULL) + { + pvApiCtx = (StrCtx*)MALLOC(sizeof(StrCtx)); + } + pvApiCtx->pstName = (char*)Tab[Fin-1].name; + (*(Tab[Fin-1].f))(Tab[Fin-1].name,Tab[Fin-1].F); + } + return 0; +} +#ifdef __cplusplus +} +#endif diff --git a/sci_gateway/cpp/libFOSSEE_Image_Processing_Toolbox.so b/sci_gateway/cpp/libFOSSEE_Image_Processing_Toolbox.so new file mode 100755 index 0000000..541d4a4 Binary files /dev/null and b/sci_gateway/cpp/libFOSSEE_Image_Processing_Toolbox.so differ diff --git a/sci_gateway/cpp/loader.sce b/sci_gateway/cpp/loader.sce new file mode 100644 index 0000000..eedb234 --- /dev/null +++ b/sci_gateway/cpp/loader.sce @@ -0,0 +1,32 @@ +// This file is released under the 3-clause BSD license. See COPYING-BSD. +// Generated by builder.sce : Please, do not edit this file +// ---------------------------------------------------------------------------- +// +libFOSSEE_Image_Pro_path = get_absolute_file_path('loader.sce'); +// +// ulink previous function with same name +[bOK, ilib] = c_link('libFOSSEE_Image_Processing_Toolbox'); +if bOK then + ulink(ilib); +end +// +list_functions = [ 'raw_blur'; + 'raw_circle'; + 'raw_dilate'; + 'raw_erode'; + 'raw_imcrop'; + 'raw_impixel'; + 'raw_imread'; + 'raw_imresize'; + 'raw_imsharpen'; + 'raw_imwrite'; + 'raw_imrotate'; + 'raw_rgb2gray'; +]; +addinter(libFOSSEE_Image_Pro_path + filesep() + 'libFOSSEE_Image_Processing_Toolbox' + getdynlibext(), 'libFOSSEE_Image_Processing_Toolbox', list_functions); +// remove temp. variables on stack +clear libFOSSEE_Image_Pro_path; +clear bOK; +clear ilib; +clear list_functions; +// ---------------------------------------------------------------------------- diff --git a/sci_gateway/cpp/multithresh.cpp b/sci_gateway/cpp/multithresh.cpp new file mode 100644 index 0000000..05a82f3 --- /dev/null +++ b/sci_gateway/cpp/multithresh.cpp @@ -0,0 +1,208 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : return_matrix_of_threshold_levels=multithresh(InputArray src,int level) */ +/* ==================================================================== */ +/*The function return various threshold levels:*/ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +using namespace cv; +using namespace std; + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + //global variables + float **p,**s,**h; + float **as,**ds; + float fast(int n,int i,int j,int index[]) + { + int k,index1; + float s; + float max = INT_MIN; + if(i>256) + { + return INT_MIN; + } + if(n==0) + return h[i][j]; + if(ds[i][n]!=-1.000000) + return ds[i][n]; + for(k=i;k<=256;k++) + { + s=h[i][k]+fast(n-1,k+1,j,index); + if(s>max) + { + max=s; + as[i][n]=k; + index[n]=k; + + } + } + return ds[i][n]=max; + } + + int multithresh(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr2=NULL ; + double thresh; + float C; + int i,j; + int error; + //checking input argument + CheckInputArgument(pvApiCtx,2,2); + //checking output argument + CheckOutputArgument(pvApiCtx,1,1); + //for first argument + Mat img; + retrieveImage(img,1); + //second argument + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of fifth argument + error=getScalarDouble(pvApiCtx,piAddr2,&thresh) ; + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //code for histogram calculation + Mat imageh; + cvtColor(img, imageh, CV_BGR2GRAY); + // number of bins + int histSize[1] = {256}; + // min andax pixel value + float hranges[2] = {0, 256}; + const float* ranges[1] = {hranges}; + int channels[1] = {0}; // only 1 channel used + cv::MatND hist; + calcHist(&imageh, 1, channels, cv::Mat(), hist, 1, histSize, ranges); + //code for ostu method for histogram calculation + float *hi; + hi=(float *)malloc(sizeof(float)*257); + float nPixels = imageh.total(); + for(i=1;i<257;i++) + { + hi[i]=(float)(hist.at(i-1)); + } + p=(float **)malloc(sizeof(float *)*257); + for(i=0;i<257;i++) + { + p[i]=(float *)malloc(sizeof(float)*257); + } + s=(float **)malloc(sizeof(float *)*257); + for(i=0;i<257;i++) + { + s[i]=(float *)malloc(sizeof(float)*257); + } + h=(float **)malloc(sizeof(float *)*257); + for(i=0;i<257;i++) + { + h[i]=(float *)malloc(sizeof(float)*257); + } + for(i=0;i<257;i++) + { + for(j=0;j<257;j++) + { + p[i][j]=0; + s[i][j]=0; + h[i][j]=0; + } + } + p[1][0]=0; + s[1][0]=0; + for(i=0;i<256;i++) + { + p[1][i+1]=p[1][i]+hi[i+1]; + s[1][i+1]=s[1][i]+((i+1)*(hi[i+1])); + } + for(i=2;i<257;i++) + { + for(j=1;j<257;j++) + { + p[i][j]=p[1][j]-p[1][i-1]; + s[i][j]=s[1][j]-s[1][i-1]; + } + } + float temp,temp1; + for(j=1;j<257;j++) + { + for(i=1;i<=j;i++) + { + temp=(s[i][j])*(s[i][j]); + temp1=(temp/p[i][j]); + h[i][j]=temp1; + } + } + as=(float **)malloc(sizeof(float *)*257); + for(i=0;i<257;i++) + { + as[i]=(float *)malloc(sizeof(float)*257); + } + ds=(float **)malloc(sizeof(float *)*257); + for(i=0;i<257;i++) + { + ds[i]=(float *)malloc(sizeof(float)*257); + } + for(i=0;i<257;i++) + { + for(j=0;j<257;j++) + { + ds[i][j]=-1.000000; + } + } + int n=(int )thresh; + int index[n+1]; + fast(n,1,256,index); + int a=0; + double *output=NULL;int k=0; + output=( double *)malloc(sizeof(double)*n); + for(i=n;i>=1;i--) + { + a=as[a+1][i]; + output[k]=a; + k++; + } + for(i=0;i<257;i++) + { + free(h[i]); + free(as[i]); + free(ds[i]); + } + free(h); + free(as); + free(ds); + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1,1,n,output); + free(output); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx,1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_adapthisteq.cpp b/sci_gateway/cpp/opencv_adapthisteq.cpp new file mode 100644 index 0000000..334f529 --- /dev/null +++ b/sci_gateway/cpp/opencv_adapthisteq.cpp @@ -0,0 +1,201 @@ +/************************************************************************************* +Author : Yash S. Bhalgat +*************************************************************************************** +---------- Performs Contrast Limited Adaptive Histogram Equalisation ------------- +Usage : + 1) output_img = adapthisteq(input_img); + In this usage, the image itself is used as the guidance image. + + 2) output_img = adapthisteq(input_img, clip_limit); +Example : + img = imread("lena.jpg"); + imshow(img); + output_img = adapthisteq(img, img, 9); + imshow(output_img); +***********************************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include // std::max + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + + + int opencv_adapthisteq(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int i,j,k; + double clip_limit; + + //Default clip limit + clip_limit = 0.001; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + int inputarg = *getNbInputArgument(pvApiCtx); + + + Mat input_img; + retrieveImage(input_img,1); + + if(inputarg == 2){ + //for value of clip_limit + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &clip_limit); + if(intErr) + return intErr; + } + + + //Main function + //cvtColor(input_img, input_img, CV_RGB2GRAY); + + float C[256]; + + int row = input_img.rows; + int col = input_img.cols; + + int window_x = 90; + int window_y = 90; + + int min_x, min_y, max_x, max_y; + Mat window_matrix; + + //float clip_limit = 0.001; + + int histSize = 256; //from 0 to 255 + float range[] = { 0, 256 } ; + const float* histRange = { range }; + + Mat H; + Mat output_img = Mat::zeros(input_img.size(), input_img.type()); + + if(input_img.channels()==1){ + for(int i=0; i(i,j)==0) output_img.at(i,j) =0; + else{ + calcHist(&window_matrix, 1, 0, Mat(), H, 1, &histSize, &histRange); + + int N = window_matrix.rows; + int M = window_matrix.cols; + H=H/(N*M); + // cout<(0,z) > clip_limit) H.at(0,z) = clip_limit; + } + + float contrastArea = 1.0 - cv::sum(H).val[0]; + float height = contrastArea / 256.00; + + H = H + height; + //cout<(0,0)*255; + for(int k=1; k<256; k++){ + C[k]= C[k-1] + H.at(0,k)*255; + } + //cout<(i,j) = C[input_img.at(i,j)]; + } + } + } + } + else if(input_img.channels()==3){ + vector Ichannels; + split(input_img, Ichannels); + + for(int d=0; d<3; d++){ + for(int i=0; i(i,j)==0) output_img.at(i,j)[d] =0; + else{ + calcHist(&window_matrix, 1, 0, Mat(), H, 1, &histSize, &histRange); + + int N = window_matrix.rows; + int M = window_matrix.cols; + H=H/(N*M); + // cout<(0,z) > clip_limit) H.at(0,z) = clip_limit; + } + + float contrastArea = 1.0 - cv::sum(H).val[0]; + float height = contrastArea / 256.00; + + H = H + height; + //cout<(0,0)*255; + for(int k=1; k<256; k++){ + C[k]= C[k-1] + H.at(0,k)*255; + } + //cout<(i,j)[d] = C[Ichannels[d].at(i,j)]; + } + } + } + } + + + } + + + + //returning image + string tempstring = type2str(output_img.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,output_img,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_affine2d.cpp b/sci_gateway/cpp/opencv_affine2d.cpp new file mode 100644 index 0000000..8dc1200 --- /dev/null +++ b/sci_gateway/cpp/opencv_affine2d.cpp @@ -0,0 +1,122 @@ +/******************************************************************* +Author : Yash S. Bhalgat +******************************************************************** +Usage : T = affine2d(A); +Output: T - 3x3 matrix equivalent to tform.T matrix in Matlab output +********************************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_affine2d(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr1 = NULL; + char **ddepth = NULL; + int i,j,k; + double *inpMatrix; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + //for inputMatrix + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols ,&inpMatrix); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n=iRows; + double inputMatrix[n][n]; + //assigning values to actual kernelMatrix + for(i=0;i(i,j) = warp_mat_t.at(i,j); + + outputMatrix.at(0,2) = 0; + outputMatrix.at(1,2) = 0; + outputMatrix.at(2,2) = 1.0; + + //returning image + string tempstring = type2str(outputMatrix.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,outputMatrix,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_applycform.cpp b/sci_gateway/cpp/opencv_applycform.cpp new file mode 100644 index 0000000..ea29604 --- /dev/null +++ b/sci_gateway/cpp/opencv_applycform.cpp @@ -0,0 +1,371 @@ +/******************************************************** +Function :applycform +Syntax :B=applycform(image,string) +*string : 'xyz2lab' 'lab2xyz' 'srgb2xyz' + 'xyz2srgb' 'srgb2lab' 'lab2srgb' +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; + struct Color +{ + float R,G,B,X,Y,Z,R1,G1,B1,r,g,b; +}; +Color rgb2lab(float R, float G, float B) +{ + + float r = R/255.f; //R 0..1 + float g = G/255.f; //G 0..1 + float b = B/255.f; //B 0..1 + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz; + if (r <= 0.04045) + r = r/12.92; + else + r = (float)pow((r+0.055)/1.055,2.4); + if (g <= 0.04045) + g = g/12.92; + else + g = (float)pow((g+0.055)/1.055,2.4); + if (b <= 0.04045) + b = b/12.92; + else + b = (float)pow((b+0.055)/1.055,2.4); + float X = 0.4124*r + 0.3576*g + 0.1805*b; + float Y = 0.2126*r + 0.7152 *g + 0.0722 *b; + float Z = 0.0193*r + 0.1192*g + 0.9505 *b; + float xr = X/REF_X; + float yr = Y/REF_Y; + float zr = Z/REF_Z; + if ( xr > eps ) + fx = (float)pow(xr, 1/3.); + else + fx = (float) ((k * xr + 16.) / 116.); + if ( yr > eps ) + fy = (float)pow(yr, 1/3.); + else + fy = (float) ((k * yr + 16.) / 116.); + if ( zr > eps ) + fz = (float)pow(zr, 1/3.); + else + fz = (float) ((k * zr + 16.) / 116); + Color rgb; + rgb.R1= ( 116 * fy ) - 16; + rgb.G1= 500*(fx-fy); + rgb.B1= 200*(fy-fz); + return rgb; + +} +Color xyz2lab(float R, float G, float B) +{ + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz,Ls,as,bs; + float xr=R/REF_X; + float yr=G/REF_Y; + float zr=B/REF_Z; + if ( xr > eps ) + fx = (float)pow(xr, 1/3.); + else + fx = (float) ((k * xr + 16.) / 116.); + if ( yr > eps ) + fy = (float)pow(yr, 1/3.); + else + fy = (float) ((k * yr + 16.) / 116.); + if ( zr > eps ) + fz = (float)pow(zr, 1/3.); + else + fz = (float) ((k * zr + 16.) / 116); + Ls = ( 116 * fy ) - 16; + as = 500*(fx-fy); + bs = 200*(fy-fz); + Color rgb; + rgb.R1 =Ls; + rgb.G1 =as; + rgb.B1 =bs; + return rgb; +} +Color rgb2xyz(float R, float G, float B) +{ + + float r = R/255.f; //R 0..1 + float g = G/255.f; //G 0..1 + float b = B/255.f; //B 0..1 + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz; + if (r <= 0.04045) + r = r/12.92; + else + r = (float)pow((r+0.055)/1.055,2.4); + if (g <= 0.04045) + g = g/12.92; + else + g = (float)pow((g+0.055)/1.055,2.4); + if (b <= 0.04045) + b = b/12.92; + else + b = (float)pow((b+0.055)/1.055,2.4); + float X1 = 0.4124*r + 0.3576*g + 0.1805*b; + float Y1 = 0.2126*r + 0.7152 *g + 0.0722 *b; + float Z1 = 0.0193*r + 0.1192*g + 0.9505 *b; + float xr = X1; + float yr = Y1; + float zr = Z1; + Color rgb; + rgb.R1= xr; + rgb.G1= yr; + rgb.B1= zr; + return rgb; + +} +Color lab2xyz(float R, float G, float B) +{ + float y = (R+ 16) / 116; + float x = G / 500 + y; + float z = y - B/ 200; + + if ( pow( y , 3 ) > 0.008856 ) { y = pow( y , 3 ); } + else { y = ( y - 16 / 116 ) / 7.787; } + if ( pow( x , 3 ) > 0.008856 ) { x = pow( x , 3 ); } + else { x = ( x - 16 / 116 ) / 7.787; } + if ( pow( z , 3 ) > 0.008856 ) { z = pow( z , 3 ); } + else { z = ( z - 16 / 116 ) / 7.787; } + + Color rgb; + rgb.R1= x * REF_X; + rgb.G1= y * REF_Y; + rgb.B1= z * REF_Z; + return rgb; + + +} +Color xyz2rgb(float R, float G, float B) +{ + float x = R; + float y = G; + float z = B; + float R1 = 3.2404542*x-1.5371385*y-0.4985314*z; + float G1 =-0.9692660*x+1.8760108*y+0.0415560 *z; + float B1 = 0.0556434*x-0.2040259*y+1.0572252*z; + if( R1 > 0.0031308 ) + R1 = 1.055 * ( pow( R1, 1./2.4 ) ) - 0.055; + else + R1 = 12.92 * R1; + if( G1 > 0.0031308 ) + G1 = 1.055 * ( pow( G1, 1./2.4 ) ) - 0.055; + else + G1 = 12.92 * G1; + if( B1 > 0.0031308 ) + B1 = 1.055 * ( pow( B1, 1./2.4 ) ) - 0.055; + else + B1= 12.92 * B1; + + Color rgb; + rgb.R1= R1; + rgb.G1= G1; + rgb.B1= B1; + return rgb; + + +} +Color lab2rgb(float R, float G, float B) +{ + float y = (R + 16) / 116; + float x = G / 500 + y; + float z = y-B / 200; + if ( pow( y , 3 ) > 0.008856 ) + { + y = pow( y , 3 ); + } + else + { + y = ( y - 16 / 116 ) / 7.787; + } + if ( pow( x , 3 ) > 0.008856 ) + { + x = pow( x , 3 ); + } + else + { + x = ( x - 16 / 116 ) / 7.787; + } + if ( pow( z , 3 ) > 0.008856 ) + { + z = pow( z , 3 ); + } + else + { + z = ( z - 16 / 116 ) / 7.787; + } + float X= x *REF_X; + float Y= y *REF_Y; + float Z = z *REF_Z; + float R1=3.240479*X-1.537150*Y -0.498535 *Z; + float G1=-0.969256*X+1.875992 *Y+0.041556* Z; + float B1=0.055648*X-0.204043*Y +1.057311*Z; + if( R1 > 0.0031308 ) + R1 = 1.055 * ( pow( R1, 1./2.4 ) ) - 0.055; + else + R1 = 12.92 * R1; + if( G1 > 0.0031308 ) + G1 = 1.055 * ( pow( G1, 1./2.4 ) ) - 0.055; + else + G1 = 12.92 * G1; + if( B1 > 0.0031308 ) + B1 = 1.055 * ( pow( B1, 1./2.4 ) ) - 0.055; + else + B1 = 12.92 * B1; + + + Color rgb; + rgb.R1= R1; + rgb.G1= G1; + rgb.B1= B1; + return rgb; + + +} + int opencv_applycform(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int* piAddr2=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + int* piaddr=NULL; + char *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img; + float R,G,B; + char* pstData = NULL; + int *piAddrNew = NULL; + int* piAddr4=NULL; + int iRet = 0; + CheckInputArgument(pvApiCtx,2,2); + CheckOutputArgument(pvApiCtx,1,1); + int m=0; + retrieveImage(img,1); + double *r1,*g1,*b1; + r1=(double *)malloc(sizeof(double)*img.rows*img.cols); + g1=(double *)malloc(sizeof(double)*img.rows*img.cols); + b1=(double *)malloc(sizeof(double)*img.rows*img.cols); + sciErr = getVarAddressFromPosition(pvApiCtx, 2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isStringType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + + iRet = getAllocatedSingleString(pvApiCtx, piAddr2, &pstData); + } + } + + for(int i=0;i(i,j); + R= intensity.val[2]; + G= intensity.val[1]; + B= intensity.val[0]; + if(strcasecmp(pstData,"xyz2lab")==0) + { + out=xyz2lab(R,G,B); + } + else if(strcasecmp(pstData,"lab2xyz")==0) + { + out=lab2xyz(R,G,B); + } + else if(strcasecmp(pstData,"srgb2xyz")==0) + { + out=rgb2xyz(R,G,B); + } + else if(strcasecmp(pstData,"xyz2srgb")==0) + { + out=xyz2rgb(R,G,B); + } + else if(strcasecmp(pstData,"srgb2lab")==0) + { + out=rgb2lab(R,G,B); + } + else if(strcasecmp(pstData,"lab2srgb")==0) + { + out=rgb2lab(R,G,B); + } + else + { + sciprint("Expected input argument 'xyz2lab' 'lab2xyz' 'srgb2xyz' 'xyz2srgb' 'srgb2lab' 'lab2srgb'"); + return 0; + } + r1[m]=out.R1; + g1[m]=out.G1; + b1[m++]=out.B1; + } + } + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 1, img.rows,img.cols, r1); + free(r1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 2, img.rows, img.cols, g1); + free(g1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 3, img.rows, img.cols, b1); + free(b1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_approxpolyDP.cpp b/sci_gateway/cpp/opencv_approxpolyDP.cpp new file mode 100644 index 0000000..95cd835 --- /dev/null +++ b/sci_gateway/cpp/opencv_approxpolyDP.cpp @@ -0,0 +1,151 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : output_curve=approxpolyDP(input_curve,double epsilon, bool closed)*/ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int approxpolyDP(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double *pstData = NULL; + char **pstData2 = NULL; + double *rrows; + double *rcols; + int noofitem; + double res; + double epsilon; + int error; + //checking input argument + CheckInputArgument(pvApiCtx,3,3); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + // get Address of first input + sciErr =getVarAddressFromPosition(pvApiCtx,1,&piAddr); + //check for any error + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrieve input array + //SciErr getMatrixOfDouble(void* _pvCtx, int* _piAddress, int* _piRows, int* _piCols, double** _pdblReal) + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols,&pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int k=0; + vector contours; + for(int i=0;i approx; + + if(!strcmp("True",pstData2[0])) + { + approxPolyDP(contours, approx,epsilon, true); + } + else if(!strcmp("False",pstData2[0])) + { + approxPolyDP(contours, approx,epsilon, false); + } + double *m=(double *)malloc(2*(approx.size())*sizeof(double)); + Point a1; + int j=0; + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int arclenght(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double *pstData = NULL; + char **pstData2 = NULL; + double *rrows; + double *rcols; + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + int noofitem; + double res; + double *epsilon; + int error; + //checking input argument + CheckInputArgument(pvApiCtx,2,2); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + // get Address of first input + sciErr =getVarAddressFromPosition(pvApiCtx,1,&piAddr); + //check for any error + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrieve input array + //SciErr getMatrixOfDouble(void* _pvCtx, int* _piAddress, int* _piRows, int* _piCols, double** _pdblReal) + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols,&pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int k=0; + vector contours; + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_arrowedline(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11 = NULL; + int *piAddr12 = NULL; + int i,j,k; + double thickness=1 , linetype=8,shift =0 ,tiplength=0.1; + double x1,y1,x2,y2,r_value,g_value,b_value; + + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 12); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&x1); + if(intErr) + return intErr; + + //for value of y coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&y1); + if(intErr) + return intErr; + + //for value of x coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4 ,&x2); + if(intErr) + return intErr; + + ///for value of y coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&y2); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6,&r_value); + if(intErr) + return intErr; + + // for G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&b_value); + if(intErr) + return intErr; + + // for thickness of circle default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr9 ,&thickness); + if(intErr) + return intErr; + + //for line type of circle default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr10,&linetype); + if(intErr) + return intErr; + + // for shift in line defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Syntax: sciErr = getMatrixOfInteger8(pvApiCtx, piAddr8, &iRows8, &iCols8, &pcData); + intErr = getScalarDouble(pvApiCtx, piAddr11 ,&shift); + if(intErr) + return intErr; + + // for tiplength of arrow default : 0.1 + sciErr = getVarAddressFromPosition(pvApiCtx,12,&piAddr12); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Syntax: sciErr = getMatrixOfInteger8(pvApiCtx, piAddr8, &iRows8, &iCols8, &pcData); + intErr = getScalarDouble(pvApiCtx, piAddr12,&tiplength); + if(intErr) + return intErr; + //temporary pt variable, to use in function as centre + Point pt1(x1,y1); + Point pt2(x2,y2); + + + /// checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead\n"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead"); + } + //calling the opencv function + arrowedLine( src, pt1, pt2, Scalar(b_value,g_value,r_value), thickness, linetype, shift, tiplength); + + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway/cpp/opencv_bilateralfilter.cpp b/sci_gateway/cpp/opencv_bilateralfilter.cpp new file mode 100644 index 0000000..d508b78 --- /dev/null +++ b/sci_gateway/cpp/opencv_bilateralfilter.cpp @@ -0,0 +1,112 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : return_image=bilateralFilter(InputArray src, int d, double sigmaColor, double sigmaSpace ) */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int bilateralfilter(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double* pstData = NULL; + int* pstData2 = NULL; + double *rrows; + double *rcols; + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + int noofitem; + double d; + double sigmaColor; + double sigmaSpace; + int error; + // checking input argument + CheckInputArgument(pvApiCtx,4,4); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + // get Address of first input + Mat img; + retrieveImage(img,1); + //second argument + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of second argument + error=getScalarDouble(pvApiCtx,piAddr2,&d) ; + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //third argument + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of third argument + error=getScalarDouble(pvApiCtx,piAddr3,&sigmaColor) ; + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + + //fourth argument + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } +//this function will fetch value of fourth argument + error=getScalarDouble(pvApiCtx,piAddr4,&sigmaSpace) ; + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + Mat dst; + //calling open cv bilateral filter function + bilateralFilter(img,dst,d,sigmaColor,sigmaSpace,BORDER_DEFAULT ); + string tempstring = type2str(dst.type()); + char* checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + //returning output variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + +} +} diff --git a/sci_gateway/cpp/opencv_blur.cpp b/sci_gateway/cpp/opencv_blur.cpp new file mode 100644 index 0000000..3db34ca --- /dev/null +++ b/sci_gateway/cpp/opencv_blur.cpp @@ -0,0 +1,134 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + + + int opencv_blur(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + double ksize_width,ksize_height,anchorX,anchorY; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of ksize_height + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &ksize_height); + if(intErr) + return intErr; + + //for value of ksize_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&ksize_width); + if(intErr) + return intErr; + + //for value of anchorX + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &anchorX); + if(intErr) + return intErr; + + //for value of anchorY + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &anchorY); + if(intErr) + return intErr; + + //applying checks on input parameters + Mat new_image(image.rows,image.cols,image.type()); + + //error checks + if(ksize_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead"); + ksize_height=1; + } + if(ksize_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead"); + ksize_width=1; + } + if(ksize_width != ksize_height) + { + sciprint("Blurring Kernel Size not equal. Value of ksize_height used for ksize_width as well"); + ksize_width = ksize_height; + } + if(anchorX >= ksize_height || anchorY >= ksize_width) + { + sciprint("Invalid anchor points. Default point (-1,-1) used instead"); + anchorY=-1; + anchorX=-1; + } + + //defining temporary size and point type variables to use in the function + Size sz(ksize_height,ksize_width); + Point pt(anchorX,anchorY); + + //void blur(InputArray src, OutputArray dst, Size ksize, Point anchor=Point(-1,-1), int borderType=BORDER_DEFAULT ) + blur(image,new_image,sz,pt); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_borderInterpolate.cpp b/sci_gateway/cpp/opencv_borderInterpolate.cpp new file mode 100644 index 0000000..e8f0464 --- /dev/null +++ b/sci_gateway/cpp/opencv_borderInterpolate.cpp @@ -0,0 +1,149 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax :return_int=borderInterpolate(int p,int len,int borderType) +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int borderInterpolate(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr1 = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double* pstData = NULL; + char **pstData2 = NULL; + double *rrows; + double *rcols; + double p; + double len; + int borderType; + int error; + // checking input argument + CheckInputArgument(pvApiCtx,3,3); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + // get Address of first input + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + //check for any error + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of first argument + error=getScalarDouble(pvApiCtx,piAddr1,&p) ; + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //second argument + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of second argument + error=getScalarDouble(pvApiCtx,piAddr2,&len) ; + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //third argument + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData2 = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i = 0 ; i < iRows * iCols ; i++) + { + pstData2[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, pstData2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!strcmp(pstData2[0],"BORDER_REFLECT_101")) + { + borderType=BORDER_REFLECT_101; + } + else if(!strcmp(pstData2[0],"BORDER_WRAP")) + { + borderType=BORDER_WRAP; + } + else if(!strcmp(pstData2[0]," BORDER_WRAP")) + { + borderType=BORDER_WRAP; + } + else if(!strcmp(pstData2[0]," BORDER_TRANSPARENT")) + { + borderType= BORDER_TRANSPARENT; + } + else if(!strcmp(pstData2[0]," BORDER_CONSTANT")) + { + borderType= BORDER_TRANSPARENT; + } + //calling open cv function for borderInterpolate + double result; + + result=borderInterpolate(p,len,borderType) ; + error=createScalarDouble(pvApiCtx,nbInputArgument(pvApiCtx)+1,result); + if(error!=0) + { + sciprint("error occurred"); + return 0; + } + //returning output + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} + diff --git a/sci_gateway/cpp/opencv_boundingRect.cpp b/sci_gateway/cpp/opencv_boundingRect.cpp new file mode 100644 index 0000000..5bf8707 --- /dev/null +++ b/sci_gateway/cpp/opencv_boundingRect.cpp @@ -0,0 +1,82 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + rectangle_dimensions_arry(height, width, x, y) = boundingRect ( vector ) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_boundingRect(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddrNew = NULL; + int i,j,k ; + double *points; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + +//retreive the points + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols,&points); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + vector actualPoints; + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int boxfilter(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddr7 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double* pstData = NULL; + char** pstData2 = NULL; + double *rrows; + double *rcols; + int noofitem; + double ddepth; + bool normalize; + int borderType,i; + int error; + double sz1,sz2; + double pi1,pi2; + // checking input argument + CheckInputArgument(pvApiCtx,7,7); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + Mat img; + retrieveImage(img,1); + //second argument + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of second argument + error=getScalarDouble(pvApiCtx,piAddr2,&ddepth); + if(error!=0) + { + sciprint("error in retrieving second argument"); + return 0; + } + //third argument + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of third argument + error=getScalarDouble(pvApiCtx,piAddr3,&sz1); + if(error!=0) + { + sciprint("error in retrieving second argument"); + return 0; + } + + //fourth argument + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of fourth argument + error=getScalarDouble(pvApiCtx,piAddr4,&sz2); + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //fifth argument + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of fifth argument + error=getScalarDouble(pvApiCtx,piAddr5,&pi1); + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //sixth argument + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of sixth argument + error=getScalarDouble(pvApiCtx,piAddr6,&pi2); + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //seventh argument + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of seventh argument + sciErr = getMatrixOfString(pvApiCtx, piAddr7, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr7, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData2 = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + pstData2[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr7, &iRows, &iCols, piLen, pstData2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!strcmp(pstData2[0],"True")) + { + normalize=true; + } + else if(!strcmp(pstData2[0],"False")) + { + normalize=true; + + } + //size constructor + Size ksize=Size(sz1,sz2); + //anchor constructor + Point anchor=Point(pi1,pi2); + Mat dst; + //call to open cv boxfilter + boxFilter(img,dst,ddepth,ksize,anchor,normalize,BORDER_DEFAULT); + //returning variable to scilab console + string tempstring = type2str(dst.type()); + char* checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + //returning output variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + + + } +} diff --git a/sci_gateway/cpp/opencv_bwconvhull.cpp b/sci_gateway/cpp/opencv_bwconvhull.cpp new file mode 100644 index 0000000..7e0a09d --- /dev/null +++ b/sci_gateway/cpp/opencv_bwconvhull.cpp @@ -0,0 +1,129 @@ +/******************************************************** +Function :bwconvexhull +Syntax :B=bwconvexhull(A) + B=bwconvexhull(A,"Object",n) + n :4 or 8 +Author: Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + // #include "string.h" + #include "../common.h" + + int opencv_bwconvhull(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + char* pstData = NULL; + int iRet = 0; + double n; + //checking input argument + CheckInputArgument(pvApiCtx, 1, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat src,src_gray; + int thresh = 100,max_thresh = 255; + retrieveImage(src, 1); + cvtColor( src, src_gray, COLOR_BGR2GRAY ); + blur( src_gray, src_gray, Size(3,3) ); + Mat src_copy = src.clone(); + Mat threshold_output; + vector > contours; + vector hierarchy; + threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY ); + findContours( threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) ); + vector >hull( contours.size() ); + for( size_t i = 0; i < contours.size(); i++ ) + { + convexHull( Mat(contours[i]), hull[i], false ); + } + Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 ); + for( size_t i = 0; i< contours.size(); i++ ) + { + Scalar color = Scalar(255,255,255); + /* if only 1 input argument line connectvity n will take default value 8 */ + if(nbInputArgument(pvApiCtx)==1) + { + drawContours( drawing, contours, (int)i, color, 1, 8, vector(), 0, Point() ); + drawContours( drawing, hull, (int)i, color,CV_FILLED, 8, vector(), 0, Point()); + } + else if(nbInputArgument(pvApiCtx)==3) + { + /* retrieve second argument */ + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getAllocatedSingleString(pvApiCtx, piAddr2, &pstData); + } + } + else + { + sciprint(" The second argument should be string "); + return 0; + } + if(strcasecmp(pstData,"Object")==0) + { + /*retrieve third argument */ + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &n); + if(intErr) + { + return intErr; + } + // The error checks for the function + if((n!=4)&&(n!=8)) + { + sciprint("The value of line connectivity must be 8 or 4"); + return 0; + } + drawContours( drawing, contours, (int)i, color, 1, n, vector(), 0, Point() ); + drawContours( drawing, hull, (int)i, color,CV_FILLED, n, vector(), 0, Point()); + } + else + { + sciprint(" The second argument must be 'Object'"); + return 0; + } + } + } + string tempstring = type2str(drawing.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,drawing,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +} diff --git a/sci_gateway/cpp/opencv_bwdistgeodesic.cpp b/sci_gateway/cpp/opencv_bwdistgeodesic.cpp new file mode 100644 index 0000000..7f83652 --- /dev/null +++ b/sci_gateway/cpp/opencv_bwdistgeodesic.cpp @@ -0,0 +1,88 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = bwdistgeodesic(input_image, mask) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_bwdistgeodesic(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get the input image from the Scilab environment + Mat marker, mask; + retrieveImage(marker, 1); + retrieveImage(mask, 2); + + if (marker.type() != CV_8UC1) + { + Mat tempM; + tempM = marker.clone(); + cvtColor(tempM, marker, CV_BGR2GRAY); + } + + if (mask.type() != CV_8UC1) + { + Mat tempM; + tempM = mask.clone(); + cvtColor(tempM, mask, CV_8UC1); + } + + int i = 1; + + Mat temp0; + Mat temp2 = marker.clone(); + Mat fin_image = marker.clone(); + + do { + temp0 = temp2.clone(); + Mat temp1, temp3; + dilate(temp0, temp1, Mat()); + min(temp1, mask, temp2); + temp3 = temp2 - temp0; + temp3.convertTo(temp3, CV_32F); + temp3 = temp3 * ((255 - i)/255.0); + temp3.convertTo(temp3, CV_8UC1); + max(fin_image, temp3, fin_image); + i++; + } while((countNonZero(temp0 != temp2) != 0) && (i != 255)); + + + string tempstring = type2str(fin_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, fin_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_bwlookup.cpp b/sci_gateway/cpp/opencv_bwlookup.cpp new file mode 100644 index 0000000..5471e55 --- /dev/null +++ b/sci_gateway/cpp/opencv_bwlookup.cpp @@ -0,0 +1,180 @@ +/* + * bwlookup + * + * 2*2 and 3*3 non linear filtering of a binary image based on a lookup table - + *lut which is taken as input from the user + * + */ + +// Created by Samiran Roy, mail: samiranroy@cse.iitb.ac.in +// An implementation of bwlookup +// Usage: +// bwlookup(I,lut) + +// I is the input binary grayscale image. If the image is not binary, it is +// converted to one. +// lut is a 1*16 double vector [2*2 filtering], or a [1*512] double vector [3*3 +// filtering] +// The indexing method used is the same as Matlab bwlookup: +// http://in.mathworks.com/help/images/ref/bwlookup.html + +// Known Changes from Matlab: +/* + * 1) None, as of now + */ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" { +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" + +int opencv_bwlookup(char* fname, unsigned long fname_len) { + SciErr sciErr; + int intErr = 0; + + int* piAddr = NULL; + + // checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + // get input matrix + + Mat image; + retrieveImage(image, 1); + + if (image.channels() > 1) { + sciprint("The image must be grayscale."); + return -1; + } + + double* lut; + + int iRows = 0, iCols = 0; + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) { + printError(&sciErr, 0); + return -1; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &lut); + if (sciErr.iErr) { + printError(&sciErr, 0); + return -1; + } + + // Error Checking + + if (!((iCols == 16) || (iCols == 512))) { + sciprint("Expected LUT (argument 2) to have 16 or 512 elements.\n"); + + return -1; + } + + if (iRows != 1) { + sciprint("Expected input number 2, LUT, to be a vector.\n"); + return -1; + } + + if (image.channels() != 1) { + sciprint("Expected input number 1, A, to be two-dimensional.\n"); + return -1; + } + + // temporary copy of image to perform computation + // converting the image to a binary image + Mat tempimg = Mat::zeros(image.size(), CV_8U); + + for (int i = 0; i < image.rows; i++) { + for (int j = 0; j < image.cols; j++) { + if (image.at(i, j) != 0) tempimg.at(i, j) = 1; + } + } + + // pad the temporary copy of the image with zeroes to handle border cases + copyMakeBorder(tempimg, tempimg, 1, 1, 1, 1, BORDER_CONSTANT, 0); + + // output images + Mat new_image = Mat::zeros(image.size(), CV_32F); + + // temporary variables + int ii, jj; + int index; + + // 2*2 filtering + if (iCols == 16) { + for (int i = 0; i < image.rows; i++) { + for (int j = 0; j < image.cols; j++) { + ii = i + 1; + jj = j + 1; + + index = tempimg.at(ii, jj) * 1 + + tempimg.at(ii, jj + 1) * 2 + + tempimg.at(ii + 1, jj) * 4 + + tempimg.at(ii + 1, jj + 1) * 8; + + new_image.at(i, j) = lut[ 0, index ]; + } + } + } + + // 3*3 filtering + if (iCols == 512) { + for (int i = 0; i < image.rows; i++) { + for (int j = 0; j < image.cols; j++) { + ii = i + 1; + jj = j + 1; + + index = tempimg.at(ii - 1, jj - 1) * 1 + + tempimg.at(ii - 1, jj) * 2 + + tempimg.at(ii - 1, jj + 1) * 4 + + tempimg.at(ii, jj - 1) * 8 + + tempimg.at(ii, jj) * 16 + + tempimg.at(ii, jj + 1) * 32 + + tempimg.at(ii + 1, jj - 1) * 64 + + tempimg.at(ii + 1, jj) * 128 + + tempimg.at(ii + 1, jj + 1) * 256; + + new_image.at(i, j) = lut[ 0, index ]; + } + } + } + + // sciprint("\n"); + + // for (int i = 0; i < new_image.rows; i++) { + // for (int j = 0; j < new_image.cols; j++) { + // sciprint("%i ", new_image.at(i,j)); + + // } + + // sciprint("\n"); + // } + + // new_image is sent to scilab as output + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char* checker; + checker = (char*)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_bwulterode.cpp b/sci_gateway/cpp/opencv_bwulterode.cpp new file mode 100644 index 0000000..e9d0206 --- /dev/null +++ b/sci_gateway/cpp/opencv_bwulterode.cpp @@ -0,0 +1,92 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = bwulterode(input_image) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + void bwulterode_imreconstruct(Mat, Mat, Mat&); + + int opencv_bwulterode(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get the input image from the Scilab environment + Mat gray_image, fin_image, m, image; + retrieveImage(gray_image, 1); + + for (int i = 0; i < gray_image.cols; i++) + { + for (int j = 0; j < gray_image.rows; j++) + { + unsigned char val = gray_image.at(i,j); + if (!(val == 0 || val == 1 || val == 255)) + { + sciprint("Please enter binary image."); + return 0; + } + } + } + + if (gray_image.type() != CV_8UC1) + { + Mat temp; + temp = gray_image.clone(); + cvtColor(temp, gray_image, CV_BGR2GRAY); + } + + distanceTransform(gray_image, image, CV_DIST_L2, DIST_MASK_5); + subtract(image, 1, gray_image); + bwulterode_imreconstruct(image, gray_image, m); + subtract(image, m, gray_image); + fin_image = gray_image * 255; + + string tempstring = type2str(fin_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, fin_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } + void bwulterode_imreconstruct(Mat g, Mat f, Mat& dest) + { + Mat m0, m1, m; + m1 = f; + do { + m0 = m1.clone(); + dilate(m0, m, Mat()); + min(g, m, m1); + } while(countNonZero(m1 != m0) != 0); + dest = m1.clone(); + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_canny.cpp b/sci_gateway/cpp/opencv_canny.cpp new file mode 100644 index 0000000..010d634 --- /dev/null +++ b/sci_gateway/cpp/opencv_canny.cpp @@ -0,0 +1,121 @@ +/******************************************************** +Authors: Sukul Bagai, Abhilasha Sancheti, Shubheksha Jalan +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int opencv_canny(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double aperture, threshold1, threshold2, gradient; + bool L2gradient = false; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + cvtColor( image, image, CV_BGR2GRAY ); + + //for value of threshold1 + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &threshold1); + if(intErr) + { + return intErr; + } + + + //for value of threshold2 + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &threshold2); + if(intErr) + { + return intErr; + } + + + //for value of aperture + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &aperture); + if(intErr) + { + return intErr; + } + + + //for value of gradient as an integer and convert to bool accordingly + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &gradient); + if(intErr) + { + return intErr; + } + + if ( gradient == 1){ + L2gradient = true; + } + Mat new_image; + Canny(image, new_image, threshold1, threshold2, aperture, L2gradient); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_circle.cpp b/sci_gateway/cpp/opencv_circle.cpp new file mode 100644 index 0000000..928c251 --- /dev/null +++ b/sci_gateway/cpp/opencv_circle.cpp @@ -0,0 +1,203 @@ +/******************************************************** + Author: Abhilasha Sancheti +********************************************************* + return_image = circle(input_image , x_coordinate_of_centre , y_coordinate_centre , radius, r_value,g_value,b_value,thickness,linetype,shift); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_circle(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int i,j,k; + double thickness=1 , linetype=8,shift =0 ,centre_x ,centre_y,radius,r_value,g_value,b_value; + + + //checking input argument + CheckInputArgument(pvApiCtx, 10, 10); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of centre + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,¢re_x); + if(intErr) + return intErr; + + + //for value of y coordinate of centre + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,¢re_y); + if(intErr) + return intErr; + + //for value of radius + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&radius); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&r_value); + if(intErr) + return intErr; + + //for value of G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7,&b_value); + if(intErr) + return intErr; + + // for thickness of circle default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&thickness); + if(intErr) + return intErr; + + //for line type of circle default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr9,&linetype); + if(intErr) + return intErr; + + // for shift in circle defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Syntax: sciErr = getMatrixOfInteger8(pvApiCtx, piAddr8, &iRows8, &iCols8, &pcData); + intErr = getScalarDouble(pvApiCtx, piAddr10,&shift); + if(intErr) + return intErr; + + // checking radius parameter + if (radius <0) + { + radius=3; + sciprint("Radius should be positive , 3 value was used instead\n"); + } + // checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead\n"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead\n"); + } + //temporary pt variable, to use in function as centre + Point pt(centre_x,centre_y); + /// Draw the circles detected + + circle( src, pt, radius, Scalar(r_value,g_value,b_value), thickness, linetype, shift); + +//// sending the image + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway/cpp/opencv_clipline.cpp b/sci_gateway/cpp/opencv_clipline.cpp new file mode 100644 index 0000000..2e13412 --- /dev/null +++ b/sci_gateway/cpp/opencv_clipline.cpp @@ -0,0 +1,149 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + return_image = clipline( width, height, x1 , y1 , x2,y2 ) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_clipline(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int i,j,k; + double x1,y1,x2,y2,width,height; + + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + //for width of the rectangle + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr1 ,&width); + if(intErr) + return intErr; + + // for height of the rectangle + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,&height); + if(intErr) + return intErr; + + //for value of x coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &x1); + if(intErr) + return intErr; + + //for value of y coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&y1); + if(intErr) + return intErr; + + //for value of x coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&x2); + if(intErr) + return intErr; + + ///for value of y coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&y2); + if(intErr) + return intErr; + + + if(height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead"); + height=1; + } + if(width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead"); + width=1; + } + //temporary pt1 variable, to use in function as first line point + Point pt1(x1,y1); + //temporary pt2 variable, to use in function as second line point + Point pt2(x2,y2); + // image size + Size sz(width,height); + Rect r(0,0,width,height); + //// calling open cv clipLine function + + bool ans = clipLine( sz , pt1, pt2); + int actualans=(int)ans; + + // returning the result to the scilab environment + // Syntax: SciErr createMatrixOfBoolean(void* _pvCtx, int _iVar, int _iRows, int _iCols, const int* _piBool) + intErr= createScalarBoolean(pvApiCtx, nbInputArgument(pvApiCtx) + 1, actualans); + if(intErr) + return intErr; + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway/cpp/opencv_contourarea.cpp b/sci_gateway/cpp/opencv_contourarea.cpp new file mode 100644 index 0000000..ba45b09 --- /dev/null +++ b/sci_gateway/cpp/opencv_contourarea.cpp @@ -0,0 +1,125 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : return_area=contourarea(InputArray contour, bool oriented); */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +//#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + int contourarea(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double *pstData = NULL; + char **pstData2 = NULL; + double *rrows; + double *rcols; + double res; + int error; + + //checking input argument + CheckInputArgument(pvApiCtx,2,2); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + // get Address of first input + sciErr =getVarAddressFromPosition(pvApiCtx,1,&piAddr); + //check for any error + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrieve input array + //SciErr getMatrixOfDouble(void* _pvCtx, int* _piAddress, int* _piRows, int* _piCols, double** _pdblReal) + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols,&pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int k=0; + vector contours; + for(int i=0;i), clockwise(1/0) , returnpoints(1/0) ) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_convexhull(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + double *pstData = NULL; + int i,j,k ; + double clkwise; + double returnpoints; + + + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + +//retreive the contour parameter + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols,&pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //cout<<*(pstData + 0*2 + 0)<<" "<<*(pstData + 0*2 + 1)< cont1; + for ( i = 0 ; i < iRows ; i++) + { + Point pt(*(pstData + 0*iRows + i),*(pstData + 1*iRows + i)); + cont1.push_back(pt); + } + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&clkwise); + if(intErr) + return intErr; + + + +//// to retreive the value of returnpoint + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&returnpoints); + if(intErr) + return intErr; + + + bool clkWise, returnPoint; +/// setting clkwise parameter of function + if ( clkwise ==1) + clkWise = true; + else if (clkwise == 0) + clkWise = false; + else + { + clkWise = false; + sciprint("only 0/1 acceptable , taking 0 instead"); + } +///// setting returnpoint parameter of function + if ( returnpoints ==1) + returnPoint = true; + else if (returnpoints == 0) + returnPoint = false; + else + { + returnPoint = true; + sciprint("only 0/1 acceptable , taking 1 instead"); + } + + vector hull; + convexHull(Mat(cont1), hull , clkWise , returnPoint); + int row = hull.size(); + double *cor = (double *)malloc(row*2*sizeof(double)); + for (int i=0;i +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_convmtx2(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + double m, n; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat H; + retrieveImage(H,1); + + //for value of m + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &m); + if(intErr) + return intErr; + + //for value of n + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &n); + if(intErr) + return intErr; + + + //taking the cases which can lead to an error + + int s1 = H.rows; + int s2 = H.cols; + + Mat T; + T = Mat::zeros((m-s1+1)*(n-s2+1),m*n,H.type()); + + if(m(k,(i+p)*n+j+q)=H.at(p,q); + } + k++; + } + } + + //printf("%f %f %f" , H.at(0,0), H.at(1,1), H.at(2,2)); + + //namedWindow( "Display window", WINDOW_AUTOSIZE );// Create a window for display. + //imshow( "Display window", T ); + + //returning image + string tempstring = type2str(T.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,T,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_copymakeborder.cpp b/sci_gateway/cpp/opencv_copymakeborder.cpp new file mode 100644 index 0000000..51175fb --- /dev/null +++ b/sci_gateway/cpp/opencv_copymakeborder.cpp @@ -0,0 +1,195 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* + void copyMakeBorder(InputArray image, int top, int bottom, int left, int right, int borderType, const Scalar& value=Scalar() ) +********************************************************/ +// for BORDER_CONSTANT, enter a 4 element array as Scalar. For any other border type, pass 0. + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_copymakeborder(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int i,j,k; + char **borderType; + double top, bottom, right, left; + double *value = NULL; + //checking input argument + CheckInputArgument(pvApiCtx, 6, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for top + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &top); + if(intErr) + { + return intErr; + } + + //for bottom + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &bottom); + if(intErr) + { + return intErr; + } + + //for left + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &left); + if(intErr) + { + return intErr; + } + + //for bottom + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &right); + if(intErr) + { + return intErr; + } + + + sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr6, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr6, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr6, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for array of Scalar object + sciErr = getVarAddressFromPosition(pvApiCtx, 7, &piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr7, &iRows, &iCols ,&value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Mat new_image(Size(image.cols+left+right, image.rows+top+bottom), image.type()); + + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0 && iCols == 4 ) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_CONSTANT, Scalar(value[0], value[1], value[2], value[3])); + else if(strcmp(borderType[0], "BORDER_CONSTANT") == 0 && iCols == 3) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_CONSTANT, Scalar(value[0], value[1], value[2])); + else if(strcmp(borderType[0], "BORDER_DEFAULT") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_DEFAULT); + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_REPLICATE); + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_REFLECT); + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_REFLECT_101); + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_WRAP); + + // namedWindow ("window", 1); + // imshow("window", new_image); + // waitKey(0); + // destroyWindow("window"); + +free(borderType); + +string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_corner.cpp b/sci_gateway/cpp/opencv_corner.cpp new file mode 100644 index 0000000..7502f96 --- /dev/null +++ b/sci_gateway/cpp/opencv_corner.cpp @@ -0,0 +1,166 @@ +/******************************************************** +Author: Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + // #include "string.h" + #include "../common.h" + + int opencv_corner(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + char* pstData = NULL; + char* pstData1 = NULL; + int iRet = 0; + int iRet1 = 0; + CheckInputArgument(pvApiCtx, 1, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat src,src_gray; + int thresh = 200; + int max_thresh = 255; + retrieveImage(src, 1); + cvtColor( src, src_gray, COLOR_BGR2GRAY ); + Mat dst, dst_norm, drawing; + dst = Mat::zeros( src.size(), CV_32FC1 ); + int blockSize = 2; + int apertureSize = 3; + double k = 0.04; + int maxCorners = 23; + RNG rng(12345); + if(nbInputArgument(pvApiCtx)==1) + { + cornerHarris( src_gray, dst, blockSize, apertureSize, k, BORDER_DEFAULT ); + normalize( dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat() ); + convertScaleAbs( dst_norm, drawing ); + for( int j = 0; j < dst_norm.rows ; j++ ) + { + for( int i = 0; i < dst_norm.cols; i++ ) + { + if( (int) dst_norm.at(j,i) > thresh ) + { + circle( drawing, Point( i, j ), 5, Scalar(0), 2, 8, 0 ); + } + } + } + } + else if(nbInputArgument(pvApiCtx)==3) + { + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getAllocatedSingleString(pvApiCtx, piAddr2, &pstData); + } + } + else + { + sciprint(" The second argument should be string "); + return 0; + } + if(strcasecmp(pstData,"Method")==0) + { + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr3)) + { + if(isScalar(pvApiCtx, piAddr3)) + { + iRet1 = getAllocatedSingleString(pvApiCtx, piAddr3, &pstData1); + } + } + else + { + sciprint(" The third argument should be string "); + return 0; + } + if(strcasecmp(pstData1,"Harris")==0) + { + cornerHarris( src_gray, dst, blockSize, apertureSize, k, BORDER_DEFAULT ); + normalize( dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat() ); + convertScaleAbs( dst_norm, drawing ); + for( int j = 0; j < dst_norm.rows ; j++ ) + { + for( int i = 0; i < dst_norm.cols; i++ ) + { + if( (int) dst_norm.at(j,i) > thresh ) + { + circle( drawing, Point( i, j ), 5, Scalar(0), 2, 8, 0 ); + } + } + } + } + else if(strcasecmp(pstData1,"MinEigenvalue")==0) + { + if( maxCorners < 1 ) + { + maxCorners = 1; + } + /// Parameters for Shi-Tomasi algorithm + vector corners; + double qualityLevel = 0.01; + double minDistance = 10; + int blockSize = 3; + bool useHarrisDetector = false; + double k = 0.04; + /// Copy the source image + drawing = src.clone(); + goodFeaturesToTrack( src_gray,corners,maxCorners,qualityLevel,minDistance,Mat(),blockSize,useHarrisDetector,k ); + int r = 4; + for( int i = 0; i < corners.size(); i++ ) + { + circle( drawing, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),rng.uniform(0,255)), -1, 8, 0 ); + } + } + else + { + sciprint(" The third argument must be 'Harris' or 'Mineigenvalue' "); + return 0; + } + } + else + { + sciprint(" The second argument must be 'Method' "); + return 0; + } + } + string tempstring = type2str(drawing.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,drawing,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +} diff --git a/sci_gateway/cpp/opencv_cornerEigenValsAndVecs.cpp b/sci_gateway/cpp/opencv_cornerEigenValsAndVecs.cpp new file mode 100644 index 0000000..205759d --- /dev/null +++ b/sci_gateway/cpp/opencv_cornerEigenValsAndVecs.cpp @@ -0,0 +1,127 @@ +/******************************************************** +Author: Shubheksha Jalan +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + + int opencv_cornerEigenValsAndVecs(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + double blockSize, ksize; + char **borderType; + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + cvtColor(image,image,CV_BGR2GRAY); + + //for block size + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &blockSize); + if(intErr) + { + return intErr; + } + + //for ksize + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &ksize); + if(intErr) + { + return intErr; + } + + //for border type + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Mat new_image; + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0){ + cornerEigenValsAndVecs(image, new_image, blockSize, ksize, BORDER_CONSTANT); + } + else if(strcmp(borderType[0], "BORDER_DEFAULT") == 0) + cornerEigenValsAndVecs(image, new_image, blockSize, ksize, BORDER_DEFAULT); + free(borderType); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_cornerHarris.cpp b/sci_gateway/cpp/opencv_cornerHarris.cpp new file mode 100644 index 0000000..d517eec --- /dev/null +++ b/sci_gateway/cpp/opencv_cornerHarris.cpp @@ -0,0 +1,144 @@ +/******************************************************** +Author: Shubheksha Jalan +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + + int opencv_cornerHarris(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j; + double blockSize, ksize, k; + char **borderType; + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + cvtColor(image,image,CV_BGR2GRAY); + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &blockSize); + if(intErr) + { + return intErr; + } + + //for ksize + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &ksize); + if(intErr) + { + return intErr; + } +//for value of k + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &k); + if(intErr) + { + return intErr; + } + + //for border type + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr5, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr5, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr5, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + cout<<"String\n"; + + Mat new_image(image.rows, image.cols, CV_32FC1); + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0){ + cornerHarris(image, new_image, blockSize, ksize, k, BORDER_CONSTANT); + } + else if(strcmp(borderType[0], "BORDER_DEFAULT") == 0) + cornerHarris(image, new_image, blockSize, ksize, k, BORDER_DEFAULT); + + namedWindow ("window", 1); + imshow("window", new_image); + waitKey(0); + destroyWindow("window"); + + new_image.convertTo(new_image, CV_32FC1, 1.0/255.0); + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_cornerMinEigenVal.cpp b/sci_gateway/cpp/opencv_cornerMinEigenVal.cpp new file mode 100644 index 0000000..19123e5 --- /dev/null +++ b/sci_gateway/cpp/opencv_cornerMinEigenVal.cpp @@ -0,0 +1,125 @@ +/******************************************************** +Author: Shubheksha Jalan +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + + int opencv_cornerMinEigenVal(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + double blockSize, ksize; + char **borderType; + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + cvtColor(image,image,CV_BGR2GRAY); + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &blockSize); + if(intErr) + { + return intErr; + } + + //for ksize + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &ksize); + if(intErr) + { + return intErr; + } + + //for border type + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + cout<<"String\n"; + + Mat new_image; + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0){ + cornerMinEigenVal(image, new_image, blockSize, ksize, BORDER_CONSTANT); + } + else if(strcmp(borderType[0], "BORDER_DEFAULT") == 0) + cornerMinEigenVal(image, new_image, blockSize, ksize, BORDER_DEFAULT); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_cvtColor.cpp b/sci_gateway/cpp/opencv_cvtColor.cpp new file mode 100644 index 0000000..2959dda --- /dev/null +++ b/sci_gateway/cpp/opencv_cvtColor.cpp @@ -0,0 +1,183 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* + void cvtColor(InputArray src, int code, int dstCn=0 ) +********************************************************/ +// number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "sciprint.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_cvtColor(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + double dstCn; + int code; + char **codeString = NULL; + int i, j, k; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 3); + CheckOutputArgument(pvApiCtx, 1,1) ; + + + Mat src; + retrieveImage(src,1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + codeString = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + codeString[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, codeString); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &dstCn); + if(intErr){ + return intErr; + } + + Mat dst; + + if(strcmp(codeString[0], "CV_BGR2GRAY") == 0) + code = CV_BGR2GRAY; + else if(strcmp(codeString[0], "CV_RGB2GRAY") == 0) + code = CV_RGB2GRAY; + else if(strcmp(codeString[0], "CV_GRAY2BGR") == 0) + code = CV_GRAY2BGR; + else if(strcmp(codeString[0], "CV_GRAY2RGB") == 0) + code = CV_GRAY2RGB; + else if(strcmp(codeString[0], "CV_BGR2XYZ") == 0) + code = CV_BGR2XYZ; + else if(strcmp(codeString[0], "CV_RGB2XYZ") == 0) + code = CV_RGB2XYZ; + else if(strcmp(codeString[0], "CV_XYZ2BGR") == 0) + code = CV_XYZ2BGR; + else if(strcmp(codeString[0], "CV_XYZ2RGB") == 0) + code = CV_XYZ2RGB; + else if(strcmp(codeString[0], "CV_BGR2YCrCb") == 0) + code = CV_BGR2YCrCb; + else if(strcmp(codeString[0], "CV_RGB2YCrCb") == 0) + code = CV_RGB2YCrCb; + else if(strcmp(codeString[0], "CV_YCrCb2BGR") == 0) + code = CV_YCrCb2BGR; + else if(strcmp(codeString[0], "CV_YCrCb2RGB") == 0) + code = CV_YCrCb2RGB; + else if(strcmp(codeString[0], "CV_BGR2HSV") == 0) + code = CV_BGR2HSV; + else if(strcmp(codeString[0], "CV_RGB2HSV") == 0) + code = CV_RGB2HSV; + else if(strcmp(codeString[0], "CV_HSV2BGR") == 0) + code = CV_HSV2BGR; + else if(strcmp(codeString[0], "CV_HSV2RGB") == 0) + code = CV_HSV2RGB; + else if(strcmp(codeString[0], "CV_BGR2HLS") == 0) + code = CV_BGR2HLS; + else if(strcmp(codeString[0], "CV_RGB2HLS") == 0) + code = CV_RGB2HLS; + else if(strcmp(codeString[0], "CV_HLS2BGR") == 0) + code = CV_HLS2BGR; + else if(strcmp(codeString[0], "CV_HLS2RGB") == 0) + code = CV_HLS2RGB; + else if(strcmp(codeString[0], "CV_BGR2Lab") == 0) + code = CV_BGR2Lab; + else if(strcmp(codeString[0], "CV_RGB2Lab") == 0) + code = CV_RGB2Lab; + else if(strcmp(codeString[0], "CV_Lab2BGR") == 0) + code = CV_Lab2BGR; + else if(strcmp(codeString[0], "CV_Lab2RGB") == 0) + code = CV_Lab2RGB; + else if(strcmp(codeString[0], "CV_BGR2Luv") == 0) + code = CV_BGR2Luv; + else if(strcmp(codeString[0], "CV_RGB2Luv") == 0) + code = CV_RGB2Luv; + else if(strcmp(codeString[0], "CV_Luv2BGR") == 0) + code = CV_Luv2BGR; + else if(strcmp(codeString[0], "CV_Luv2RGB") == 0) + code = CV_Luv2RGB; + else{ + + sciprint("Invalid code"); + return 0; + } + + cvtColor(src, dst, code, dstCn); + + // namedWindow ("window", 1); + // imshow("window", dst); + // waitKey(0); + // destroyWindow("window"); + + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + free(checker); + + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_deconvlucy.cpp b/sci_gateway/cpp/opencv_deconvlucy.cpp new file mode 100644 index 0000000..9523f91 --- /dev/null +++ b/sci_gateway/cpp/opencv_deconvlucy.cpp @@ -0,0 +1,120 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = deconvlucy(input_image, PSF) + return_image = deconvlucy(input_image, PSF, no_of_iterations) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_deconvlucy(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + int i; + double h; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 3); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get the input image from the Scilab environment + Mat image, psf; + retrieveImage(image, 1); + retrieveImage(psf, 2); + + // Check if number of arguments are three, + // if so, then the number of iterations is also + // specified. + // If not, then set number of iterations as + // ten. + if (nbInputArgument(pvApiCtx) == 3) + { + // Get the address of the iterations scalar + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the no. of iterations scalar + intErr = getScalarDouble(pvApiCtx, piAddr, &h); + if(intErr) + { + return intErr; + } + + if (h < 0) + { + sciprint("Please enter a nonnegative scalar for number of iterations.\n"); + return 0; + } + } + else + h = 10; + + // Check if the input image is grayscale or not, + // if not, convert it to grayscale. + if (image.type() != CV_8UC1) + { + Mat temp; + temp = image.clone(); + cvtColor(temp, image, CV_BGR2GRAY); + } + + // Check if the PSF is grayscale or not, + // if not, convert it to grayscale. + if (psf.type() != CV_8UC1) + { + Mat temp; + temp = psf.clone(); + cvtColor(temp, psf, CV_BGR2GRAY); + } + + // Apply the Lucy-Richardson method for deblurring + // an image. + Mat convRes, convRes2, temp; + temp = image.clone(); + for (i = 0; i < h; i++) + { + filter2D(temp, convRes, -1, psf); + max((temp - convRes), 0, convRes2); + min((temp + convRes2), 255, convRes); + temp = convRes.clone(); + } + image = temp.clone(); + + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_decorrstretch.cpp b/sci_gateway/cpp/opencv_decorrstretch.cpp new file mode 100644 index 0000000..a3432a2 --- /dev/null +++ b/sci_gateway/cpp/opencv_decorrstretch.cpp @@ -0,0 +1,102 @@ +/*************************************************** +Author : Dhruti Shah +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + + Mat dstretch(Mat& input) + { + CV_Assert(input.channels() > 1); + + Mat dataMu, dataSigma, eigDataSigma, scale, stretch; + Mat targetMean = Mat(); + + Mat data = input.reshape(1, input.rows*input.cols); + + meanStdDev(input, dataMu, dataSigma); + + PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW); + + sqrt(pca.eigenvalues, eigDataSigma); + scale = Mat::diag(1/eigDataSigma); + stretch = Mat::diag(dataSigma); + + stretch.convertTo(stretch, CV_32F); + + Mat zmudata; + Mat repMu = repeat(dataMu.t(), data.rows, 1); + subtract(data, repMu, zmudata, Mat(), CV_32F); + + Mat transformed = zmudata*(pca.eigenvectors.t()*scale*pca.eigenvectors*stretch); + add(transformed, repMu, transformed, Mat(), CV_32F); + + Mat dstr32f = transformed.reshape(input.channels(), input.rows); + return dstr32f; + } + + int opencv_decorrstretch(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + char **type = NULL; + int i,j,k; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + Mat lab; + cvtColor(image, lab, CV_BGR2Lab); + Mat dstrlab32f = dstretch(lab); + Mat dstrlab8u; + dstrlab32f.convertTo(dstrlab8u, CV_8UC3); + Mat dstrlab2bgr; + cvtColor(dstrlab8u, dstrlab2bgr, CV_Lab2BGR); + Mat dstrbgr32f = dstretch(image); + Mat dstrbgr8u; + dstrbgr32f.convertTo(dstrbgr8u, CV_8UC3); + + + string tempstring = type2str(dstrbgr8u.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dstrbgr8u,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_demosaic.cpp b/sci_gateway/cpp/opencv_demosaic.cpp new file mode 100644 index 0000000..317e163 --- /dev/null +++ b/sci_gateway/cpp/opencv_demosaic.cpp @@ -0,0 +1,127 @@ +/******************************************************** + Author: V Srinivas +********************************************************* + void cvtColor(InputArray src, int code, int dstCn=0 ) +********************************************************/ +//Demosaicing using cvt color filter + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "sciprint.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_demosaic(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + double dstCn; + int code; + char **codeString = NULL; + int i, j, k; + + //checking input argument + CheckInputArgument(pvApiCtx, 2,2); + CheckOutputArgument(pvApiCtx, 1,1) ; + + + Mat src; + retrieveImage(src,1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + + codeString = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + codeString[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, codeString); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + + Mat dst; + + if(strcmp(codeString[0], "gbrg") == 0) + code = CV_BayerGR2RGB; + else if(strcmp(codeString[0], "bggr") == 0) + code = CV_BayerRG2RGB; + else if(strcmp(codeString[0], "rggb") == 0) + code = CV_BayerBG2RGB; + else if(strcmp(codeString[0], "grbg") == 0) + code = CV_BayerGB2RGB; + else{ + + sciprint("Invalid Bayer filter code"); + return 0; + } + + cvtColor(src, dst, CV_BayerRG2RGB, 0); + + // namedWindow ("window", 1); + // imshow("window", dst); + // waitKey(0); + // destroyWindow("window"); + + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + free(checker); + + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_demosaic.cpp~ b/sci_gateway/cpp/opencv_demosaic.cpp~ new file mode 100644 index 0000000..4c2ee4b --- /dev/null +++ b/sci_gateway/cpp/opencv_demosaic.cpp~ @@ -0,0 +1,127 @@ +/******************************************************** + Author: V Srinivas +********************************************************* + void cvtColor(InputArray src, int code, int dstCn=0 ) +********************************************************/ +//Demosaicing using cvt color filter + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "sciprint.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_demosaic(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + double dstCn; + int code; + char **codeString = NULL; + int i, j, k; + + //checking input argument + CheckInputArgument(pvApiCtx, 2,2); + CheckOutputArgument(pvApiCtx, 1,1) ; + + + Mat src; + retrieveImage(src,1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + + codeString = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + codeString[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, codeString); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + + Mat dst; + + if(strcmp(codeString[0], "gbrg") == 0) + code = CV_BayerGR2RGB; + else if(strcmp(codeString[0], "bggr") == 0) + code = CV_BayerRG2RGB; + else if(strcmp(codeString[0], "rggb") == 0) + code = CV_BayerBG2RGB; + else if(strcmp(codeString[0], "grbg") == 0) + code = CV_BayerGB2RGB; + else{ + + sciprint("Invalid Bayer filter code"); + return 0; + } + + cvtColor(src, dst, code, 0); + + // namedWindow ("window", 1); + // imshow("window", dst); + // waitKey(0); + // destroyWindow("window"); + + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + free(checker); + + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_dilate.cpp b/sci_gateway/cpp/opencv_dilate.cpp new file mode 100644 index 0000000..44b26f1 --- /dev/null +++ b/sci_gateway/cpp/opencv_dilate.cpp @@ -0,0 +1,119 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_dilate(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + char **type = NULL; + int i,j,k; + double anchorX,anchorY,iterations; + double *kernel = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for kernel + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &kernel); + if(sciErr.iErr) + { + printError(&sciErr,0); + return 0; + } + + double actualkernel[iRows][iCols]; + for(i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_ellipse(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11 = NULL; + int *piAddr12 = NULL; + int *piAddr13 = NULL; + int *piAddr14 = NULL; + int i,j,k; + double thickness=1 , linetype=8, shift=0 ,centre_x ,centre_y,first_axis,second_axis,angle,startAngle,endAngle,r_value,g_value,b_value; + + //checking input argument + CheckInputArgument(pvApiCtx, 14, 14); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of centre + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,¢re_x); + if(intErr) + return intErr; + + + //for value of y coordinate of centre + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,¢re_y); + if(intErr) + return intErr; + + //for value of first_axis + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&first_axis); + if(intErr) + return intErr; + + //for value of second_axis + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&first_axis); + if(intErr) + return intErr; + + //for value of angle + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6,&angle); + if(intErr) + return intErr; + + //for value of startAngle + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7,&startAngle); + if(intErr) + return intErr; + + //for value of endAngle + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&endAngle); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr9 ,&r_value); + if(intErr) + return intErr; + + //for value of G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr10 ,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr11,&b_value); + if(intErr) + return intErr; + + // for thickness of circle default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,12,&piAddr12); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr12,&thickness); + if(intErr) + return intErr; + + //for line type of circle default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,13,&piAddr13); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr13,&linetype); + if(intErr) + return intErr; + + // for shift in circle default : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,14,&piAddr14); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr14,&shift); + if(intErr) + return intErr; + + // checking radius parameter + if (first_axis < 0) + { + first_axis=3; + sciprint("first_axis should be positive , 3 value was used instead\n"); + } + if (second_axis < 0) + { + second_axis=3; + sciprint("second_axis should be positive , 3 value was used instead\n"); + } + if (first_axis < second_axis) + { + double temp; + temp=first_axis; + first_axis=second_axis; + second_axis=temp; + sciprint("first_axis should be greater than second_axis. Values swapped.\n"); + } + if(angle < 0 || angle > 360) + { + angle=45; + sciprint("Imvalid angle value used. Using 45 instead"); + } + if(startAngle < 0 || startAngle > 360) + { + startAngle=45; + sciprint("Imvalid startAngle value used. Using 120 instead"); + } + if(endAngle < 0 || endAngle > 360) + { + endAngle=45; + sciprint("Imvalid endAngle value used. Using 120 instead"); + } + // checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead\n"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead\n"); + } + //temporary pt variable, to use in function as centre + Point pt(centre_x,centre_y); + Size sz(first_axis,second_axis); + /// Draw the circles detected + + ellipse( src, pt, sz, angle, startAngle, endAngle, Scalar(r_value,g_value,b_value), thickness, linetype, shift); + +//// sending the image + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway/cpp/opencv_ellipse2poly.cpp b/sci_gateway/cpp/opencv_ellipse2poly.cpp new file mode 100644 index 0000000..e7edef4 --- /dev/null +++ b/sci_gateway/cpp/opencv_ellipse2poly.cpp @@ -0,0 +1,171 @@ +/******************************************************** + Author: Abhilasha Sancheti +********************************************************* + pts = ellipse2Poly(double center_x,double center_y,double width,double height,int angle, int arcStart, int arcEnd, int delta) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_ellipse2poly(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + double *pstData = NULL; + int i,j,k ; + double center_x,center_y,width,height,arcstart,arcend,delta,angle; + + + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 8); + CheckOutputArgument(pvApiCtx, 1, 1) ; + +//// to retreive the value of x coordinate of center + + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr1 ,¢er_x); + if(intErr) + return intErr; + + + +//// to retreive the value of y coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,¢er_y); + if(intErr) + return intErr; + +//// to retreive the value of width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&width); + if(intErr) + return intErr; + +//// to retreive the value of height + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&height); + if(intErr) + return intErr; + +//// to retreive the value of angle parameter + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&angle); + if(intErr) + return intErr; + +//// to retreive the value of arcstart parameter + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6,&arcstart); + if(intErr) + return intErr; + +//// to retreive the value of arcend parameter + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7,&arcend); + if(intErr) + return intErr; + +//// to retreive the value of delta parameter + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&delta); + if(intErr) + return intErr; + + + vector pts; + Point center(center_x,center_y); + Size axes(width,height); + ellipse2Poly(center, axes, angle, arcstart, arcend,delta, pts); + + int row = pts.size(); + double *cor = (double *)malloc(row*2*sizeof(double)); + for (int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_erode(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + char **type = NULL; + int i,j,k; + double anchorX,anchorY,iterations; + double *kernel; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for kernel + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &kernel); + if(sciErr.iErr) + { + printError(&sciErr,0); + return 0; + } + + double actualkernel[iRows][iCols]; + for(i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_fftshift(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + Mat image; + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + retrieveImage(image,1); + int cx = floor(image.cols/2); + int cy = floor(image.rows/2); + Mat q0(image, Rect(0, 0, cx, cy)); // Top-Left - Create a ROI per quadrant + Mat q1(image, Rect(cx, 0, cx, cy)); // Top-Right + Mat q2(image, Rect(0, cy, cx, cy)); // Bottom-Left + Mat q3(image, Rect(cx, cy, cx, cy)); // Bottom-Right + Mat tmp; // swap quadrants (Top-Left with Bottom-Right) + q0.copyTo(tmp); + q3.copyTo(q0); + tmp.copyTo(q3); + q1.copyTo(tmp); // swap quadrant (Top-Right with Bottom-Left) + q2.copyTo(q1); + tmp.copyTo(q2); + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,image,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_fillconvexpoly.cpp b/sci_gateway/cpp/opencv_fillconvexpoly.cpp new file mode 100644 index 0000000..1ce29c6 --- /dev/null +++ b/sci_gateway/cpp/opencv_fillconvexpoly.cpp @@ -0,0 +1,173 @@ +/************************************************* +Authors : Abhilasha Sancheti & Sukul Bagai +************************************************** + sample inputs : b=fillconvexpoly(a,points,4,0,0,0,8,0); (points: [0 0;0 100;100 0; 100 100]) + fillConvexpoly( image , points , r_value , g_value , b_value , linrtype,shift); +*************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_fillconvexpoly(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int i,j,k ; + double *pstData = NULL; + double npts ,r_value,g_value ,b_value ,linetype=8 ,shift=0; + + + + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 8); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + +//retreive the value of clockwise parameter + Mat image; + retrieveImage(image,1); + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } +///// store values in a contour + + Point *pts = (Point *)malloc(iRows * 2 * sizeof(double)); + for ( i = 0 ; i < iRows ; i++) + { + pts[i].x = *(pstData + i); + pts[i].y = *(pstData + 1*iRows + i); + } + + /// to retrieve the number of points + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &npts); + if(intErr) + { + return intErr; + } + +//// to retreive the value of r_value + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &r_value); + if(intErr) + { + return intErr; + } + /// to retreive the value of g_value + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &g_value); + if(intErr) + { + return intErr; + } + + /// to retreive the value of b_value + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &b_value); + if(intErr) + { + return intErr; + } + /// to retreive the value of linetype + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7, &linetype); + if(intErr) + { + return intErr; + } +/// to retreive the value of shift + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8, &shift); + if(intErr) + { + return intErr; + } + + + + // call the fillconvexpoly function of opencv + fillConvexPoly(image,pts,npts,Scalar(r_value,g_value,b_value),linetype,shift); + + //temp variable was not needed, hence has been discarded + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,image,1); //here, remove the temp as a parameter as it is not needed, and instead add 1 as the third parameter. 1 denotes that the first output argument will be this variable + free(checker); //free memory taken up by checker + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} diff --git a/sci_gateway/cpp/opencv_filter2D.cpp b/sci_gateway/cpp/opencv_filter2D.cpp new file mode 100644 index 0000000..650771b --- /dev/null +++ b/sci_gateway/cpp/opencv_filter2D.cpp @@ -0,0 +1,184 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = filter2D(input_image,"",kernel_matrix,anchor_x,anchor_y,delta); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_filter2D(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + char **ddepth = NULL; + int i,j,k; + double *kernel,anchorX,anchorY,delta; + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for kernel matrix + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&kernel); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n=iRows; + double kernelArray[n][n]; + //assigning values to actual kernelMatrix + for(i=0;i= n || anchorY >=n) + { + sciprint("Invalid anchor point given. Centre point (-1,-1) was used instead"); + anchorY = -1; + anchorX = -1; + } + Mat new_image(image.rows,image.cols,image.type()); + Point pt(anchorX,anchorY); + + int actualddepth; + + if(strcmp(ddepth[0],"CV_8U")==0) + actualddepth=CV_8U; + else if(strcmp(ddepth[0],"CV_16U")==0) + actualddepth=CV_16U; + else if(strcmp(ddepth[0],"CV_16S")==0) + actualddepth=CV_16S; + else if(strcmp(ddepth[0],"CV_32F")==0) + actualddepth=CV_32F; + else if(strcmp(ddepth[0],"CV_64F")==0) + actualddepth=CV_64F; + else + { + sciprint("Invalid type %s used. CV_8U was used instead by default",ddepth[0]); + actualddepth = CV_8U; + } + + //applying function + filter2D(image,new_image,actualddepth,kernelMatrix,pt,delta); + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_findContours.cpp b/sci_gateway/cpp/opencv_findContours.cpp new file mode 100644 index 0000000..05da2b3 --- /dev/null +++ b/sci_gateway/cpp/opencv_findContours.cpp @@ -0,0 +1,199 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + contours = findcontours(input_image(after canny is applied on it), mode, method, point_x, point_y) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_findContours(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen2=NULL; + int *piLen3 = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k, findmode , findmethod; + char **find_method=NULL , **mode = NULL; + double x,y; + + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat src; + retrieveImage(src,1); + + // for mode parameter of findContours + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + piLen2 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen2, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + mode= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + mode[i] = (char*)malloc(sizeof(char) * (piLen2[i] + 1));//+ 1 for null termination + } + //third call to retrieve data of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen2, mode); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for method parameter of findContours + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + piLen3 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen3, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + find_method= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + find_method[i] = (char*)malloc(sizeof(char) * (piLen3[i] + 1));//+ 1 for null termination + } + //third call to retrieve data of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen3, find_method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //toget the x coordinate of point parameter of findcontours + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&x); + if(intErr) + return intErr; + + //toget the y coordinate of point parameter of findcontours + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&y); + if(intErr) + return intErr; + + //to set hte mode for findcontour + if (strcmp(mode[0],"CV_RETR_EXTERNAL")==0) + findmode = CV_RETR_EXTERNAL; + else if (strcmp(mode[0],"CV_RETR_LIST")==0) + findmode = CV_RETR_LIST; + else if (strcmp(mode[0],"CV_RETR_CCOMP")==0) + findmode = CV_RETR_CCOMP; + else if (strcmp(mode[0],"CV_RETR_TREE")==0) + findmode = CV_RETR_TREE; + else + { + findmode = CV_RETR_TREE; + sciprint("wrong mode given , using CV_RETR_TREE instead"); + } + // to set the method for findContours + if (strcmp(find_method[0],"CV_CHAIN_APPROX_NONE")==0) + findmethod = CV_CHAIN_APPROX_NONE; + else if (strcmp( find_method[0],"CV_CHAIN_APPROX_SIMPLE")==0) + findmethod = CV_CHAIN_APPROX_SIMPLE; + else if (strcmp(find_method[0],"CV_CHAIN_APPROX_TC89_L1")==0) + findmethod = CV_CHAIN_APPROX_TC89_L1; + else if (strcmp(find_method[0],"CV_CHAIN_APPROX_TC89_KCOS")==0) + findmethod = CV_CHAIN_APPROX_TC89_KCOS; + else + { + findmethod = CV_CHAIN_APPROX_SIMPLE; + sciprint("wrong method given , using CV_CHAIN_APPROX_SIMPLE instead"); + } + Point pt(x,y); + /// Find contours + vector > contours; + vector hierarchy; + findContours( src, contours, hierarchy, findmode, findmethod, pt); + + //Assigning the list as the Output Variable + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, contours.size(), &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int fitellipse(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double* pstData = NULL; + int* pstData2 = NULL; + double *rrows; + double *rcols; + double sigmaSpace; + int error; + ////checking input argument + CheckInputArgument(pvApiCtx,1,1); + //checking output argument + CheckOutputArgument(pvApiCtx,5,5); + //for first argument + // get Address of first input + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + //check for any error + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrieve input array + //SciErr getMatrixOfDouble(void* _pvCtx, int* _piAddress, int* _piRows, int* _piCols, double** _pdblReal) + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols,&pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int k=0; + vector contours; + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; + +void rotate180(Mat &m) { + double temp; + for (int i=0; i<(m.rows+1)/2; i++) { + int k = m.cols; + if ((i+1)>=((m.rows+1)/2) && m.rows%2!=0) { + k = (m.cols+1)/2; + } + for (int j=0; j(i, j); + m.at(i, j) = m.at(m.rows-i-1, m.cols-j-1); + m.at(m.rows-i-1, m.cols-j-1) = temp; + } + } + +} + +Mat fftshift(Mat m) { + int a = m.rows/2; + int b = m.cols/2; + Mat r = Mat::zeros(m.size(), m.type()); + for (int i=0; i((i+a)%m.rows, (j+b)%m.cols) = m.at(i, j); + } + } + return r; +} + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + + int opencv_fsamp2(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int cRows=0,cCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Mat image, imgcpy; + // retrieveImage(image, 1); + // string tempstring = type2str(image.type()); + // char *imtype; + // imtype = (char *)malloc(tempstring.size() + 1); + // memcpy(imtype, tempstring.c_str(), tempstring.size() + 1); + // bool integer = true; + // int scale = 1; + // double error = 0; + + if (nbInputArgument(pvApiCtx) == 1) { + Mat hd, hdcpy; + retrieveImage(hdcpy, 1); + hdcpy.convertTo(hd, CV_64F); + rotate180(hd); + + fftshift(hd).copyTo(hd); + + rotate180(hd); + + + Mat padded; //expand input image to optimal size + int m = getOptimalDFTSize( hd.rows ); + int n = getOptimalDFTSize( hd.cols ); // on the border add zero values + copyMakeBorder(hd, padded, 0, m - hd.rows, 0, n - hd.cols, BORDER_CONSTANT, Scalar::all(0)); + + Mat planes[] = {Mat_(padded), Mat::zeros(padded.size(), CV_64F)}; + Mat complexI; + merge(planes, 2, complexI); // Add to the expanded another plane with zeros + + dft(complexI, complexI, DFT_INVERSE | DFT_SCALE); // this way the result may fit in the source matrix + + split(complexI, planes); + + fftshift(planes[0]).copyTo(planes[0]); + fftshift(planes[1]).copyTo(planes[1]); + rotate180(planes[0]); + rotate180(planes[1]); + + double *re = (double *)malloc(planes[0].rows * planes[0].cols * sizeof(double)); + double *im = (double *)malloc(planes[0].rows * planes[0].cols * sizeof(double)); + + for(int i=0;i(i, j); + //cout<(i, j)<<" "; + im[i+planes[0].rows*j]=planes[1].at(i, j); + } + //cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + +void rotate180(Mat &m) { + double temp; + for (int i=0; i<(m.rows+1)/2; i++) { + int k = m.cols; + if ((i+1)>=((m.rows+1)/2) && m.rows%2!=0) { + k = (m.cols+1)/2; + } + for (int j=0; j(i, j); + m.at(i, j) = m.at(m.rows-i-1, m.cols-j-1); + m.at(m.rows-i-1, m.cols-j-1) = temp; + } + } + +} + +Mat fftshift(Mat m) { + int a = m.rows/2; + int b = m.cols/2; + Mat r = Mat::zeros(m.size(), m.type()); + for (int i=0; i((i+a)%m.rows, (j+b)%m.cols) = m.at(i, j); + } + } + return r; +} + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_ftrans2(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int cRows=0,cCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat b, bcpy, t; + Mat P0, P1, P2, h, hh; + + retrieveImage(bcpy, 1); + bcpy.convertTo(b, CV_64F); + + if (b.cols == 1) { + transpose(b, b); + } + else if (b.rows == 1) { + + } + else { + sciprint("b at argument 1 expected to be vector"); + return 0; + } + + if (b.cols%2 == 0) { + sciprint("b at argument 1 must be of odd length"); + return 0; + } + + Mat c = b.clone(); + rotate180(c); + + int zeroCount = 0; + double eps = 1.5e-5; + + for (int i=0; i(0, i) - c.at(0, i)) > eps) { + zeroCount = b.cols; + break; + } + if (b.at(0, i) == 0) { + zeroCount++; + } + } + if (zeroCount == b.cols) { + sciprint("b at argument 1 must be nonzero and symmetric"); + return 0; + } + + + if (nbInputArgument(pvApiCtx) == 2) { + Mat tcpy; + retrieveImage(tcpy, 2); + tcpy.convertTo(t, CV_64F); + + } + else { + Mat tcpy; + double data[3][3] = {{1.0/8, 2.0/8, 1.0/8},{2.0/8, -4.0/8, 2.0/8},{1.0/8, 2.0/8, 1.0/8}}; + tcpy = Mat(3, 3, CV_64F, &data); + t = tcpy.clone(); + } + + rotate180(b); + + fftshift(b).copyTo(b); + + rotate180(b); + + int inset1 = (t.rows-1)/2; + int inset2 = (t.cols-1)/2; + int n = (b.cols-1)/2; + + Mat a = Mat::zeros(1, n+1, CV_64F); + a.at(0, 0) = b.at(0,0); + for (int i=1; i<=n; i++) { + a.at(0, i) = 2*b.at(0, i); + } + + P0 = Mat::ones(1, 1, CV_64F); + t.copyTo(P1); + h = (P1*a.at(0, 1)); + + h.at(inset1,inset2) = h.at(inset1,inset2) + a.at(0, 0); + + for (int i=2; i<=n; i++) { + Mat src = t.clone(); + + int additionalRows = P1.rows-1, additionalCols = P1.cols-1; + + copyMakeBorder(src, src, (additionalRows+1)/2, additionalRows/2, (additionalCols+1)/2, additionalCols/2, BORDER_CONSTANT, Scalar(0)); + + Point anchor(P1.cols - P1.cols/2 - 1, P1.rows - P1.rows/2 - 1); + int borderMode = BORDER_CONSTANT; + Mat P11; + flip(P1, P11,-1); + filter2D(src, P2, src.depth(), P11, anchor, 0, borderMode); + + P2 = P2 * 2; + + for (int x=0; x(x + 2*inset1, y+ 2*inset2) = P2.at(x + 2*inset1, y+ 2*inset2) - P0.at(x, y); + } + } + h.copyTo(hh); + h = (P2*a.at(0, i)); + for (int x=0; x(x + inset1, y + inset2) = h.at(x + inset1, y + inset2) + hh.at(x, y); + } + } + + P1.copyTo(P0); + P2.copyTo(P1); + + } + + rotate180(h); + + Mat hcopy; + h.copyTo(hcopy); + + string tempstring = type2str(hcopy.type()); + char *imtype; + imtype = (char *)malloc(tempstring.size() + 1); + memcpy(imtype, tempstring.c_str(), tempstring.size() + 1); + returnImage(imtype,hcopy,1); + free(imtype); + + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_gabor.cpp b/sci_gateway/cpp/opencv_gabor.cpp new file mode 100644 index 0000000..7e218df --- /dev/null +++ b/sci_gateway/cpp/opencv_gabor.cpp @@ -0,0 +1,129 @@ +/* + * gabor + * + * gabor in scilab + * + */ + +// Created by Samiran Roy, mail: samiranroy@cse.iitb.ac.in +// An implementation of gabor method of matlab +// Usage: +// gabor(wavelength,orientation) - Generates a gabor kernel with the given wavelength and orientation +// wavelength: pixels/cycle of the sinusoidal carrier, must be >=2 +// orientation: orientation of the filter in degrees, must be between [0,360] + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" { +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" + +int opencv_gabor(char *fname, unsigned long fname_len) { + SciErr sciErr; + int intErr = 0; + int iRows = 0, iCols = 0; + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + + int error; + + double wavelength; + double orientation; + + // Get the number of input arguments + int inputarg = *getNbInputArgument(pvApiCtx); + + // String holding the second argument + int iRet = 0; + char *pstData = NULL; + + // Checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Geting the wavelength + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr1, &wavelength); + if (sciErr.iErr) { + printError(&sciErr, 0); + return intErr; + } + + if (wavelength < 2) { + sciprint("Wavelength must be >=2"); + return 0; + } + // Getting the orientation + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr2, &orientation); + if (sciErr.iErr) { + printError(&sciErr, 0); + return intErr; + } + + if ((orientation < 0) || (orientation > 360)) { + sciprint("Orientation must be in the range [0,360]"); + return 0; + } + + double sigma = (1 / CV_PI) * sqrt(log(2) / 2) * 3 * + wavelength; // calculating sigma following matlab convention + + orientation = (orientation / 360) * 2 * CV_PI; // Converting degree to radian + int K_size = + 33 + + 16 * (wavelength - 2); // size of kernel following matlab convention + + Mat new_image = getGaborKernel(cv::Size(K_size, K_size), sigma, orientation, + wavelength, 0.5, 0); + // sciprint("\n"); + + // for (int i = 0; i < new_image.rows; i++) { + // for (int j = 0; j < new_image.cols; j++) { + // sciprint("%f ", new_image.at(i,j)); + + // } + + // sciprint("\n"); + // } + + // new_image is sent to scilab as output + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_gaussianblur.cpp b/sci_gateway/cpp/opencv_gaussianblur.cpp new file mode 100644 index 0000000..f81885e --- /dev/null +++ b/sci_gateway/cpp/opencv_gaussianblur.cpp @@ -0,0 +1,132 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = gaussianblur(input_image,ksize_height,ksize_width,sigmaX,sigmaY); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_gaussianblur(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double ksize_width,ksize_height,sigmaX,sigmaY; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of ksize_height + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &ksize_height); + if(intErr) + return intErr; + + //for value of ksize_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &ksize_width); + if(intErr) + return intErr; + + //for value of sigmaX + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &sigmaX); + if(intErr) + return intErr; + + //for value of sigmaY + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &sigmaY); + if(intErr) + return intErr; + + //applying checks to input parameters, and assigning default values in case of error + Mat new_image(image.rows,image.cols,CV_8UC3); + if(ksize_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead"); + ksize_height=1; + } + if(ksize_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead"); + ksize_width=1; + } + if((int)ksize_height%2==0) + { + ksize_height+=1; + sciprint("Odd Value Required for Height. %f value was used instead",&ksize_height); + } + if((int)ksize_width%2==0) + { + ksize_width+=1; + sciprint("Odd Value Required for Width. %f value was used instead",&ksize_width); + } + + //temporary size variable, to use in function + Size sz(ksize_height,ksize_width); + + //void GaussianBlur(InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT ) + GaussianBlur(image,new_image,sz,sigmaX,sigmaY); + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_getStructuringElement.cpp b/sci_gateway/cpp/opencv_getStructuringElement.cpp new file mode 100644 index 0000000..8226671 --- /dev/null +++ b/sci_gateway/cpp/opencv_getStructuringElement.cpp @@ -0,0 +1,147 @@ +/************************************************* +Author: Sukul Bagai +**************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getStructuringElement(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr1 = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k,type; + char **gettype = NULL; + double cols,rows,anchorX,anchorY; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, NULL, NULL); + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + gettype= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + gettype[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, gettype); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp("MORPH_RECT",gettype[0])==0) + type=MORPH_RECT; + else if(strcmp("MORPH_ELLIPSE",gettype[0])==0) + type=MORPH_ELLIPSE; + else if(strcmp("MORPH_CROSS",gettype[0])==0) + type=MORPH_CROSS; + + //for cols + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&cols); + if(intErr) + return intErr; + + //for rows + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &rows); + if(intErr) + return intErr; + + //for anchorX + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4 ,&anchorX); + if(intErr) + return intErr; + + //for anchorY + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&anchorY); + if(intErr) + return intErr; + + Mat temp = getStructuringElement(type,Size(rows,cols),Point(anchorX,anchorY)); + + double *m = (double *)malloc(temp.rows*temp.cols*sizeof(double)); + for(i=0;i(i, j); + *(m + i*temp.cols + j) = intensity; + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, temp.rows, temp.cols, m); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} diff --git a/sci_gateway/cpp/opencv_getTextSize.cpp b/sci_gateway/cpp/opencv_getTextSize.cpp new file mode 100644 index 0000000..150b8ec --- /dev/null +++ b/sci_gateway/cpp/opencv_getTextSize.cpp @@ -0,0 +1,188 @@ +/*************************************************** +Author : Sukul Bagai , Abhilasha Sancheti +**************************************************** +Usage : [size,baseline] = getTextSize("","",fontscale,thickness); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getTextSize(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + int *piLen2 = NULL; + char **pstData = NULL; + char **pstData2 = NULL; + double font_scale = 0,thickness = 0; + int i; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 2, 2) ; + + //for string + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + pstData[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for fontface + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen2 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen2, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData2 = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + pstData2[i] = (char*)malloc(sizeof(char) * (piLen2[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen2, pstData2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of font scale + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &font_scale); + if(intErr) + return intErr; + + //for value of thickness + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &thickness); + if(intErr) + return intErr; + + int fontFace; + + if(strcmp(pstData2[0],"FONT_HERSHEY_SIMPLEX")==0) + fontFace = FONT_HERSHEY_SIMPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_PLAIN")==0) + fontFace= FONT_HERSHEY_PLAIN; + else if (strcmp(pstData2[0],"FONT_HERSHEY_DUPLEX")==0) + fontFace= FONT_HERSHEY_DUPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_COMPLEX")==0) + fontFace= FONT_HERSHEY_COMPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_TRIPLEX")==0) + fontFace = FONT_HERSHEY_TRIPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_COMPLEX_SMALL")==0) + fontFace = FONT_HERSHEY_COMPLEX_SMALL; + else if (strcmp(pstData2[0],"FONT_HERSHEY_SCRIPT_SIMPLEX")==0) + fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_SCRIPT_COMPLEX")==0) + fontFace = FONT_HERSHEY_SCRIPT_COMPLEX; + + int baseline; + Size sz = getTextSize(pstData[0],fontFace,font_scale,thickness,&baseline); + double tempsize[2]; + tempsize[0]=sz.width; + tempsize[1]=sz.height; + double returnbaseline = (double)baseline; + + sciErr = createMatrixOfDouble(pvApiCtx,nbInputArgument(pvApiCtx)+1,1,2,tempsize); + if(sciErr.iErr) + { + printError(&sciErr,0); + return 0; + } + sciErr = createMatrixOfDouble(pvApiCtx,nbInputArgument(pvApiCtx)+2,1,1,&returnbaseline); + if(sciErr.iErr) + { + printError(&sciErr,0); + return 0; + } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_getgaussiankernel.cpp b/sci_gateway/cpp/opencv_getgaussiankernel.cpp new file mode 100644 index 0000000..99c6d7d --- /dev/null +++ b/sci_gateway/cpp/opencv_getgaussiankernel.cpp @@ -0,0 +1,142 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* +Mat getGaussianKernel(int ksize, double sigma, int ktype=CV_64F ) +********************************************************/ + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getgaussiankernel(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + double ksize, sigma; + char **kType = NULL; + int i, j, k; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + + //for value of ksize + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &ksize); + if(intErr || ksize < 0 || (int) ksize % 2 == 0) + { + return intErr; + } + + + //for first value of size + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &sigma); + if(intErr) + { + return intErr; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + kType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + kType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, kType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + Mat temp; + + if(strcmp(kType[0], "CV_32F") == 0) + temp = getGaussianKernel(ksize, sigma, CV_32F + ); + else if(strcmp(kType[0], "CV_64F") == 0) + temp = getGaussianKernel(ksize, sigma, CV_64F); + + double *m = (double *)malloc(temp.rows*temp.cols*sizeof(double)); + for(i=0;i(i, j); + *(m + i*temp.cols + j) = intensity; + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, temp.rows, temp.cols, m); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + + + + + diff --git a/sci_gateway/cpp/opencv_getkerneltype.cpp b/sci_gateway/cpp/opencv_getkerneltype.cpp new file mode 100644 index 0000000..68e72d9 --- /dev/null +++ b/sci_gateway/cpp/opencv_getkerneltype.cpp @@ -0,0 +1,118 @@ +//Syntax int getKernelType(InputArray, Px,py) +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : returnkernel_type= getkerneltype(InputArray,point px,point py) */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int getkerneltype(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr1 = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double* pstData = NULL; + double* pstData2; + double *rrows; + double *rcols; + int error; + double pi1,pi2; + // checking input argument + CheckInputArgument(pvApiCtx,3,3); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + // get Address of first input + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + //check for any error + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &piRows, &piCols,&pstData2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + Mat kernel=Mat(piRows,piCols,CV_64F); + int k=0,m=0; + for(int j=0;j(i,j)=pstData2[k++]; + + } + } + cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getrectsubpix(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int i,j,k; + double centerX, centerY, alpha, beta, patchType; + + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for first value of size + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &alpha); + if(intErr) + { + return intErr; + } + + + //for second value of size + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &beta); + if(intErr) + { + return intErr; + } + + //for x coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, ¢erX); + if(intErr) + { + return intErr; + } + + //for y coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, ¢erY); + if(intErr) + { + return intErr; + } + + + //for patchType + sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &patchType); + if(intErr) + { + return intErr; + } + + + Size patchSize = Size(alpha, beta); + + Point2f center = Point2f(centerX, centerY); + + Mat patch; + + getRectSubPix(image, patchSize, center, patch, patchType); + + + String tempstring = type2str(patch.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, patch, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_getrotationmatrix2D.cpp b/sci_gateway/cpp/opencv_getrotationmatrix2D.cpp new file mode 100644 index 0000000..5a54ed2 --- /dev/null +++ b/sci_gateway/cpp/opencv_getrotationmatrix2D.cpp @@ -0,0 +1,121 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* +Mat getRotationMatrix2D(Point2f center, double angle, double scale) +********************************************************/ + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getrotationmatrix2D(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + double centerX, centerY, angle, scale; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1); + + + //for x coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, ¢erX); + if(intErr) + { + return intErr; + } + + //for y coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, ¢erY); + if(intErr) + { + return intErr; + } + + + //for value of angle + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &angle); + if(intErr) + { + return intErr; + } + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &scale); + if(intErr) + { + return intErr; + } + + Point2f center = Point2f(centerX, centerY); + + Mat temp = getRotationMatrix2D(center, angle, scale); + + double *m = (double *)malloc(temp.rows*temp.cols*sizeof(double)); + for(i=0;i(i, j); + *(m + i*temp.cols + j) = intensity; + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, temp.rows, temp.cols, m); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_goodfeaturestotrack.cpp b/sci_gateway/cpp/opencv_goodfeaturestotrack.cpp new file mode 100644 index 0000000..79e614f --- /dev/null +++ b/sci_gateway/cpp/opencv_goodfeaturestotrack.cpp @@ -0,0 +1,168 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + corner_points = goodFeaturesToTrack( input_image, maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector((1 for true)/(0 for false)),k ); +********************************************************/ + + +// Abhilasha Sancheti +// sample input : +//output: (image , corners) +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_goodfeaturestotrack(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + + int i,j, detector; + double maxCorners ,qualityLevel,minDistance,blocksize=3 ,k=0.04, usedetector; + bool useHarrisDetector = false; + + //checking input argument + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat image; + retrieveImage(image,1); + + //for maximum corners + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&maxCorners); + if(intErr) + return intErr; + + + //for qualityLevel + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&qualityLevel); + if(intErr) + return intErr; + + //for minDistance + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4 ,&minDistance); + if(intErr) + return intErr; + + + //for blocksize + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&blocksize); + if(intErr) + return intErr; + + //for Harrisusedetector taking integer 1 or 0 + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6,&usedetector); + if(intErr) + return intErr; + detector=(int)usedetector; + + //for k value + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7 ,&k); + if(intErr) + return intErr; + + + //checking the input parameter usedetector + if (detector == 1) + useHarrisDetector = true; + else if(detector == 0) + useHarrisDetector = false; + else + { + sciprint("Either 1 or 0 , 0 value was used instead"); + useHarrisDetector = false; + } + + /// processing of the input image and other parameters + if( maxCorners < 1 ) { maxCorners = 1; sciprint("maxcorners cannot be less than 1 , using 1 instead");} + + /// Parameters for Shi-Tomasi algorithm + + vector corners; + /// Copy the source image + Mat src_gray; + cvtColor( image, src_gray, CV_BGR2GRAY ); + /// Apply corner detection + goodFeaturesToTrack( src_gray,corners,maxCorners,qualityLevel,minDistance,Mat(),blocksize,useHarrisDetector,k ); + + int row = corners.size(); + double *cor = (double *)malloc(2*row*sizeof(double *)); + for (int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_grayconnected(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double row, column, tolerance=32; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of row + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &row); + if(intErr) + return intErr; + + //for value of column + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &column); + if(intErr) + return intErr; + + if(nbInputArgument(pvApiCtx)==4) + { + //for value of tolerance + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &tolerance); + if(intErr) + return intErr; + } + + + if(row<0) + { + sciprint("Positive Value Required for Row."); + return 0; + } + if(column<0) + { + sciprint("Positive Value Required for Column."); + return 0; + } + if(tolerance<0) + { + sciprint("Positive Value Required for tolerance."); + return 0; + } + + + Mat gray_image; + cvtColor( image, gray_image, CV_BGR2GRAY ); + Mat temp_image = Mat::zeros( image.size(), image.type() ); + Mat image_to_display = Mat::zeros( image.size(), image.type() ); + + int value = gray_image.at(row,column); + + for(int i=0;i(i,j); + if(value1>=value-tolerance && value1<=tolerance+value) + temp_image.at(i,j)=255; + else + temp_image.at(i,j)=0; + } + } + + Mat labelled_image(image.size(), image.type() ); + int nlabels = connectedComponents(temp_image , labelled_image ); + int label = labelled_image.at(row,column); + + for(int i=0;i(i,j)==label) + image_to_display.at(i,j)=255; + } + } + + //returning image + string tempstring = type2str(image_to_display.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,image_to_display,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_graycoprops.cpp b/sci_gateway/cpp/opencv_graycoprops.cpp new file mode 100644 index 0000000..130b261 --- /dev/null +++ b/sci_gateway/cpp/opencv_graycoprops.cpp @@ -0,0 +1,408 @@ +/* + * graycoprops + * + * graycoprops in scilab + * Please refer to : + * http://www.cse.unsw.edu.au/~icml2002/workshops/MLCV02/MLCV02-Bevk.pdf, p.3. + */ + +// Created by Samiran Roy, mail: samiranroy@cse.iitb.ac.in +// An implementation of graycoprops method of matlab +// Usage: +// graycoprops(GLCM,property) : Calculates the Property of the input Gray level co-occurence matrix + +// Known Changes from Matlab: +/* + * 1) None, as of now - Matching exactly - but does not use structures - for properties + */ + +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" { +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" + +double return_contrast(Mat image) + +{ + double contrast = 0; + + for (int i = 0; i < image.rows; i++) { + for (int j = 0; j < image.cols; j++) { + contrast = contrast + (pow((i - j), 2) * image.at(i, j)); + } + } + return contrast; +} + +double return_correlation(Mat image) + +{ + double correlation = 0; + + std::vector mg_rows; + std::vector mg_cols; + + // Meshgrid of row and column indices + for (int i = 0; i < image.rows; i++) { + for (int j = 0; j < image.cols; j++) { + mg_rows.push_back(j); + mg_cols.push_back(i); + } + } + + // for (int i=0; i(i); + + for (int i = 0; i < mg_cols.size(); i++) + mean_col = mean_col + mg_cols[i] * flat_image.at(i); + + for (int i = 0; i < mg_rows.size(); i++) + std_row = + std_row + (pow(mg_rows[i] - mean_row, 2) * flat_image.at(i)); + + std_row = sqrt(std_row); + + for (int i = 0; i < mg_cols.size(); i++) + std_col = + std_col + (pow(mg_cols[i] - mean_col, 2) * flat_image.at(i)); + + std_col = sqrt(std_col); + + for (int i = 0; i < mg_rows.size(); i++) + correlation = + correlation + ((mg_rows[i] - mean_row) * (mg_cols[i] - mean_col) * + flat_image.at(i)); + + double denom = std_col * std_row; + if (denom != 0) { + correlation = correlation / denom; + } + + // sciprint("\n"); + + // sciprint("MR: %f",mean_row); + // sciprint("MC: %f",mean_col); + + // sciprint("SR: %f",std_row); + // sciprint("SC: %f",std_col); + return correlation; +} + +double return_energy(Mat image) + +{ + double energy = 0; + + for (int i = 0; i < image.rows; i++) { + for (int j = 0; j < image.cols; j++) { + energy = energy + pow(image.at(i, j), 2); + } + } + return energy; +} + +double return_homogeneity(Mat image) + +{ + double homogeneity = 0; + + for (int i = 0; i < image.rows; i++) { + for (int j = 0; j < image.cols; j++) { + homogeneity = homogeneity + (image.at(i, j) / (1 + abs(i - j))); + } + } + return homogeneity; +} + +int opencv_graycoprops(char *fname, unsigned long fname_len) { + SciErr sciErr; + int intErr = 0; + + int *piAddr1 = NULL; + + int error; + + // String holding the second argument + int iRet = 0; + char *pstData = NULL; + + // Checking input argument + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get input image + + Mat image; + retrieveImage(image, 1); + + // converting image to float + + image.convertTo(image, CV_64FC1, 1, 0); + + // for (int i = 0; i < image.rows; i++) { + // for (int j = 0; j < image.cols; j++) { + // sciprint("%f ", image.at(i, j)); + // } + + // sciprint("\n"); + // } + + // Error Checks + + if (image.channels() == 1) { + // Normalizing image + + double s = cv::sum(image)[0]; + image = image / s; + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr1); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + if (isStringType(pvApiCtx, piAddr1)) { + if (isScalar(pvApiCtx, piAddr1)) { + iRet = getAllocatedSingleString(pvApiCtx, piAddr1, &pstData); + } + } + if (strcmp(pstData, "contrast") == 0) { + double contrast = return_contrast(image); + intErr = + createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, contrast); + if (intErr) return intErr; + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + + else if (strcmp(pstData, "correlation") == 0) { + double correlation = return_correlation(image); + + intErr = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, + correlation); + if (intErr) return intErr; + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } else if (strcmp(pstData, "energy") == 0) { + double energy = return_energy(image); + + intErr = + createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, energy); + if (intErr) return intErr; + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + + else if (strcmp(pstData, "homogeneity") == 0) { + double homogeneity = return_homogeneity(image); + + intErr = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, + homogeneity); + if (intErr) return intErr; + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + + else { + sciprint("\nUnknown Parameter Name: %s\n", pstData); + return 0; + } + } + + else if (image.channels() == 3) { + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr1); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + if (isStringType(pvApiCtx, piAddr1)) { + if (isScalar(pvApiCtx, piAddr1)) { + iRet = getAllocatedSingleString(pvApiCtx, piAddr1, &pstData); + } + } + if (strcmp(pstData, "contrast") == 0) { + vector rgb; + split(image, rgb); + + double *contrast = (double *)malloc(sizeof(double) * 3); + double s; + + for (int i = 0; i < 3; i++) { + s = cv::sum(rgb[i])[0]; + rgb[i] = rgb[i] / s; + contrast[2-i] = return_contrast(rgb[i]); + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx), 1, 3, + contrast); + + free(contrast); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx); + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + + else if (strcmp(pstData, "correlation") == 0) { + vector rgb; + split(image, rgb); + + double *correlation = (double *)malloc(sizeof(double) * 3); + double s; + + for (int i = 0; i < 3; i++) { + s = cv::sum(rgb[i])[0]; + rgb[i] = rgb[i] / s; + correlation[2-i] = return_correlation(rgb[i]); + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx), 1, 3, + correlation); + + free(correlation); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx); + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + + else if (strcmp(pstData, "energy") == 0) { + vector rgb; + split(image, rgb); + + double *energy = (double *)malloc(sizeof(double) * 3); + double s; + + for (int i = 0; i < 3; i++) { + s = cv::sum(rgb[i])[0]; + rgb[i] = rgb[i] / s; + energy[2-i] = return_energy(rgb[i]); + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx), 1, 3, + energy); + + free(energy); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx); + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + + else if (strcmp(pstData, "homogeneity") == 0) { + vector rgb; + split(image, rgb); + + double *homogeneity = (double *)malloc(sizeof(double) * 3); + double s; + + for (int i = 0; i < 3; i++) { + s = cv::sum(rgb[i])[0]; + rgb[i] = rgb[i] / s; + homogeneity[2-i] = return_homogeneity(rgb[i]); + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx), 1, 3, + homogeneity); + + free(homogeneity); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx); + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + else { + sciprint("\nUnknown Parameter Name: %s\n", pstData); + return 0; + } + + } else { + sciprint("Invalid number of channels in the image(must be 1 or 3)\n"); + return 0; + } + + // sciprint("\n"); + + // for (int i = 0; i < new_image.rows; i++) { + // for (int j = 0; j < new_image.cols; j++) { + // sciprint("%f ", new_image.at(i,j)); + + // } + + // sciprint("\n"); + // } + + // new_image is sent to scilab as output +} +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_graydiffweight.cpp b/sci_gateway/cpp/opencv_graydiffweight.cpp new file mode 100644 index 0000000..a0ea5e8 --- /dev/null +++ b/sci_gateway/cpp/opencv_graydiffweight.cpp @@ -0,0 +1,85 @@ +/******************************************************** +Authors: Dhruti Shah +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int opencv_graydiffweight(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double refGrayVal; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + cvtColor( image, image, CV_BGR2GRAY ); + + //for value of refGrayVal + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &refGrayVal); + if(intErr) + { + return intErr; + } + + double value1, value2; + Mat new_image = Mat::zeros(image.size(),image.type()); + for(int i=0; i(i,j); + value2 = abs(value1-refGrayVal); + new_image.at(i,j)=value2; + } + } + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_graydist.cpp b/sci_gateway/cpp/opencv_graydist.cpp new file mode 100644 index 0000000..af09afd --- /dev/null +++ b/sci_gateway/cpp/opencv_graydist.cpp @@ -0,0 +1,81 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = graydist(input_image, mask) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_graydist(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get the input image from the Scilab environment + Mat marker, mask; + retrieveImage(marker, 1); + retrieveImage(mask, 2); + + // do conversion of marker to gray + // do conversion of mask to gray + + vector sources; + Mat distances(mask.size(), CV_32F); + Mat sptSet = Mat::zeros(mask.size(), CV_8UC1); + for (i = 0; i < mask.cols; i++) + { + for (j = 0; j < mask.rows; j++) + { + if (mask.at(i,j)) { + sources.push_back(Point(i,j)); + distances.at(i,j) = 0.0; + } + else + distances.at(i,j) = FLT_MAX; + } + } + + for (c = 0; c < mask.cols * mask.rows; c++) + { + float l = minDistance(distances, sptSet); + + } + + + string tempstring = type2str(fin_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, fin_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_histeq.cpp b/sci_gateway/cpp/opencv_histeq.cpp new file mode 100644 index 0000000..2770074 --- /dev/null +++ b/sci_gateway/cpp/opencv_histeq.cpp @@ -0,0 +1,63 @@ +//histeq function is for grayscale image +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : return_image=histeq(rgbimage); +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include +#include "../common.h" +int histeq(char *fname, unsigned long fname_len) +{ + + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + char** pstData = NULL; + int * pstData2 = NULL; + int* piAddr3 =NULL; + int* piAddr4 =NULL; + double *rrows; + double *rcols; + int t; + //checking input argument + CheckInputArgument(pvApiCtx,1,1); + //checking output argument + CheckOutputArgument(pvApiCtx,1,1) ; + //for first argument + Mat img; + retrieveImage(img,1); + Mat dst; + ////open cv function to equalize the histogram + equalizeHist(img,dst); + string tempstring = type2str(dst.type()); + char* checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} +/* ==================================================================== */ + diff --git a/sci_gateway/cpp/opencv_houghcircles.cpp b/sci_gateway/cpp/opencv_houghcircles.cpp new file mode 100644 index 0000000..4b36aa6 --- /dev/null +++ b/sci_gateway/cpp/opencv_houghcircles.cpp @@ -0,0 +1,164 @@ +/******************************************************** +Authors: Sukul Bagai, Abhilasha Sancheti +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_houghcircles(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int i,j,k; + double *dp =NULL , *mindist = NULL ,*param1= NULL ,*param2=NULL; + double *minradius =NULL, *maxradius = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + // for dp parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols ,&dp); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for minDist parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&mindist); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for param1 parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols ,¶m1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for param2 parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr5, &iRows, &iCols ,¶m2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + // for minradius parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr6, &iRows, &iCols ,&minradius); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + // for maxradius parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr7, &iRows, &iCols ,&maxradius); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Mat src_gray; + cvtColor( src, src_gray, CV_BGR2GRAY ); + + /// Reduce the noise so we avoid false circle detection + + + vector circles; + + /// Apply the Hough Transform to find the circles + HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, dp[0], mindist[0], param1[0], param2[0], minradius[0], maxradius[0] ); + int rows = circles.size(); + cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int opencv_houghlines(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int i,j,k; + double rho, theta, srn, stn, threshold; + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + cvtColor(image, image, CV_BGR2GRAY); + //for value of rho + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &rho); + if(intErr) + { + return intErr; + } + + //for value of theta + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &theta); + if(intErr) + { + return intErr; + } + + //for value of threshold + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &threshold); + if(intErr) + { + return intErr; + } + //for value of srn + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &srn); + if(intErr) + { + return intErr; + } + //for value of stn + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &stn); + if(intErr) + { + return intErr; + } + vector < vector > output; + cout<<"here5"; + HoughLines(image, output, rho, theta, threshold, srn, stn); + cout<<"here5"; +//create matrix from 2-D vector + int row = output.size(); + cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int opencv_houghlinesp(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int i,j,k; + double rho, theta, minLineLength = 0, maxLineGap = 0, threshold; + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + cvtColor(image, image, CV_BGR2GRAY); + //for value of rho + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &rho); + if(intErr) + { + return intErr; + } + + //for value of theta + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &theta); + if(intErr) + { + return intErr; + } + + //for value of threshold + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &threshold); + if(intErr) + { + return intErr; + } + //for value of minLineLength + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &minLineLength); + if(intErr) + { + return intErr; + } + + //for value of maxLineGap + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &maxLineGap); + if(intErr) + { + return intErr; + } + + vector < vector > output; + + HoughLinesP(image, output, rho, theta, threshold, minLineLength, maxLineGap); + +//create matrix from 2-D vector + int row = output.size(); + double *cor = (double *)malloc(row*4*sizeof(double)); + + + for (int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_ifftshift(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + Mat image; + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + retrieveImage(image,1); + int cx = floor(image.cols/2); + int cy = floor(image.rows/2); + Mat q0(image, Rect(0, 0, cx, cy)); // Top-Left - Create a ROI per quadrant + Mat q1(image, Rect(cx, 0, cx, cy)); // Top-Right + Mat q2(image, Rect(0, cy, cx, cy)); // Bottom-Left + Mat q3(image, Rect(cx, cy, cx, cy)); // Bottom-Right + Mat tmp; // swap quadrants (Top-Left with Bottom-Right) + q0.copyTo(tmp); + q3.copyTo(q0); + tmp.copyTo(q3); + q1.copyTo(tmp); // swap quadrant (Top-Right with Bottom-Left) + q2.copyTo(q1); + tmp.copyTo(q2); + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,image,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_im2double.cpp b/sci_gateway/cpp/opencv_im2double.cpp new file mode 100644 index 0000000..45ee009 --- /dev/null +++ b/sci_gateway/cpp/opencv_im2double.cpp @@ -0,0 +1,56 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_im2double(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int i,j,k; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + Mat image; + retrieveImage(image, 1); + + Mat img_dbprec; + image.convertTo(img_dbprec, CV_64FC3,1.0/255.0); /// Create Image with Double Precision + + string tempstring = type2str(img_dbprec.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,img_dbprec,1); + free(checker); + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imabsdiff.cpp b/sci_gateway/cpp/opencv_imabsdiff.cpp new file mode 100644 index 0000000..becb773 --- /dev/null +++ b/sci_gateway/cpp/opencv_imabsdiff.cpp @@ -0,0 +1,51 @@ +/******************************************************** +Function :imabsdiff +Syntax :C=imabsdiff(A,B) +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_imabsdiff(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + Mat A,B,C; + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + retrieveImage(A,1); + retrieveImage(B,2); + Size s1=A.size(); + Size s2=B.size(); + if(s1!=s2) + { + sciprint("\nBoth input should be same size"); + return 0; + } + absdiff(A,B,C); + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(C.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,C,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_imadd.cpp b/sci_gateway/cpp/opencv_imadd.cpp new file mode 100644 index 0000000..b67335d --- /dev/null +++ b/sci_gateway/cpp/opencv_imadd.cpp @@ -0,0 +1,71 @@ +/******************************************************** +Function :imadd +Syntax :C=imadd(A,B) +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_imadd(char *fname, unsigned long fname_len) + { + SciErr sciErr; + Mat A,B,C; + int* piAddr2=NULL; + int iType = 0; + int iRet = 0; + double value= 0; + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + retrieveImage(A,1); + sciErr =getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + sciErr = getVarType(pvApiCtx, piAddr2, &iType); + if(iType==sci_list) + { + retrieveImage(B,2); + Size s1=A.size(); + Size s2=B.size(); + if(s1!=s2) + { + sciprint("\nBoth input should be same size"); + return 0; + } + add(A,B,C); + } + else + { + if(isDoubleType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getScalarDouble(pvApiCtx, piAddr2, &value); + } + } + add(A,value,C); + } + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(C.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,C,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_imattributes.cpp b/sci_gateway/cpp/opencv_imattributes.cpp new file mode 100644 index 0000000..51b544e --- /dev/null +++ b/sci_gateway/cpp/opencv_imattributes.cpp @@ -0,0 +1,114 @@ +/******************************************************** + Author: Dhruti Shah +********************************************************* + void imattributes(image) +********************************************************/ + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "sciprint.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_imattributes(char *fname, unsigned long fname_len) + { + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1,1) ; + + + Mat image; + retrieveImage(image,1); + + int row = image.rows; + int column = image.cols; + int type = image.type(); + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + uchar chans = 1 + (type >> CV_CN_SHIFT); + + switch ( depth ) { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + + r += "C"; + r += (chans+'0'); + + + int flag_binary=1; + + for(int i =0; i(i,j)!=0 || image.at(i,j)!=255) + { + flag_binary=0; + break; + } + } + if (flag_binary=0) + break; + } + + string image_type; + //uchar chans = 1 + (type >> CV_CN_SHIFT); + if (flag_binary==1) + image_type = "Binary"; + else if (chans==1) + image_type= "Intensity"; + else if (chans==3) + image_type= "Truecolor"; + + char image_type1[100]; + strcpy(image_type1, image_type.c_str()); + + char image_class[100]; + strcpy(image_class, r.c_str()); + + sciprint("Width (columns) \t%d\n",column); + sciprint("Height (rows) \t%d\n",row); + sciprint("Class \t%s\n",image_class); + sciprint("Image Type \t%s\n",image_type1); + + if(flag_binary!=1 && chans==1) + { + int min_intensity=255,max_intensity=0; + for(int i=0; i(i,j)(i,j); + if (image.at(i,j)>max_intensity) + max_intensity=image.at(i,j); + } + } + + sciprint("Min intensity \t%d\n",min_intensity); + sciprint("Max intensity \t%d\n",max_intensity); + } + + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imboxfilt3.cpp b/sci_gateway/cpp/opencv_imboxfilt3.cpp new file mode 100644 index 0000000..1af549f --- /dev/null +++ b/sci_gateway/cpp/opencv_imboxfilt3.cpp @@ -0,0 +1,130 @@ +/*************************************************** +Author : Yash S. Bhalgat +**************************************************** +Usage : + 1) box_filtered_image = imboxfilt3(input_img); + In this usage, the default filter size of 3x3 is used. + + 2) box_filtered_image = imboxfilt3(input_img, filter_height, filter_width); + +Example : + img = imread("lena.jpg"); + imshow(img); + box_filtered_img = imboxfilt3(img, 9, 9); + imshow(box_filtered_img); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_imboxfilt3(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + + //Default filter size + double filter_height = 3; + double filter_width = 3; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + int inputarg = *getNbInputArgument(pvApiCtx); + + + Mat image; + retrieveImage(image,1); + + + if(inputarg >= 2){ + //for value of filter_height + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &filter_height); + if(intErr) + return intErr; + + //for value of filter_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &filter_width); + if(intErr) + return intErr; + } + + + //taking the cases which can lead to an error + Mat filtered_image(image.rows,image.cols,image.type()); + + if(filter_height<0) + { + sciprint("Positive Value Required for Height. 3 value was used instead"); + filter_height=3; + } + if(filter_width<0) + { + sciprint("Positive Value Required for Width. 3 value was used instead"); + filter_width=3; + } + if((int)filter_height%2==0) + { + filter_height+=1; + sciprint("Odd Value Required for Height. %f value was used instead",&filter_height); + } + if((int)filter_width%2==0) + { + filter_width+=1; + sciprint("Odd Value Required for Width. %f value was used instead",&filter_width); + } + + //temporary size variable, to use in function + Size sz(filter_height,filter_width); + + //opencv function called + //if both filter_size is not given, default value of 3x3 is used + boxFilter(image,filtered_image, -1, sz); + + //returning image + string tempstring = type2str(filtered_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,filtered_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imcomplement.cpp b/sci_gateway/cpp/opencv_imcomplement.cpp new file mode 100644 index 0000000..efcaa9a --- /dev/null +++ b/sci_gateway/cpp/opencv_imcomplement.cpp @@ -0,0 +1,67 @@ +/******************************************************** +Function :imcomplement +Syntax :B=imcomplement(A) +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_imcomplement(char *fname, unsigned long fname_len) + { + SciErr sciErr; + Mat A,B,C; + int* piAddr2=NULL; + int iType = 0; + int iRet = 0; + double value= 0; + CheckInputArgument(pvApiCtx, 1,1); + CheckOutputArgument(pvApiCtx, 1, 1); + retrieveImage(A,1); + if(A.channels()==3) + { + for(int x=0;x< A.rows; x++) + { + for ( int y=0;y(x,y)[i]=255-A.at(x,y)[i]; + } + } + } + else + { + for(int x=0;x(x,y)=255-A.at(x,y); + } + } + } + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(A.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,A,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_imcontour.cpp b/sci_gateway/cpp/opencv_imcontour.cpp new file mode 100644 index 0000000..7d6a83b --- /dev/null +++ b/sci_gateway/cpp/opencv_imcontour.cpp @@ -0,0 +1,62 @@ +/******************************************************** +Function :imcontour +Syntax :B=imcontour(A) +Author: Tess Zacharias + +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imcontour(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + Mat gray,image; + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + retrieveImage(image, 1); + cvtColor(image, gray, CV_BGR2GRAY); + Canny(gray, gray, 100, 200, 3); + /// Find contours + vector > contours; + vector hierarchy; + RNG rng(12345); + findContours( gray, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) ); + /// Draw contours + Mat drawing = Mat::zeros( gray.size(), CV_8UC3 ); + for( int i = 0; i< contours.size(); i++ ) + { + Scalar color = Scalar(rng.uniform(0,255),rng.uniform(0,255),rng.uniform(0,255)); + drawContours( drawing, contours, i, color, 1, 8, hierarchy, 0, Point()); + } + bitwise_not(drawing,drawing); + string tempstring = type2str(drawing.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,drawing,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +} diff --git a/sci_gateway/cpp/opencv_imcontrast.cpp b/sci_gateway/cpp/opencv_imcontrast.cpp new file mode 100644 index 0000000..216228f --- /dev/null +++ b/sci_gateway/cpp/opencv_imcontrast.cpp @@ -0,0 +1,100 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_imcontrast(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + double alpha, beta; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + + //for value of alpha + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &alpha); + if(intErr) + { + return intErr; + } + + //for value of beta + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &beta); + if(intErr) + { + return intErr; + } + + + //we copy the original image into the new_image, and change values of each pixel using value of alpha and beta given by the user + Mat new_image = Mat::zeros( image.size(), image.type() ); + + //changes the attributes of each individual pixel + for( int y = 0; y < image.rows; y++ ) + { + for( int x = 0; x < image.cols; x++ ) + { + for( int c = 0; c < 3; c++ ) + { + new_image.at(y,x)[c] = + saturate_cast( alpha*( image.at(y,x)[c] ) + beta ); + } + } + } + + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imcrop.cpp b/sci_gateway/cpp/opencv_imcrop.cpp new file mode 100644 index 0000000..d180c0a --- /dev/null +++ b/sci_gateway/cpp/opencv_imcrop.cpp @@ -0,0 +1,130 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imcrop(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double x, y, width, height; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + + //for value of top-left x-coordinate + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &x); + if(intErr) + { + return intErr; + } + + //for value top-left y-coordinate + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &y); + if(intErr) + { + return intErr; + } + + //for value of width + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &width); + if(intErr) + { + return intErr; + } + + //for value of height + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &height); + if(intErr) + { + return intErr; + } + + if(x>=image.cols || y>=image.rows || x<0 || y<0) + { + sciprint("Invalid x or y value\n"); + return 0; + } + if(width<=0 || height<=0 || x+width > image.cols || y+height > image.rows) + { + sciprint("Invalid width or height value\n"); + return 0; + } + //defining a temporary rectangle, that denotes the area that has to be cropped into the new image + Rect myROI(x, y, width, height); + + // Crop the full image to that image contained by the rectangle myROI + // Note that this doesn't copy the data + Mat croppedRef(image, myROI); + + Mat cropped; + // Copy the data into new matrix + croppedRef.copyTo(cropped); + + string tempstring = type2str(cropped.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,cropped,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imdivide.cpp b/sci_gateway/cpp/opencv_imdivide.cpp new file mode 100644 index 0000000..2ede4d2 --- /dev/null +++ b/sci_gateway/cpp/opencv_imdivide.cpp @@ -0,0 +1,71 @@ +/******************************************************** +Function :imdivide +Syntax :C=imdivide(A,B) +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_imdivide(char *fname, unsigned long fname_len) + { + SciErr sciErr; + Mat A,B,C; + int* piAddr2=NULL; + int iType = 0; + int iRet = 0; + double value= 0; + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + retrieveImage(A,1); + sciErr =getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + sciErr = getVarType(pvApiCtx, piAddr2, &iType); + if(iType==sci_list) + { + retrieveImage(B,2); + Size s1=A.size(); + Size s2=B.size(); + if(s1!=s2) + { + sciprint("\nBoth input should be same size"); + return 0; + } + divide(A,B,C); + } + else + { + if(isDoubleType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getScalarDouble(pvApiCtx, piAddr2, &value); + } + } + C=A/value; + } + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(C.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,C,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_imextendedmax.cpp b/sci_gateway/cpp/opencv_imextendedmax.cpp new file mode 100644 index 0000000..ba457d5 --- /dev/null +++ b/sci_gateway/cpp/opencv_imextendedmax.cpp @@ -0,0 +1,99 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = imextendedmax(input_image, h) +Example: + im = imextendedmax(image, 80) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + void imextendedmax_imreconstruct(Mat, Mat, Mat&); + + int opencv_imextendedmax(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + double h; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Get the input image from the Scilab environment + Mat image; + retrieveImage(image, 1); + + // Get the address of 2nd argument, the H-Maxima transform scalar + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the H-Maxima transform scalar + intErr = getScalarDouble(pvApiCtx, piAddr, &h); + if(intErr) + { + return intErr; + } + + if (h < 0) + { + sciprint("Please enter a nonnegative scalar for H-maxima transform.\n"); + return 0; + } + + Mat gray_image, dst, fin_image, m, m2; + cvtColor(image, gray_image, CV_BGR2GRAY); + max((gray_image-h), 0, m); + imextendedmax_imreconstruct(gray_image, m, dst); + subtract(dst, 1, m2); + imextendedmax_imreconstruct(dst, m2, m); + subtract(dst, m, m2); + fin_image = m2 * 255; + + string tempstring = type2str(fin_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, fin_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } + void imextendedmax_imreconstruct(Mat g, Mat f, Mat& dest) + { + Mat m0, m1, m; + m1 = f; + do { + m0 = m1.clone(); + dilate(m0, m, Mat()); + min(g, m, m1); + } while(countNonZero(m1 != m0) != 0); + dest = m1.clone(); + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imextendedmin.cpp b/sci_gateway/cpp/opencv_imextendedmin.cpp new file mode 100644 index 0000000..b5a8fa3 --- /dev/null +++ b/sci_gateway/cpp/opencv_imextendedmin.cpp @@ -0,0 +1,100 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = imextendedmin(input_image, h) +Example: + im = imextendedmin(image, 80) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + void imextendedmin_imreconstruct(Mat, Mat, Mat&); + + int opencv_imextendedmin(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + double h; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Get the input image from the Scilab environment + Mat image; + retrieveImage(image, 1); + + // Get the address of 2nd argument, the H-Minima transform scalar + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the H-Minima transform scalar + intErr = getScalarDouble(pvApiCtx, piAddr, &h); + if(intErr) + { + return intErr; + } + + if (h < 0) + { + sciprint("Please enter a nonnegative scalar for H-minima transform.\n"); + return 0; + } + + Mat gray_image, dst, fin_image, m, m2; + cvtColor(image, gray_image, CV_BGR2GRAY); + min((gray_image + h), 255, m); + imextendedmin_imreconstruct(gray_image, m, dst); + add(dst, 1, m2); + imextendedmin_imreconstruct(dst, m2, m); + subtract(m, dst, m2); + fin_image = m2 * 255; + + + string tempstring = type2str(fin_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, fin_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } + void imextendedmin_imreconstruct(Mat g, Mat f, Mat& dest) + { + Mat m0, m1, m; + m1 = f; + do { + m0 = m1.clone(); + erode(m0, m, Mat()); + max(g, m, m1); + } while(countNonZero(m1 != m0) != 0); + dest = m1.clone(); + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imfill.cpp b/sci_gateway/cpp/opencv_imfill.cpp new file mode 100644 index 0000000..7e0c79d --- /dev/null +++ b/sci_gateway/cpp/opencv_imfill.cpp @@ -0,0 +1,192 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = imfill(image) + return_image = imfill(image, 'holes') + return_image = imfill(image, points) +Example: + im = imfill(image, [1 1; 10 10; 11 12]) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + void imfill_imreconstruct(Mat, Mat, Mat&); + + int opencv_imfill(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + int i, j; + double maxVal; + int iRet; + int iRows, iCols; + double *pstData = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get the input image from the Scilab environment + Mat image, dst; + retrieveImage(image, 1); + + if (nbInputArgument(pvApiCtx) == 2) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (image.type() != CV_8UC1) + { + Mat temp; + temp = image.clone(); + cvtColor(temp, image, CV_BGR2GRAY); + } + + if(isStringType(pvApiCtx, piAddr)) + { + char* pstData = NULL; + + iRet = getAllocatedSingleString(pvApiCtx, piAddr, &pstData); + + if(iRet) + { + freeAllocatedSingleString(pstData); + return iRet; + } + + if (strcmp(pstData, "holes") == 0) + { + freeAllocatedSingleString(pstData); + // Floodfill from point (0, 0) + Mat im_floodfill = image.clone(); + floodFill(im_floodfill, Point(0,0), Scalar(255)); + + // Invert floodfilled image + Mat im_floodfill_inv; + bitwise_not(im_floodfill, im_floodfill_inv); + + // Combine the two images to get the foreground. + Mat dst2 = (image | im_floodfill_inv); + erode(dst2, dst, Mat()); + + } + else + { + sciprint("Option not identified. Maybe you meant 'holes'."); + return 0; + } + } + else + { + + // Get the points list in the form of a matrix + // No. of columns = 2 + // No. of rows = No. of points + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iCols != 2) + { + sciprint("Please enter a valid column and row index."); + return 0; + } + + for (i = 0; i < iRows*iCols; i += iCols) + { + if (pstData[i] < 0 || pstData[i+1] < 0) + { + sciprint("Coordinates cannot be negative.\n"); + return 0; + } + else + floodFill(image, Point(pstData[i], pstData[i+1]), Scalar(255)); + } + + dst = image.clone(); + } + + } + else + { + Mat gray_image; + cvtColor(image, gray_image, CV_BGR2GRAY); + + minMaxLoc(gray_image, NULL, &maxVal, NULL, NULL); + + Mat marker = Mat(gray_image.size(), gray_image.type()); + + marker = Scalar::all(maxVal); + + for (i = 0; (i == 0 || i == marker.cols-1); i += (marker.cols - 1)) + { + for (j = 0; j < marker.rows; j++) + { + marker.at(i,j) = gray_image.at(i,j); + } + } + + for (i = 1; i < marker.cols-1; i++) + { + for (j = 0; (j == 0 || j == marker.rows-1); j += (marker.rows - 1)) + { + marker.at(i,j) = gray_image.at(i,j); + } + } + + imfill_imreconstruct(gray_image, marker, dst); + } + + + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, dst, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } + void imfill_imreconstruct(Mat g, Mat f, Mat& dest) + { + Mat m0, m1, m; + m1 = f; + do { + m0 = m1.clone(); + erode(m0, m, Mat()); + max(g, m, m1); + } while(countNonZero(m1 != m0) != 0); + dest = m1.clone(); + } + +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_imfindcircles.cpp b/sci_gateway/cpp/opencv_imfindcircles.cpp new file mode 100644 index 0000000..2569343 --- /dev/null +++ b/sci_gateway/cpp/opencv_imfindcircles.cpp @@ -0,0 +1,128 @@ +/******************************************************** +Function :imfindcircles +Syntax :B=imfindcircles(A) +Author: Tess Zacharias + +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imfindcircles(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + Mat src_gray,image; + double Rmin,Rmax; + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 2) ; + retrieveImage(image, 1); + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &Rmin); + if(intErr) + { + return intErr; + } + if(Rmin!= round(Rmin) || Rmin<=0) + { + sciprint("The value of minium Radius must be an integer\n"); + return 0; + } + if(Rmin<5) + { + sciprint("The value of minium Radius too small\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &Rmax); + if(intErr) + { + return intErr; + } + if(Rmax!= round(Rmax) || Rmax<=0) + { + sciprint("The value of maximum Radius must be an integer\n"); + return 0; + } + cvtColor(image,src_gray, CV_BGR2GRAY); + GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 ); + vector circles; + HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 2, 10, 200, 100,Rmin,Rmax); + int k=circles.size(); + double *radius=NULL; + radius=( double *)malloc(sizeof(double)*k); + double *c1=NULL; + c1=( double *)malloc(sizeof(double)*k); + double *c2=NULL; + c2=( double *)malloc(sizeof(double)*k); + for( size_t i= 0; i < circles.size(); i++ ) + { + c1[i]=cvRound(circles[i][0]); + c2[i]=cvRound(circles[i][1]); + radius[i]=cvRound(circles[i][2]); + } + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 2, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddrNew, 1, 1, k, c1); + free(c1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddrNew, 2, 1, k, c2); + free(c2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + if(Lhs==2) + { + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) +2,1,k,radius); + free(radius); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + } + ReturnArguments(pvApiCtx); + return 0; + + } +} diff --git a/sci_gateway/cpp/opencv_imfindcircles.cpp~ b/sci_gateway/cpp/opencv_imfindcircles.cpp~ new file mode 100644 index 0000000..7201e7d --- /dev/null +++ b/sci_gateway/cpp/opencv_imfindcircles.cpp~ @@ -0,0 +1,148 @@ +/******************************************************** +Function :imfindcircles +Syntax :B=imfindcircles(A) +Author: Tess Zacharias + +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imfindcircles(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + Mat src_gray,image; + double Rmin,Rmax; + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 2) ; + sciprint("here1"); + retrieveImage(image, 1); +// sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); +// if (sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// intErr = getScalarDouble(pvApiCtx, piAddr2, &Rmin); +// if(intErr) +// { +// return intErr; +// } +// if(Rmin!= round(Rmin) || Rmin<=0) +// { +// sciprint("The value of minium Radius must be an integer\n"); +// return 0; +// } +// if(Rmin<5) +// { +// sciprint("The value of minium Radius too small\n"); +// return 0; +// } +// sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); +// if (sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// intErr = getScalarDouble(pvApiCtx, piAddr3, &Rmax); +// if(intErr) +// { +// return intErr; +// } +// if(Rmax!= round(Rmax) || Rmax<=0) +// { +// sciprint("The value of maximum Radius must be an integer\n"); +// return 0; +// } + sciprint("here2"); + cvtColor(image,src_gray,CV_BGR2GRAY); + GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 ); + vector circles; + HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100,0,0); + sciprint("here3"); + int k=circles.size(); + if (k==0) + {sciprint("zero");} + else if (k==1) + {sciprint("one");} + else if (k==2) + {sciprint("two");} + else if (k==3) + {sciprint("three");} + else + {sciprint(">3");} + double *radius=NULL; + radius=( double *)malloc(sizeof(double)*k); + double *c1=NULL; + c1=( double *)malloc(sizeof(double)*k); + double *c2=NULL; + c2=( double *)malloc(sizeof(double)*k); + sciprint("here4"); + for( size_t i= 0; i < circles.size(); i++ ) + { + c1[i]=cvRound(circles[i][0]); + cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imfuse(char *fname, unsigned long fname_len){ + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piLen1 = NULL; + int *piLen2 = NULL; + char **method = NULL; + char **scaling = NULL; + + Mat image1,image2; + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + retrieveImage(image1, 1); + retrieveImage(image2, 2); + + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen1 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen1, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + method = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i = 0 ; i < iRows * iCols ; i++) + method[i] = (char*)malloc(sizeof(char) * (piLen1[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen1, method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // now the fourth argument which is aging a string + + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen2 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen2, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + scaling = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i = 0 ; i < iRows * iCols ; i++) + scaling[i] = (char*)malloc(sizeof(char) * (piLen2[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen2, scaling); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + char x1[15] = "montage"; + char x2[15] = "diff"; + char x3[15] = "blend"; + char x4[15] = "falsecolor"; + char y1[15] = "joint"; + char y2[15] = "independent"; + char y3[15] = "none"; + // Error check on method + if(!(strcmp(method[0],x1)==0 || strcmp(method[0],x2)==0 || strcmp(method[0],x3)==0 || strcmp(method[0],x4)==0 )){ + sciprint("Method input not correct, choose from 'blend','diff','montage' or 'falsecolor' \n"); + return 0; + } + // Error check on scaling + + if(!(strcmp(scaling[0],y1)==0 || strcmp(scaling[0],y2)==0 || strcmp(scaling[0],y3)==0 )){ + sciprint("Method input not correct, choose from 'none','diff','montage' or 'falsecolor' \n"); + return 0; + } + + int max_rows,max_cols; + Mat out; + max_rows = max(image1.rows,image2.rows); + max_cols = max(image1.cols,image2.cols); + if(image1.channels() == 1){ cvtColor(image1,image1,CV_BGR2GRAY);} + if(image2.channels() == 1){ cvtColor(image2,image2,CV_BGR2GRAY);} + + vector rgbChannels(3); + // for montage and no scaling plus joint scaling + + if(strcmp(method[0],x1)==0 && ((strcmp(scaling[0],y1)==0)||(strcmp(scaling[0],y3)==0))) { + out = Mat::zeros(max_rows,2*max_cols,image1.type()); + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j + max_cols)[0] = image2.at(i,j)[0]; + out.at(i,j + max_cols)[1] = image2.at(i,j)[1]; + out.at(i,j + max_cols)[2] = image2.at(i,j)[2]; + } + } + } + // for montage and scaling independent + if(strcmp(method[0],x1)==0 && strcmp(scaling[0],y2)==0) { + + + split(image1, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image1); + split(image2, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image2); + + out = Mat::zeros(max_rows,2*max_cols,image1.type()); + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j + max_cols)[0] = image2.at(i,j)[0]; + out.at(i,j + max_cols)[1] = image2.at(i,j)[1]; + out.at(i,j + max_cols)[2] = image2.at(i,j)[2]; + } + } + } + + // for diff and no scaling plus joint scaling + if(strcmp(method[0],x2)==0 && ((strcmp(scaling[0],y1)==0)||(strcmp(scaling[0],y3)==0))) { + + // cvtColor(image1,image1,CV_BGR2GRAY); + // cvtColor(image2,image2,CV_BGR2GRAY); + out = Mat::zeros(max_rows,max_cols,image1.type()); + + for(int i=0;i(i,j)[0] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + } + } + for(int i=0;i(i,j)[0] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[1] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + } + for(int i=0;i(i,j)[0] = abs((image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2]) - (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2]))/3; + out.at(i,j)[1] = abs((image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2]) - (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2]))/3; + out.at(i,j)[2] = abs((image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2]) - (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2]))/3; + } + } + } + // for diff and independent scaling + if(strcmp(method[0],x2)==0 && strcmp(scaling[0],y2)==0) { + + + split(image1, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image1); + split(image2, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image2); + + // cvtColor(image1,image1,CV_BGR2GRAY); + // cvtColor(image2,image2,CV_BGR2GRAY); + out = Mat::zeros(max_rows,max_cols,image1.type()); + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j)[0] = image2.at(i,j)[0]; + out.at(i,j)[1] = image2.at(i,j)[1]; + out.at(i,j)[2] = image2.at(i,j)[2]; + } + } + for(int i=0;i(i,j)[0] = abs(image1.at(i,j)[0] - image2.at(i,j)[0]); + out.at(i,j)[1] = abs(image1.at(i,j)[1] - image2.at(i,j)[1]); + out.at(i,j)[2] = abs(image1.at(i,j)[2] - image2.at(i,j)[2]); + } + } + } + + // for blend and no scaling[0] plus joint scaling[0] + if(strcmp(method[0],x3)==0 && ((strcmp(scaling[0],y1)==0)||(strcmp(scaling[0],y3)==0))) { + out = Mat::zeros(max_rows,max_cols,image1.type()); + + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j)[0] = image2.at(i,j)[0]; + out.at(i,j)[1] = image2.at(i,j)[1]; + out.at(i,j)[2] = image2.at(i,j)[2]; + } + } + + for(int i=0;i(i,j)[0] = 0.5*(image1.at(i,j)[0] + image2.at(i,j)[0]); + out.at(i,j)[1] = 0.5*(image1.at(i,j)[1] + image2.at(i,j)[1]); + out.at(i,j)[2] = 0.5*(image1.at(i,j)[2] + image2.at(i,j)[2]); + } + } + + } + // for blend and independent scaling[0] + if(strcmp(method[0],x3)==0 && strcmp(scaling[0],y2)==0) { + out = Mat::zeros(max_rows,max_cols,image1.type()); + + split(image1, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image1); + split(image2, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image2); + + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j)[0] = image2.at(i,j)[0]; + out.at(i,j)[1] = image2.at(i,j)[1]; + out.at(i,j)[2] = image2.at(i,j)[2]; + } + } + + for(int i=0;i(i,j)[0] = 0.5*(image1.at(i,j)[0] + image2.at(i,j)[0]); + out.at(i,j)[1] = 0.5*(image1.at(i,j)[1] + image2.at(i,j)[1]); + out.at(i,j)[2] = 0.5*(image1.at(i,j)[2] + image2.at(i,j)[2]); + } + } + + } + + // for falsecolor and no scaling[0] plus joint scaling[0] + if(strcmp(method[0],x4)==0 && ((strcmp(scaling[0],y1)==0)||(strcmp(scaling[0],y3)==0))) { + + out = Mat::zeros(max_rows,max_cols,image1.type()); + + // image 1 green image2 red + for(int i=0;i(i,j)[0] = 0; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = 0; + } + } + for(int i=0;i(i,j)[0] = 0; + out.at(i,j)[1] = 0; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + } + for(int i=0;i(i,j) == image2.at(i,j)){ + out.at(i,j)[0] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[1] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + else{ + out.at(i,j)[0] = 0; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + + } + } + + } + // for falsecolor and independent scaling[0] + if(strcmp(method[0],x4)==0 && strcmp(scaling[0],y2)==0) { + out = Mat::zeros(max_rows,max_cols,image1.type()); + //scale image 1 and image 2 + + split(image1, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image1); + split(image2, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image2); + // image 1 green image2 red + for(int i=0;i(i,j)[0] = 0; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = 0; + } + } + for(int i=0;i(i,j)[0] = 0; + out.at(i,j)[1] = 0; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + } + for(int i=0;i(i,j) == image2.at(i,j)){ + out.at(i,j)[0] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[1] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + else{ + out.at(i,j)[0] = 0; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + + } + } + + } + + // for joint scaling[0] + if(strcmp(scaling[0],y3)==0){ + split(out, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,out); + } + + if(image1.channels()==1 && image2.channels()==1){ + cvtColor(out,out,CV_BGR2GRAY); + } + + // out is the return image + string tempstring = type2str(out.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,out,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_imgaborfilt.cpp b/sci_gateway/cpp/opencv_imgaborfilt.cpp new file mode 100644 index 0000000..457a4be --- /dev/null +++ b/sci_gateway/cpp/opencv_imgaborfilt.cpp @@ -0,0 +1,159 @@ +/* + * imgaborfilt + * + * imgaborfilt in scilab + * + */ + +// Created by Samiran Roy, mail: samiranroy@cse.iitb.ac.in +// An implementation of imgaborfilt in scilab +// Usage: +// imgaborfilt(I,wavelength,orientation) : Perform gabor filtering on a grayscale image with given wavelength and orientation +// imboxfilt(I,method) +// method : 'upright' (default) +// method : 'rotated' The area sums are calulated over a rectangle, which is +// rotated 45 degrees +// Known Changes from Matlab: +/* + * 1) None, as of now + */ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" { +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" + +int opencv_imgaborfilt(char *fname, unsigned long fname_len) { + SciErr sciErr; + int intErr = 0; + int iRows = 0, iCols = 0; + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + + int error; + + double wavelength; + double orientation; + + int borderType = BORDER_REPLICATE; + + // Parameters for convolution - Leave these alone + + /* ********************************************************************************** + */ + + Point anchor; + double delta; + int ddepth; + + anchor = Point(-1, -1); // The center is unchanged + delta = 0; // No value is added to output + ddepth = -1; // The dimentions of input and output images are the same + + /* ********************************************************************************** + */ + + // Get the number of input arguments + int inputarg = *getNbInputArgument(pvApiCtx); + + // String holding the second argument + int iRet = 0; + char *pstData = NULL; + + // Checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get input image + + Mat image; + retrieveImage(image, 1); + + if (image.channels() > 1) { + sciprint("The image must be grayscale."); + return 0; + } + + // Getting the wavelength + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr2, &wavelength); + if (sciErr.iErr) { + printError(&sciErr, 0); + return intErr; + } + + if (wavelength < 2) { + sciprint("Wavelength must be >=2"); + return 0; + } + // Getting the orientation + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr3, &orientation); + if (sciErr.iErr) { + printError(&sciErr, 0); + return intErr; + } + + if ((orientation < 0) || (orientation > 360)) { + sciprint("Orientation must be in the range [0,360]"); + return 0; + } + + double sigma = (1 / CV_PI) * sqrt(log(2) / 2) * 3 * + wavelength; // calculating sigma following matlab convention + + orientation = (orientation / 360) * 2 * CV_PI; // Converting degree to radian + int K_size = + 33 + + 16 * (wavelength - 2); // size of kernel following matlab convention + + Mat kernel = getGaborKernel(cv::Size(K_size, K_size), sigma, orientation, + wavelength, 0.5, 0); + + Mat floatimage; + image.convertTo(floatimage, CV_32F); // Converting image to float type + + Mat dst; + Mat new_image; + filter2D(floatimage, new_image, CV_32F, kernel); // Performing convolution + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imgaussfilt3.cpp b/sci_gateway/cpp/opencv_imgaussfilt3.cpp new file mode 100644 index 0000000..f07076f --- /dev/null +++ b/sci_gateway/cpp/opencv_imgaussfilt3.cpp @@ -0,0 +1,138 @@ +/*************************************************** +Author : Yash S. Bhalgat +**************************************************** +Usage : filtered_image = imgaussfilt3(input_img, filter_height, filter_width, sigmaX, sigmaY); +Example : + img = imread("lena.jpg"); + imshow(img); + filtered_img = imgaussfilt3(img, 9, 9, 3, 3); + imshow(filtered_img); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_imgaussfilt3(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double filter_width,filter_height,sigmaX,sigmaY; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of filter_height + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &filter_height); + if(intErr) + return intErr; + + //for value of filter_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &filter_width); + if(intErr) + return intErr; + + //for value of sigmaX + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &sigmaX); + if(intErr) + return intErr; + + //for value of sigmaY + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &sigmaY); + if(intErr) + return intErr; + + //taking the cases which can lead to an error + Mat filtered_image(image.rows,image.cols,CV_8UC3); + if(filter_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead"); + filter_height=1; + } + if(filter_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead"); + filter_width=1; + } + if((int)filter_height%2==0) + { + filter_height+=1; + sciprint("Odd Value Required for Height. %f value was used instead",&filter_height); + } + if((int)filter_width%2==0) + { + filter_width+=1; + sciprint("Odd Value Required for Width. %f value was used instead",&filter_width); + } + + //temporary size variable, to use in function + Size sz(filter_height,filter_width); + + //opencv function called + //if both sigmaX, sigmaY are set to zero, they are calaulated from the filter_size + GaussianBlur(image,filtered_image,sz,sigmaX,sigmaY); + + //returning image + string tempstring = type2str(filtered_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,filtered_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imguidedfilter.cpp b/sci_gateway/cpp/opencv_imguidedfilter.cpp new file mode 100644 index 0000000..6d4df43 --- /dev/null +++ b/sci_gateway/cpp/opencv_imguidedfilter.cpp @@ -0,0 +1,227 @@ +/************************************************************************************* +Author : Yash S. Bhalgat + +References : Kaiming He, Jian Sun, Xiaoou Tang, "Guided Image Filtering", IEEE Transactions + on Pattern Analysis & Machine Intelligence, vol.35, no. 6, + pp. 1397-1409, June 2013, doi:10.1109/TPAMI.2012.213 +*************************************************************************************** +Usage : + Covers all the cases of I and p being grayscale and RGB + + 1) guidedFiltered_image = imguidedfilter(input_img); + In this usage, the image itself is used as the guidance image. + + 2) guidedFiltered_image = imguidedfilter(input_img, guidance_img); + + The following two functionalities are not provided in Matlab in this way: + 3) guidedFiltered_image = imguidedfilter(input_img, guidance_img, r); + 4) guidedFiltered_image = imguidedfilter(input_img, guidance_img, r, eps); + +Example : + img = imread("lena.jpg"); + imshow(img); + guidedFiltered_image = imguidedfilter(img, img, 9); + imshow(guidedFiltered_image); +***********************************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_imguidedfilter(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + + //Default filter size + double r= 4; + double eps= 0.2 * 0.2 * 255 * 255; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + int inputarg = *getNbInputArgument(pvApiCtx); + + + Mat I; + retrieveImage(I,1); + + Mat p=I; + + if(inputarg > 1){ + //for value of filter_height + //sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + //if (sciErr.iErr) + //{ + // printError(&sciErr, 0); + // return 0; + //} + //intErr = getScalarDouble(pvApiCtx, piAddr2, &filter_height); + //if(intErr) + // return intErr; + Mat G; + retrieveImage(G,1); + p=G; + + if(inputarg > 2){ + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &r); + if(intErr) + return intErr; + } + if(inputarg > 3){ + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &eps); + if(intErr) + return intErr; + } + } + + + Mat guidedFiltered_image; + + + //Main function + //if both filter_size is not given, default value of 3x3 is used + if(I.channels() == 1){ + Mat mean_p; + blur(p, mean_p, Size(r, r)); + + Mat mean_Ip; + blur(I.mul(p), mean_Ip, Size(r, r)); + + Mat mean_I; + blur(I, mean_I, Size(r, r)); + + Mat mean_II; + blur(I.mul(I), mean_II, Size(r,r)); + + Mat var_I = mean_II - mean_I.mul(mean_I); + + Mat cov_Ip = mean_Ip - mean_I.mul(mean_p); //covariance of (I, p) in each local patch. + + Mat a = cov_Ip / (var_I + eps); + Mat b = mean_p - a.mul(mean_I); + + Mat mean_a; + blur(a, mean_a, Size(r, r)); + Mat mean_b; + blur(b, mean_b, Size(r, r)); + + guidedFiltered_image = mean_a.mul(I) + mean_b; + } + else if(I.channels()==3 && p.channels()==1){ + vector Ichannels, channels; + split(I, Ichannels); + guidedFiltered_image = I; + //split(guidedFiltered_image, channels); + + Mat mean_p, mean_I, mean_Ip; + Mat mean_II, var_I, cov_Ip, a, b; + Mat mean_a, mean_b; + + for(i=0; i<3; i++){ + blur(p, mean_p, Size(r, r)); + + blur(Ichannels[i].mul(p), mean_Ip, Size(r, r)); + + blur(Ichannels[i], mean_I, Size(r, r)); + + blur(Ichannels[i].mul(Ichannels[i]), mean_II, Size(r,r)); + + var_I = mean_II - mean_I.mul(mean_I); + + cov_Ip = mean_Ip - mean_I.mul(mean_p); //covariance of (Ichannels[i], p) in each local patch. + + a = cov_Ip / (var_I + eps); + b = mean_p - a.mul(mean_I); + + blur(a, mean_a, Size(r, r)); + blur(b, mean_b, Size(r, r)); + + channels.push_back(mean_a.mul(Ichannels[i]) + mean_b); + } + merge(channels,guidedFiltered_image); + } + else if(I.channels()==3 && p.channels()==3){ + vector Ichannels, channels, pchannels; + + split(I, Ichannels); + guidedFiltered_image = I; + //split(guidedFiltered_image, channels); + split(p, pchannels); + + Mat mean_p, mean_I, mean_Ip; + Mat mean_II, var_I, cov_Ip, a, b; + Mat mean_a, mean_b; + + for(i=0; i<3; i++){ + blur(pchannels[i], mean_p, Size(r, r)); + + blur(Ichannels[i].mul(pchannels[i]), mean_Ip, Size(r, r)); + + blur(Ichannels[i], mean_I, Size(r, r)); + + blur(Ichannels[i].mul(Ichannels[i]), mean_II, Size(r,r)); + + var_I = mean_II - mean_I.mul(mean_I); + + cov_Ip = mean_Ip - mean_I.mul(mean_p); //covariance of (Ichannels[i], pchannels[i]) in each local patch. + + a = cov_Ip / (var_I + eps); + b = mean_p - a.mul(mean_I); + + blur(a, mean_a, Size(r, r)); + blur(b, mean_b, Size(r, r)); + + channels.push_back(mean_a.mul(Ichannels[i]) + mean_b); + } + merge(channels,guidedFiltered_image); + } + + //returning image + string tempstring = type2str(guidedFiltered_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,guidedFiltered_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imhistmatch.cpp b/sci_gateway/cpp/opencv_imhistmatch.cpp new file mode 100644 index 0000000..146b0b3 --- /dev/null +++ b/sci_gateway/cpp/opencv_imhistmatch.cpp @@ -0,0 +1,296 @@ +/* + * imhistmatch + * + * histogram matching in scilab + * + */ + +// Created by Samiran Roy, mail: samiranroy@cse.iitb.ac.in +// An implementation of imhistmatch method +// Usage: +// 1) imhistmatch(image,referenceimage) +// 2) imhistmatch(image,referenceimage,outputbins) + +// Known Changes from Matlab: +/* + * Default number of bins is 256 - for both input and output + */ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" { +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" + +// Matches the input image histogram with the reference image histogram, +// returning the output in n bins + +Mat match(Mat image, Mat ref, int bins) { + Mat dst, hist; + Mat input_image = Mat::zeros(image.size(), CV_8U); + Mat refhist; + + int inputbins = 256; + + Mat cdf = Mat(inputbins, 1, CV_8U, cvScalar(0)); + + Mat refcdf = Mat(bins, 1, CV_8U, cvScalar(0)); + + // Calculating histogram of image + image.convertTo(dst, CV_8U, 1, 0); + + calcHist(&dst, 1, 0, Mat(), hist, 1, &inputbins, 0); + + // Calculating histogram of reference image + + ref.convertTo(dst, CV_8U, 1, 0); + calcHist(&dst, 1, 0, Mat(), refhist, 1, &bins, 0); + + hist.copyTo(cdf); + refhist.copyTo(refcdf); + + // calculate cdf + for (int h = 1; h < inputbins; h++) { + float binVal = hist.at(h, 0); + + cdf.at(h, 0) = cdf.at(h, 0) + cdf.at(h - 1, 0); + } + + // normalize histogram + for (int h = 0; h < inputbins; h++) { + cdf.at(h, 0) = cdf.at(h, 0) / cdf.at(inputbins - 1, 0); + } + + // for reference image + + // calculate cdf + for (int h = 1; h < bins; h++) { + float binVal = refhist.at(h, 0); + + refcdf.at(h, 0) = + refcdf.at(h, 0) + refcdf.at(h - 1, 0); + } + + // normalize histogram + for (int h = 0; h < bins; h++) { + refcdf.at(h, 0) = + refcdf.at(h, 0) / refcdf.at(bins - 1, 0); + } + + // for( int h = 0; h < bins; h++ ) + // { + // sciprint("%f\n",refcdf.at(h,0)); + + // } + + // sciprint("\n"); + + // for( int h = 0; h < bins; h++ ) + // { + // sciprint("%f\n",cdf.at(h,0)); + + // } + + float observed_cdf, minval; + float minindex; + + for (int i = 0; i < input_image.rows; i++) + for (int j = 0; j < input_image.cols; j++) { + observed_cdf = cdf.at(image.at(i, j), 0); + + minval = 1000; // will be overwritten + + for (int h = 0; h < bins; h++) { + if ((abs(refcdf.at(h, 0) - observed_cdf)) < minval) { + minval = abs(refcdf.at(h, 0) - observed_cdf); + minindex = h; + } + // sciprint("%d\n",minindex); + } + + input_image.at(i, j) = minindex; + } + transpose(input_image, input_image); + + return input_image; +} + +int opencv_imhistmatch(char *fname, unsigned long fname_len) { + SciErr sciErr; + int intErr = 0; + + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + + double num_bins = 256; // default number of bins for histogram calculation + + // checking input argument + CheckInputArgument(pvApiCtx, 2, 3); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get the number of input arguments + int inputarg = *getNbInputArgument(pvApiCtx); + + // get input matrix + + Mat image, new_image, r, g, b; + retrieveImage(image, 1); + + Mat ref; + retrieveImage(ref, 2); + + int case1 = 0; + if (image.channels() == 1) + + { + if (ref.channels() != 1) { + sciprint( + "If A is a grayscale image, the ref image must also be grayscale\n"); + return 0; + + } else { + case1 = 1; + } + } + + else if (image.channels() == 3) + + { + if (ref.channels() == 1) { + case1 = 2; + } + if (ref.channels() == 3) { + case1 = 3; + } + } + + else { + sciprint("Invalid Image\n"); + return 0; + } + + if (!((image.channels() == 3) || (image.channels() == 1))) { + sciprint("Invalid Image\n"); + return 0; + } + + if (!((ref.channels() == 3) || (ref.channels() == 1))) { + sciprint("Invalid Reference Image\n"); + return 0; + } + + if (inputarg == 3) { + // Get the number of bins for histogram calculation + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr3, &num_bins); + if (sciErr.iErr) { + return intErr; + } + + if (num_bins < 1) { + sciprint("Invalid number of histogram bins\n"); + return 0; + } + } + + int bins = (int)num_bins; + + if (case1 == 1) { + new_image = match(image, ref, bins); + + } + + else if (case1 == 2) { + Mat rgb[3]; + + split(image, rgb); + + r = match(rgb[0], ref, bins); + + g = match(rgb[1], ref, bins); + + b = match(rgb[2], ref, bins); + + vector channels; + + channels.push_back(r); + channels.push_back(g); + channels.push_back(b); + + merge(channels, new_image); + + transpose(new_image, new_image); + + } + + else if (case1 == 3) { + Mat rgb[3]; + Mat rgbref[3]; + + split(ref, rgbref); + split(image, rgb); + + r = match(rgb[0], rgbref[0], bins); + + g = match(rgb[1], rgbref[1], bins); + + b = match(rgb[2], rgbref[2], bins); + + vector channels; + + channels.push_back(r); + channels.push_back(g); + channels.push_back(b); + + merge(channels, new_image); + + transpose(new_image, new_image); + } + + // Normalizing the final image + cv::normalize(new_image, new_image, 0, 255, NORM_MINMAX, CV_8U); + + // sciprint("\n"); + + // for (int i = 0; i < new_image.rows; i++) { + // for (int j = 0; j < new_image.cols; j++) { + // sciprint(" %i ", new_image.at(i,j)); + + // } + + // sciprint("\n"); + // } + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_imhmax.cpp b/sci_gateway/cpp/opencv_imhmax.cpp new file mode 100644 index 0000000..3b1d486 --- /dev/null +++ b/sci_gateway/cpp/opencv_imhmax.cpp @@ -0,0 +1,111 @@ +/*************************************************** +Author : Yash S. Bhalgat +**************************************************** +Usage : transformed_image = imhmax(input_img, threshold, conn); + input is a 2-D array + conn must be equal to 4 or 8 +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_imhmax(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + double maximum, minimum; + double threshold, conn; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of threshold + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &threshold); + if(intErr) + return intErr; + + //for value of conn + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &conn); + if(intErr) + return intErr; + + + //taking the cases which can lead to an error + if(threshold<0) + { + sciprint("Positive Value Required for threshold. 1 value was used instead"); + threshold=1; + } + + if(conn!=8) conn=8; + + //connectivity taken is 8 + //main computations + Mat transformed_image(image.rows,image.cols,image.type()); + transformed_image = image; + + for(int x=1; x(x-1,y-1),image.at(x,y-1),image.at(x+1,y-1),image.at(x-1,y),image.at(x+1,y),image.at(x-1,y+1),image.at(x,y+1),image.at(x+1,y+1)}; + maximum = *max_element(myarray, myarray+8); + minimum = *min_element(myarray, myarray+8); + if(image.at(x,y) >= maximum){ + if(image.at(x,y)-minimum < threshold) transformed_image.at(x,y) = minimum; + } + } + } + + sciprint("Threshold is: %f", &threshold); + + //returning image + string tempstring = type2str(transformed_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,transformed_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imhmin.cpp b/sci_gateway/cpp/opencv_imhmin.cpp new file mode 100644 index 0000000..0a87246 --- /dev/null +++ b/sci_gateway/cpp/opencv_imhmin.cpp @@ -0,0 +1,96 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = imhmin(input_image, h) +Example: + im = imhmin(image, 80) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + void imhmin_imreconstruct(Mat, Mat, Mat&); + + int opencv_imhmin(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + double h; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Get the input image from the Scilab environment + Mat image; + retrieveImage(image, 1); + + // Get the address of 2nd argument, the H-Minima transform scalar + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the H-Minima transform scalar + intErr = getScalarDouble(pvApiCtx, piAddr, &h); + if(intErr) + { + return intErr; + } + + if (h < 0) + { + sciprint("Please enter a nonnegative scalar for H-minima transform.\n"); + return 0; + } + + Mat gray_image, m, dst; + cvtColor(image, gray_image, CV_BGR2GRAY); + min((gray_image+h), 255, m); + imhmin_imreconstruct(gray_image, m, dst); + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, dst, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } + void imhmin_imreconstruct(Mat g, Mat f, Mat& dest) + { + Mat m0, m1, m; + m1 = f; + do { + m0 = m1.clone(); + erode(m0, m, Mat()); + max(g, m, m1); + } while(countNonZero(m1 != m0) != 0); + dest = m1.clone(); + } + +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imimposemin.cpp b/sci_gateway/cpp/opencv_imimposemin.cpp new file mode 100644 index 0000000..b610158 --- /dev/null +++ b/sci_gateway/cpp/opencv_imimposemin.cpp @@ -0,0 +1,89 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = imimposemin(mask, marker) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + void imimposemin_imreconstruct(Mat, Mat, Mat&); + + int opencv_imimposemin(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + int i, j; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Get the input image from the Scilab environment + Mat mask, marker; + retrieveImage(mask, 1); + retrieveImage(marker, 2); + + Mat gray_mask, gray_marker; + cvtColor(mask, gray_mask, CV_BGR2GRAY); + cvtColor(marker, gray_marker, CV_BGR2GRAY); + + for (i = 0; i < marker.cols; i++) + { + for (j = 0; j < marker.rows; j++) + { + if (!(marker.at(i,j) == 0 || marker.at(i,j) == 1 || marker.at(i,j) == 255)) + { + sciprint("Please ensure marker image (second argument) is binary."); + return 0; + } + } + } + + Mat m, dst; + min((gray_mask + 1), gray_marker, m); + imimposemin_imreconstruct(m, gray_marker, dst); + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, dst, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } + void imimposemin_imreconstruct(Mat g, Mat f, Mat& dest) + { + Mat m0, m1, m; + m1 = f; + do { + m0 = m1.clone(); + erode(m0, m, Mat()); + max(g, m, m1); + } while(countNonZero(m1 != m0) != 0); + dest = m1.clone(); + } + +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_imlincomb.cpp b/sci_gateway/cpp/opencv_imlincomb.cpp new file mode 100644 index 0000000..a3d9a9a --- /dev/null +++ b/sci_gateway/cpp/opencv_imlincomb.cpp @@ -0,0 +1,84 @@ +/******************************************************** +Function :imlincomb +Syntax :C=imlincomb(x1,A1,x2,A2...xn,An) + A1...An image matrix,x1...xn double values +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_imlincomb(char *fname, unsigned long fname_len) + { + SciErr sciErr; + Mat A,B,C,tmp; + int* piAddr2=NULL; + int* piAddr4=NULL; + int iType = 0; + int iRet = 0; + double value= 0; + CheckOutputArgument(pvApiCtx, 1, 1); + for(int i=1;i<=Rhs;i=i+2) + { + sciErr = getVarAddressFromPosition(pvApiCtx,i,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isDoubleType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getScalarDouble(pvApiCtx, piAddr2, &value); + } + } + if(i!=Rhs) + { + retrieveImage(A,i+1); + A.convertTo(A,CV_64F); + C=A*value; + if((i+1)==2) + { + C.copyTo(tmp); + A.copyTo(B); + } + else + { + add(C,tmp,tmp); + + } + } + else + { + add(tmp,value,tmp); + } + A.release(); + C.release(); + } + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(tmp.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,tmp,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_immultiply.cpp b/sci_gateway/cpp/opencv_immultiply.cpp new file mode 100644 index 0000000..ce104a5 --- /dev/null +++ b/sci_gateway/cpp/opencv_immultiply.cpp @@ -0,0 +1,71 @@ +/******************************************************** +Function :immultiply +Syntax :C=immultiply(A,B) +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_immultiply(char *fname, unsigned long fname_len) + { + SciErr sciErr; + Mat A,B,C; + int* piAddr2=NULL; + int iType = 0; + int iRet = 0; + double value= 0; + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + retrieveImage(A,1); + sciErr =getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + sciErr = getVarType(pvApiCtx, piAddr2, &iType); + if(iType==sci_list) + { + retrieveImage(B,2); + Size s1=A.size(); + Size s2=B.size(); + if(s1!=s2) + { + sciprint("\nBoth input should be same size"); + return 0; + } + multiply(A,B,C); + } + else + { + if(isDoubleType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getScalarDouble(pvApiCtx, piAddr2, &value); + } + } + C=A*value; + } + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(C.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,C,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_impixel.cpp b/sci_gateway/cpp/opencv_impixel.cpp new file mode 100644 index 0000000..464efde --- /dev/null +++ b/sci_gateway/cpp/opencv_impixel.cpp @@ -0,0 +1,122 @@ +/******************************************************** +Function :impixel +Syntax :C=impixel(A,[1 2 3],[1 2 3]) +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_impixel(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + Mat image; + int* piAddr2=NULL; + int* piAddr3=NULL; + double *value1= 0; + double *value2= 0; + int iRows1 = 0; + int iCols1 = 0; + int iRows2 = 0; + int iCols2 = 0; + double n1,n2; + CheckInputArgument(pvApiCtx, 3,3); + CheckOutputArgument(pvApiCtx, 1, 1); + retrieveImage(image,1); + //retrieve coordinates + sciErr =getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows1, &iCols1,&value1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr =getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr= getMatrixOfDouble(pvApiCtx, piAddr3, &iRows2, &iCols2,&value2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if((iRows1!=iRows2)||(iCols1!=iCols2)) + { + sciprint("\n Give proper number of coordinates"); + return 0; + } + for(int i=0;i(value1[j-1],value2[j-1]); + float R= intensity.val[2]; + float G= intensity.val[1]; + float B= intensity.val[0]; + output[m]=R; + output[m+1]=G; + output[m+2]=B; + m=m+3; + } + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1,3,iCols1,output); + free(output); // Data have been copied into Scilab memory + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_impyramid.cpp b/sci_gateway/cpp/opencv_impyramid.cpp new file mode 100644 index 0000000..ecd0f7c --- /dev/null +++ b/sci_gateway/cpp/opencv_impyramid.cpp @@ -0,0 +1,103 @@ +/******************************************************** +Function :impyramid +Syntax :B=impyramid(A,direction) +*direction :expand or reduce +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_impyramid(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr2=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + int* piLen = NULL; + char **val; + int i,j,k=0,l=0; + int error; + String s; + int iRet = 0; + char* pstData = NULL; + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + sciErr = getVarAddressFromPosition(pvApiCtx, 2,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isStringType(pvApiCtx, piAddr3)) + { + if(isScalar(pvApiCtx, piAddr3)) + { + + iRet = getAllocatedSingleString(pvApiCtx, piAddr3, &pstData); + } + } + + + k=image.rows; + l=image.cols; + Mat new_image; + if(strcmp(pstData,"expand")==0) + { + Size size((k*2)-1,(l*2)-1); + resize(image,new_image,size); + } + else if(strcmp(pstData,"reduce")==0) + { + Size size(k/2,l/2); + resize(image,new_image,size); + } + else + { + sciprint("Not valid 2nd argument"); + } + + + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imread.cpp b/sci_gateway/cpp/opencv_imread.cpp new file mode 100644 index 0000000..6e7e7b9 --- /dev/null +++ b/sci_gateway/cpp/opencv_imread.cpp @@ -0,0 +1,99 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + #include "../common.cpp" + +int opencv_imread(char *fname, unsigned long fname_len) +{ + SciErr sciErr; + int iLen = 0; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int *piAddr = NULL; + int *piLen = NULL; + char **pstData = NULL; + int **pstData1 = NULL; + int i,j,k=0; + + + //Checking number of input and output arguments (enviromnet variable, min arguments, max arguments) + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // get Address of inputs + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + pstData[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating image matrix, by getting the image at the specified path + Mat img = imread(pstData[0],CV_LOAD_IMAGE_COLOR); + + string tempstring = type2str(img.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,img,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imrect.cpp b/sci_gateway/cpp/opencv_imrect.cpp new file mode 100644 index 0000000..d19f58e --- /dev/null +++ b/sci_gateway/cpp/opencv_imrect.cpp @@ -0,0 +1,110 @@ +/******************************************************** +Author: Dhruti Shah +******************************************************** +Usage: return_image = imrect(input_image, x_list, y_list) +Example: + im = imrect(image, [x1 x2], [y1 y2]) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imrect(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRowsR=0,iColsR=0,iColsC=0,iRowsC=0; + int *piAddr = NULL; + int *piAddr2 = NULL; + double *pstDataY = NULL; + double *pstDataX = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Get the input image from the Scilab environment + Mat image; + retrieveImage(image, 1); + + // Get the address of 2nd argument, the x-coordinate list + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the address of the 3rd agument, the y-coordinate list + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the x-coordinate list in the form of a matrix + // No. of columns = 2 + // No. of rows = 1 + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRowsC, &iColsC, &pstDataX); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the y-coordinate list in the form of a matrix + // No. of columns = 2 + // No. of rows = 1 + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRowsR, &iColsR, &pstDataY); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Point points[1][2]; + + for (int i = 0; i < 2 ; i++) + { + if (pstDataY[i] < 0 || pstDataX[i] < 0) + { + sciprint("Coordinates cannot be negative.\n"); + return 0; + } + else + points[0][i] = Point(pstDataX[i], pstDataY[i]); + } + + rectangle(image,points[0][0],points[0][1],Scalar(0,0,0),2); + + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_imresize.cpp b/sci_gateway/cpp/opencv_imresize.cpp new file mode 100644 index 0000000..190ab5e --- /dev/null +++ b/sci_gateway/cpp/opencv_imresize.cpp @@ -0,0 +1,94 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imresize(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + double alpha, beta; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + + //for new no. of rows + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &alpha); + if(sciErr.iErr) + { + return intErr; + } + + //for new no. of columns + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &beta); + if(sciErr.iErr) + { + return intErr; + } + + + Mat new_image; + Size size(alpha, beta); + + if(alpha<=0 || beta<=0) + { + sciprint("Invalid new size\n"); + return 0; + } + //resizes the image to new_image to the size given by the user + resize(image,new_image,size); + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imrotate.cpp b/sci_gateway/cpp/opencv_imrotate.cpp new file mode 100644 index 0000000..074101c --- /dev/null +++ b/sci_gateway/cpp/opencv_imrotate.cpp @@ -0,0 +1,81 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : rotated_image=imrotate(sourcrimage,angle); */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int opencv_imrotate(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double pstData; + int* pstData2 = NULL; + double *rrows; + double *rcols; + int error; + //function to check number of input variable + CheckInputArgument(pvApiCtx, 2, 2); + //function to check number of output variable + CheckOutputArgument(pvApiCtx, 1, 1); + // get Address of first input + Mat img; + retrieveImage(img,1); + //second argument + //fetching address of second argument + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch second argument i.e angle + error=getScalarDouble(pvApiCtx,piAddr2,&pstData); + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //creating distination matrix + Mat dst; + //following set of functions for image rotation + Point2f pt((img.cols)/2., (img.rows)/2.); + Mat r1 = getRotationMatrix2D(pt, pstData, 1.0); + warpAffine(img, dst, r1, Size(dst.cols, dst.rows)); + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + string tempstring = type2str(dst.type()); + char* checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + //returning output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + + + + } +} diff --git a/sci_gateway/cpp/opencv_imrotate.cpp~ b/sci_gateway/cpp/opencv_imrotate.cpp~ new file mode 100644 index 0000000..a2b0e54 --- /dev/null +++ b/sci_gateway/cpp/opencv_imrotate.cpp~ @@ -0,0 +1,81 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : rotated_image=imrotate(sourcrimage,angle); */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int imrotate(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double pstData; + int* pstData2 = NULL; + double *rrows; + double *rcols; + int error; + //function to check number of input variable + CheckInputArgument(pvApiCtx, 2, 2); + //function to check number of output variable + CheckOutputArgument(pvApiCtx, 1, 1); + // get Address of first input + Mat img; + retrieveImage(img,1); + //second argument + //fetching address of second argument + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch second argument i.e angle + error=getScalarDouble(pvApiCtx,piAddr2,&pstData); + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //creating distination matrix + Mat dst; + //following set of functions for image rotation + Point2f pt((img.cols)/2., (img.rows)/2.); + Mat r1 = getRotationMatrix2D(pt, pstData, 1.0); + warpAffine(img, dst, r1, Size(dst.cols, dst.rows)); + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + string tempstring = type2str(dst.type()); + char* checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + //returning output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + + + + } +} diff --git a/sci_gateway/cpp/opencv_imsharpen.cpp b/sci_gateway/cpp/opencv_imsharpen.cpp new file mode 100644 index 0000000..ad0a593 --- /dev/null +++ b/sci_gateway/cpp/opencv_imsharpen.cpp @@ -0,0 +1,59 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_imsharpen(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + + Mat tmp; + cv::GaussianBlur(image, tmp, cv::Size(5,5), 5); + cv::addWeighted(image, 1.5, tmp, -0.5, 0, image); + + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, image, 1); + free(checker); + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imshowpair.cpp b/sci_gateway/cpp/opencv_imshowpair.cpp new file mode 100644 index 0000000..7feb00a --- /dev/null +++ b/sci_gateway/cpp/opencv_imshowpair.cpp @@ -0,0 +1,474 @@ +/*//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +Created By: Riddhish Bhalodia +Date: 30th September 2015 + +Usage: + +imshowpair(Mat image1,Mat image2,String method,String scaling) + +1) image1 : Input image1 +2) image2 : Input image2 +3) method : Method for the type of display + + | Method | Function | + | | | + | "montage" | Display Images side by side | + | "blend" | Display the blend of two Images alpha=0.5 | + | "diff" | Display the absolute difference between Images | + | "falsecolor" | Display image1 by green channel and image2 by red | + +4) scaling : The scaling of the images + + | Scaling | Function | + | | | + | "none" | No scaling to the images | + | "joint" | Scale the joint images | + | "independent" | Scale the images independently before processing | + + + +*///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imshowpair(char *fname, unsigned long fname_len){ + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piLen1 = NULL; + int *piLen2 = NULL; + char **method = NULL; + char **scaling = NULL; + + Mat image1,image2; + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + retrieveImage(image1, 1); + retrieveImage(image2, 2); + + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen1 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen1, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + method = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i = 0 ; i < iRows * iCols ; i++) + method[i] = (char*)malloc(sizeof(char) * (piLen1[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen1, method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // now the fourth argument which is aging a string + + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen2 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen2, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + scaling = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i = 0 ; i < iRows * iCols ; i++) + scaling[i] = (char*)malloc(sizeof(char) * (piLen2[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen2, scaling); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + char x1[15] = "montage"; + char x2[15] = "diff"; + char x3[15] = "blend"; + char x4[15] = "falsecolor"; + char y1[15] = "joint"; + char y2[15] = "independent"; + char y3[15] = "none"; + // Error check on method + if(!(strcmp(method[0],x1)==0 || strcmp(method[0],x2)==0 || strcmp(method[0],x3)==0 || strcmp(method[0],x4)==0 )){ + sciprint("Method input not correct, choose from 'blend','diff','montage' or 'falsecolor' \n"); + return 0; + } + // Error check on scaling + + if(!(strcmp(scaling[0],y1)==0 || strcmp(scaling[0],y2)==0 || strcmp(scaling[0],y3)==0 )){ + sciprint("Method input not correct, choose from 'none','diff','montage' or 'falsecolor' \n"); + return 0; + } + + int max_rows,max_cols; + Mat out; + max_rows = max(image1.rows,image2.rows); + max_cols = max(image1.cols,image2.cols); + if(image1.channels() == 1){ cvtColor(image1,image1,CV_BGR2GRAY);} + if(image2.channels() == 1){ cvtColor(image2,image2,CV_BGR2GRAY);} + + vector rgbChannels(3); + // for montage and no scaling plus joint scaling + + if(strcmp(method[0],x1)==0 && ((strcmp(scaling[0],y1)==0)||(strcmp(scaling[0],y3)==0))) { + out = Mat::zeros(max_rows,2*max_cols,image1.type()); + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j + max_cols)[0] = image2.at(i,j)[0]; + out.at(i,j + max_cols)[1] = image2.at(i,j)[1]; + out.at(i,j + max_cols)[2] = image2.at(i,j)[2]; + } + } + } + // for montage and scaling independent + if(strcmp(method[0],x1)==0 && strcmp(scaling[0],y2)==0) { + + + split(image1, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image1); + split(image2, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image2); + + out = Mat::zeros(max_rows,2*max_cols,image1.type()); + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j + max_cols)[0] = image2.at(i,j)[0]; + out.at(i,j + max_cols)[1] = image2.at(i,j)[1]; + out.at(i,j + max_cols)[2] = image2.at(i,j)[2]; + } + } + } + + // for diff and no scaling plus joint scaling + if(strcmp(method[0],x2)==0 && ((strcmp(scaling[0],y1)==0)||(strcmp(scaling[0],y3)==0))) { + + // cvtColor(image1,image1,CV_BGR2GRAY); + // cvtColor(image2,image2,CV_BGR2GRAY); + out = Mat::zeros(max_rows,max_cols,image1.type()); + + for(int i=0;i(i,j)[0] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + } + } + for(int i=0;i(i,j)[0] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[1] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + } + for(int i=0;i(i,j)[0] = abs((image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2]) - (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2]))/3; + out.at(i,j)[1] = abs((image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2]) - (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2]))/3; + out.at(i,j)[2] = abs((image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2]) - (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2]))/3; + } + } + } + // for diff and independent scaling + if(strcmp(method[0],x2)==0 && strcmp(scaling[0],y2)==0) { + + + split(image1, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image1); + split(image2, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image2); + + // cvtColor(image1,image1,CV_BGR2GRAY); + // cvtColor(image2,image2,CV_BGR2GRAY); + out = Mat::zeros(max_rows,max_cols,image1.type()); + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j)[0] = image2.at(i,j)[0]; + out.at(i,j)[1] = image2.at(i,j)[1]; + out.at(i,j)[2] = image2.at(i,j)[2]; + } + } + for(int i=0;i(i,j)[0] = abs(image1.at(i,j)[0] - image2.at(i,j)[0]); + out.at(i,j)[1] = abs(image1.at(i,j)[1] - image2.at(i,j)[1]); + out.at(i,j)[2] = abs(image1.at(i,j)[2] - image2.at(i,j)[2]); + } + } + } + + // for blend and no scaling[0] plus joint scaling[0] + if(strcmp(method[0],x3)==0 && ((strcmp(scaling[0],y1)==0)||(strcmp(scaling[0],y3)==0))) { + out = Mat::zeros(max_rows,max_cols,image1.type()); + + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j)[0] = image2.at(i,j)[0]; + out.at(i,j)[1] = image2.at(i,j)[1]; + out.at(i,j)[2] = image2.at(i,j)[2]; + } + } + + for(int i=0;i(i,j)[0] = 0.5*(image1.at(i,j)[0] + image2.at(i,j)[0]); + out.at(i,j)[1] = 0.5*(image1.at(i,j)[1] + image2.at(i,j)[1]); + out.at(i,j)[2] = 0.5*(image1.at(i,j)[2] + image2.at(i,j)[2]); + } + } + + } + // for blend and independent scaling[0] + if(strcmp(method[0],x3)==0 && strcmp(scaling[0],y2)==0) { + out = Mat::zeros(max_rows,max_cols,image1.type()); + + split(image1, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image1); + split(image2, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image2); + + for(int i=0;i(i,j)[0] = image1.at(i,j)[0]; + out.at(i,j)[1] = image1.at(i,j)[1]; + out.at(i,j)[2] = image1.at(i,j)[2]; + } + } + for(int i=0;i(i,j)[0] = image2.at(i,j)[0]; + out.at(i,j)[1] = image2.at(i,j)[1]; + out.at(i,j)[2] = image2.at(i,j)[2]; + } + } + + for(int i=0;i(i,j)[0] = 0.5*(image1.at(i,j)[0] + image2.at(i,j)[0]); + out.at(i,j)[1] = 0.5*(image1.at(i,j)[1] + image2.at(i,j)[1]); + out.at(i,j)[2] = 0.5*(image1.at(i,j)[2] + image2.at(i,j)[2]); + } + } + + } + + // for falsecolor and no scaling[0] plus joint scaling[0] + if(strcmp(method[0],x4)==0 && ((strcmp(scaling[0],y1)==0)||(strcmp(scaling[0],y3)==0))) { + + out = Mat::zeros(max_rows,max_cols,image1.type()); + + // image 1 green image2 red + for(int i=0;i(i,j)[0] = 0; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = 0; + } + } + for(int i=0;i(i,j)[0] = 0; + out.at(i,j)[1] = 0; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + } + for(int i=0;i(i,j) == image2.at(i,j)){ + out.at(i,j)[0] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[1] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + else{ + out.at(i,j)[0] = 0; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + + } + } + + } + // for falsecolor and independent scaling[0] + if(strcmp(method[0],x4)==0 && strcmp(scaling[0],y2)==0) { + out = Mat::zeros(max_rows,max_cols,image1.type()); + //scale image 1 and image 2 + + split(image1, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image1); + split(image2, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,image2); + // image 1 green image2 red + for(int i=0;i(i,j)[0] = 0; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = 0; + } + } + for(int i=0;i(i,j)[0] = 0; + out.at(i,j)[1] = 0; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + } + for(int i=0;i(i,j) == image2.at(i,j)){ + out.at(i,j)[0] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[1] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + else{ + out.at(i,j)[0] = 0; + out.at(i,j)[1] = (image1.at(i,j)[0] + image1.at(i,j)[1] + image1.at(i,j)[2])/3; + out.at(i,j)[2] = (image2.at(i,j)[0] + image2.at(i,j)[1] + image2.at(i,j)[2])/3; + } + + } + } + + } + + // for joint scaling[0] + if(strcmp(scaling[0],y3)==0){ + split(out, rgbChannels); + normalize(rgbChannels[0],rgbChannels[0],0,255,CV_MINMAX); + normalize(rgbChannels[1],rgbChannels[1],0,255,CV_MINMAX); + normalize(rgbChannels[2],rgbChannels[2],0,255,CV_MINMAX); + merge(rgbChannels,out); + } + + if(image1.channels()==1 && image2.channels()==1){ + cvtColor(out,out,CV_BGR2GRAY); + } + + // out is the return image + namedWindow("Output",CV_WINDOW_AUTOSIZE); + imshow("Output",out); + // waitKey(0); + string tempstring = type2str(out.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,out,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_imsubtract.cpp b/sci_gateway/cpp/opencv_imsubtract.cpp new file mode 100644 index 0000000..1d7f3bd --- /dev/null +++ b/sci_gateway/cpp/opencv_imsubtract.cpp @@ -0,0 +1,71 @@ +/******************************************************** +Function :imsubtract +Syntax :C=imsubtract(A,B) +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_imsubtract(char *fname, unsigned long fname_len) + { + SciErr sciErr; + Mat A,B,C; + int* piAddr2=NULL; + int iType = 0; + int iRet = 0; + double value= 0; + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + retrieveImage(A,1); + sciErr =getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + sciErr = getVarType(pvApiCtx, piAddr2, &iType); + if(iType==sci_list) + { + retrieveImage(B,2); + Size s1=A.size(); + Size s2=B.size(); + if(s1!=s2) + { + sciprint("\nBoth input should be same size"); + return 0; + } + subtract(A,B,C); + } + else + { + if(isDoubleType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getScalarDouble(pvApiCtx, piAddr2, &value); + } + } + subtract(A,value,C); + } + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(C.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,C,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_imwarp.cpp b/sci_gateway/cpp/opencv_imwarp.cpp new file mode 100644 index 0000000..d5bc6df --- /dev/null +++ b/sci_gateway/cpp/opencv_imwarp.cpp @@ -0,0 +1,104 @@ +/******************************************************** + Author: V Srinivas +********************************************************* + imwarp(image,matrix(2*3))=output. +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_imwarp(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + //int *piAddr2 = NULL; + double *tform; + int maxx,maxy,minx,miny; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2);//two arguments 1 takes the image and the other takes the transformation matrix + CheckOutputArgument(pvApiCtx, 1, 1); + //retieve Image + Mat image; + retrieveImage(image,1); + //retreive the transformation matrix + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols,&tform); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + if(iRows!=2 || iCols!=3)//checking if the transformation matrix is of the desired size + { + sciprint("Please enter a valid transformation matrix"); + return -1; + } + Mat dst; + //This and the next few lines are dedicated to finding the size of the frame in which the whole transformed image can be viewed. + double x [4]={0,0,image.cols-1,image.cols-1}; + double y [4]={0,image.rows-1,0,image.rows-1}; + int modx[4]; + int mody[4]; + for(int i=0;i<4;i++)// the modifications performed by the matrix is performed on the edges of the image to find the optimum size. This works as the transformation is linear so the edges always are on the extreme points. + { + modx[i]=(int)(tform[0] * x[i] + tform[1] * y[i] + tform[4]); + mody[i]=(int)(tform[2] * x[i] + tform[3] * y[i] + tform[5]); + //cout<<*(modx+i)<<" "<<*(mody+i)<(2,3)<maxx) + {maxx=modx[i];} + if(mody[i]maxy) + {maxy=mody[i];} + } + warpAffine(image,dst,trans,Size(maxx-minx,maxy-miny)); + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + free(checker); + + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + + } + +} diff --git a/sci_gateway/cpp/opencv_imwrite.cpp b/sci_gateway/cpp/opencv_imwrite.cpp new file mode 100644 index 0000000..2815ac9 --- /dev/null +++ b/sci_gateway/cpp/opencv_imwrite.cpp @@ -0,0 +1,85 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_imwrite(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piLen = NULL; + char **pstData=NULL; + int i,j,k; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + //CheckOutputArgument(pvApiCtx, 0, 0) ; + Mat image; + retrieveImage(image, 1); + + //Now, retriving path + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i = 0 ; i < iRows * iCols ; i++) + { + pstData[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //image path will contain the path where the image has to be written + string image_path=pstData[0]; + + // image_path="\""+image_path+"\""; + + //writes to the path + imwrite(image_path,image); + + return 0; + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_ind2gray.cpp b/sci_gateway/cpp/opencv_ind2gray.cpp new file mode 100644 index 0000000..60c45ba --- /dev/null +++ b/sci_gateway/cpp/opencv_ind2gray.cpp @@ -0,0 +1,115 @@ +/******************************************************** +Author: Vinay + +Function: ind2gray(image, colormap) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_ind2gray(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int cRows=0,cCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image, imgcpy; + retrieveImage(image, 1); + string tempstring = type2str(image.type()); + char *imtype; + imtype = (char *)malloc(tempstring.size() + 1); + memcpy(imtype, tempstring.c_str(), tempstring.size() + 1); + bool integer = true; + int scale = 1; + double error = 0; + + if (strcmp(imtype,"8U")==0) { + integer = true; + scale = 255; + error = 0.5; + } + else if (strcmp(imtype,"16U")==0) { + integer = true; + scale = 65535; + error = 0.5; + } + else if (strcmp(imtype,"32F")==0 || strcmp(imtype,"64F")==0) { + integer = false; + scale = 1; + error = 0; + } + else { + sciprint("Invalid image"); + return 0; + } + iRows = image.rows; + iCols = image.cols; + image.convertTo(imgcpy, CV_64F); + + Mat cmap, cmapcpy; + retrieveImage(cmap, 2); + cRows = cmap.rows; + cCols = cmap.cols; + cmap.convertTo(cmapcpy, CV_64F); + + for (int i=0; i(i,j)<0 || cmapcpy.at(i,j)>1) { + sciprint("Invalid colormap"); + return 0; + } + } + } + + Mat gray = Mat::zeros(image.size(), CV_64F); + + + for (int i=0; i(i, j); + if (temp >= cRows) { + temp = cRows - 1; + } + if (!integer) { + if (temp!=0) {temp-=1;} + } + gray.at(i,j) = (0.2989 * cmapcpy.at(temp, 0) + 0.5870 * cmapcpy.at(temp, 1) + 0.1140 * cmapcpy.at(temp, 2)) ; + + } + } + + Mat grayimage; + gray.convertTo(grayimage, image.type(), scale, error); + returnImage(imtype,grayimage,1); + free(imtype); + + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_ind2rgb.cpp b/sci_gateway/cpp/opencv_ind2rgb.cpp new file mode 100644 index 0000000..872cbfb --- /dev/null +++ b/sci_gateway/cpp/opencv_ind2rgb.cpp @@ -0,0 +1,146 @@ +/******************************************************** +Author: Vinay + +Function: ind2rgb(image, colormap) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_ind2rgb(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int cRows=0,cCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + //unsigned short int *image = NULL; + double *map = NULL; + int i,j,k; + double x, y, width, height; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image, imgcpy; + retrieveImage(image, 1); + string tempstring = type2str(image.type()); + char *imtype; + imtype = (char *)malloc(tempstring.size() + 1); + memcpy(imtype, tempstring.c_str(), tempstring.size() + 1); + bool integer; + + if (strcmp(imtype,"8U")==0 || strcmp(imtype,"16U")==0) { + integer = true; + } + else if (strcmp(imtype,"32F")==0 || strcmp(imtype,"64F")==0) { + integer = false; + } + else { + sciprint("Invalid image"); + return 0; + } + iRows = image.rows; + iCols = image.cols; + image.convertTo(imgcpy, CV_64F); + + Mat cmap, cmapcpy; + retrieveImage(cmap, 2); + cRows = cmap.rows; + cCols = cmap.cols; + cmap.convertTo(cmapcpy, CV_64F); + + for (int i=0; i(i,j)<0 || cmapcpy.at(i,j)>1) { + sciprint("Invalid colormap"); + return 0; + } + } + } + + + double *r,*g,*b; + r=(double *)malloc(sizeof(double)*iRows*iCols); + g=(double *)malloc(sizeof(double)*iRows*iCols); + b=(double *)malloc(sizeof(double)*iRows*iCols); + int m = 0; + + + for (int i=0; i(i, j); + if (temp >= cRows) { + temp = cRows - 1; + } + if (!integer) { + if (temp!=0) {temp-=1;} + } + r[i + iRows*j] = cmapcpy.at(temp, 0); + g[i + iRows*j] = cmapcpy.at(temp, 1); + b[i + iRows*j] = cmapcpy.at(temp, 2); + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Adding the R value matrix to the list + //Syntax : createMatrixOfInteger32InList(void* _pvCtx, int _iVar, int* _piParent, int _iItemPos, int _iRows, int _iCols, const int* _piData) + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 1,iRows, iCols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 2,iCols, iRows, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 3, iCols, iRows, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_integralImage.cpp b/sci_gateway/cpp/opencv_integralImage.cpp new file mode 100644 index 0000000..46e0082 --- /dev/null +++ b/sci_gateway/cpp/opencv_integralImage.cpp @@ -0,0 +1,145 @@ +/* + * integralImage + * + * integralImage in scilab + * + */ + +// Created by Samiran Roy, mail: samiranroy@cse.iitb.ac.in +// An implementation of integralImage method of matlab +// Usage: +// integralImage(I) : Calculate the integral image of I, I must be grayscale + +// method : 'upright' (default) +// method : 'rotated' The area sums are calulated over a rectangle, which is rotated 45 degrees + + +// Known Changes from Matlab: +/* + * 1) None, as of now + */ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" { +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" + +int opencv_integralImage(char *fname, unsigned long fname_len) { + SciErr sciErr; + int intErr = 0; + + + int *piAddr1 = NULL; + + int error; + +// String holding the second argument + int iRet = 0; + char* pstData = NULL; + +// Checking input argument + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + +// Get input image + + Mat image; + retrieveImage(image, 1); + + + + for (int i = 0; i < image.rows; i++) { + for (int j = 0; j < image.cols; j++) { + sciprint("%f ", image.at(i,j)); + + } + + sciprint("\n"); + } + +// Error Checks + + if (image.channels() > 1) { + sciprint("The image must be grayscale."); + return 0; + } + +// Output variables holding integralImage, squared integralImage, integralImage over rectangle rotated by 45 degrees + Mat new_image, integralimage, squaredimage, rotatedimage; + + integral(image, integralimage, squaredimage, rotatedimage, -1); + +// Get the number of input arguments + int inputarg = *getNbInputArgument(pvApiCtx); + + if (inputarg == 1) + integralimage.copyTo(new_image); + + if (inputarg == 2) + + { + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr1); + + if (sciErr.iErr) { + printError(&sciErr, 0); + return 0; + } + + if (isStringType(pvApiCtx, piAddr1)) { + if (isScalar(pvApiCtx, piAddr1)) { + + iRet = getAllocatedSingleString(pvApiCtx, piAddr1, &pstData); + } + } + if (strcmp(pstData, "rotated") == 0) { + rotatedimage.copyTo(new_image); + } else if (strcmp(pstData, "upright") == 0) { + integralimage.copyTo(new_image); + } else { + sciprint("Unknown Parameter Name:%s\n", pstData); + + } + + } + + // sciprint("\n"); + + // for (int i = 0; i < new_image.rows; i++) { + // for (int j = 0; j < new_image.cols; j++) { + // sciprint("%f ", new_image.at(i,j)); + + // } + + // sciprint("\n"); + // } + + // new_image is sent to scilab as output + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *) malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments (pvApiCtx); + return 0; + +} +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_lab2double.cpp b/sci_gateway/cpp/opencv_lab2double.cpp new file mode 100644 index 0000000..c4eeeb3 --- /dev/null +++ b/sci_gateway/cpp/opencv_lab2double.cpp @@ -0,0 +1,130 @@ +/*-------------------------------------- + function-lab2double + Author :Tess Zacharias +------------------------------------- */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; + int opencv_lab2double(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int iRows = 0; + int iCols = 0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + int i,j; + int error; + float L,a,b; + int *piAddrNew = NULL; + CheckOutputArgument(pvApiCtx,1,1); + int k=0; + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int m=0; + double *r1,*g1,*b1; + r1=(double *)malloc(sizeof(double)*iRows*iCols); + g1=(double *)malloc(sizeof(double)*iRows*iCols); + b1=(double *)malloc(sizeof(double)*iRows*iCols); + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + // #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 +#define REF_Y 1.00000; +#define REF_Z 1.08883; + +struct Color +{ + float R,G,B,X,Y,Z,L,a,b; +}; + +Color lab2rgb1(float L, float a, float b) +{ + + + float y = (L + 16) / 116; + float x = a / 500 + y; + float z = y - b / 200; + if ( pow( y , 3 ) > 0.008856 ) + { + y = pow( y , 3 ); + } + else + { + y = ( y - 16 / 116 ) / 7.787; + } + if ( pow( x , 3 ) > 0.008856 ) + { + x = pow( x , 3 ); + } + else + { + x = ( x - 16 / 116 ) / 7.787; + } + if ( pow( z , 3 ) > 0.008856 ) + { + z = pow( z , 3 ); + } + else + { + z = ( z - 16 / 116 ) / 7.787; + } + float X= x *REF_X; + float Y= y *REF_Y; + float Z = z *REF_Z; + //X = X/100.f; //R 0..1 + //Y= Y/100.f; //G 0..1 + //Z= Z/100.f; //B 0..1 + float R1=3.240479*X-1.537150*Y -0.498535 *Z; + float G1=-0.969256*X+1.875992 *Y+0.041556* Z; + float B1=0.055648*X-0.204043*Y +1.057311*Z; + if( R1 > 0.0031308 ) + R1 = 1.055 * ( pow( R1, 1./2.4 ) ) - 0.055; + else + R1 = 12.92 * R1; + if( G1 > 0.0031308 ) + G1 = 1.055 * ( pow( G1, 1./2.4 ) ) - 0.055; + else + G1 = 12.92 * G1; + if( B1 > 0.0031308 ) + B1 = 1.055 * ( pow( B1, 1./2.4 ) ) - 0.055; + else + B1 = 12.92 * B1; + + + Color rgb; + rgb.R= R1; + rgb.G= G1; + rgb.B= B1; + return rgb; + +} + Color lab2rgb2(float L, float a, float b,char *s) +{ + double REF_X1; + double REF_Y1; + double REF_Z1; + if(!strcmp(s,"a")) + { + REF_X1=1.0985; + REF_Y1= 1.0000; + REF_Z1=0.3558; + } + else if(!strcmp(s,"c")) + { + REF_X1=0.9807; + REF_Y1= 1.0000; + REF_Z1=1.1822; + } + else if(!strcmp(s,"d50")) + { + REF_X1=0.9642; + REF_Y1= 1.0000; + REF_Z1=0.8251; + } + else if(!strcmp(s,"d65")) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(!strcmp(s,"icc")) + { + REF_X1=0.962; + REF_Y1=1.000; + REF_Z1= 0.8249; + } + else if(!strcmp(s,"e")) + { + REF_X1=1.000; + REF_Y1=1.000; + REF_Z1=1.000; + } + + float y = (L + 16) / 116; + float x = a / 500 + y; + float z = y - b / 200; + if ( pow( y , 3 ) > 0.008856 ) + { + y = pow( y , 3 ); + } + else + { + y = ( y - 16 / 116 ) / 7.787; + } + if ( pow( x , 3 ) > 0.008856 ) + { + x = pow( x , 3 ); + } + else + { + x = ( x - 16 / 116 ) / 7.787; + } + if ( pow( z , 3 ) > 0.008856 ) + { + z = pow( z , 3 ); + } + else + { + z = ( z - 16 / 116 ) / 7.787; + } + float X= x *REF_X; + float Y= y *REF_Y; + float Z = z *REF_Z; + X = X/100.f; //R 0..1 + Y= Y/100.f; //G 0..1 + Z= Z/100.f; //B 0..1 + float R1=3.240479*X-1.537150*Y -0.498535 *Z; + float G1=-0.969256*X+1.875992 *Y+0.041556* Z; + float B1=0.055648*X-0.204043*Y +1.057311*Z; + if( R1 > 0.0031308 ) + R1 = 1.055 * ( pow( R1, 1./2.4 ) ) - 0.055; + else + R1 = 12.92 * R1; + if( G1 > 0.0031308 ) + G1 = 1.055 * ( pow( G1, 1./2.4 ) ) - 0.055; + else + G1 = 12.92 * G1; + if( B1 > 0.0031308 ) + B1 = 1.055 * ( pow( B1, 1./2.4 ) ) - 0.055; + else + B1 = 12.92 * B1; + + + Color rgb; + rgb.R= R1*100; + rgb.G= G1*100; + rgb.B= B1*100; + return rgb; + +} + int opencv_lab2rgb(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img,dst; + float L,a,b; + int *piAddrNew = NULL; + //checking output argument + CheckOutputArgument(pvApiCtx,1,1); + //checking if number of input arguments are 3 + + int k=0; + // SciErr getMatrixOfDoubleInList(void* _pvCtx, int* _piParent, int _iItemPos, int* _piRows, int* _piCols, double** _pdblReal) + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int m=0; + double *r1,*g1,*b1; + r1=(double *)malloc(sizeof(double)*iRows*iCols); + g1=(double *)malloc(sizeof(double)*iRows*iCols); + b1=(double *)malloc(sizeof(double)*iRows*iCols); + for(int i=0;i(i,j); + L= (pstDataR[k]); + a= (pstDataG[k]); + b= (pstDataB[k++]); + out=lab2rgb1(L,a,b); + //cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + struct Color + { + float R,G,B,L,a,b; + }; + Color lab2uint16(float L, float a, float b) + { + /* lab to uint16 conversion */ + float R1=round((65535 * L) / (100 + (25500/65280))); + float G1=round(65535 * ((128 + a) / (255 + (255/256)))); + float B1=round(65535 * ((128 + b) / (255 + (255/256)))); + if((R1<0)||(R1>65535)) + R1=65535; + if((G1<0)||(G1>65535)) + G1=65535; + if((B1<0)||(B1>65535)) + B1=65535; + Color rgb; + rgb.R= (R1-142); + rgb.G = (G1-142); + rgb.B = (B1-142); + return rgb; + } + int opencv_lab2uint16(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int iRows= 0; + int iCols= 0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + int i,j; + int error; + Color out; + float L,a,b; + int *piAddrNew = NULL; + CheckOutputArgument(pvApiCtx,1,1); + int k=0; + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int m=0; + unsigned short *r1,*g1,*b1; + r1=(unsigned short *)malloc(sizeof(unsigned short )*iRows*iCols); + g1=(unsigned short *)malloc(sizeof(unsigned short )*iRows*iCols); + b1=(unsigned short *)malloc(sizeof(unsigned short )*iRows*iCols); + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + +struct Color +{ + float R,G,B,X,Y,Z,L,a,b; +}; + + +Color lab2uint8(float L, float a, float b) +{ + float R1=round(((255*L)/100)); + float G1=round(a+128); + float B1=round(b+128); + Color rgb; + rgb.R= R1; + rgb.G = G1; + rgb.B = B1; + return rgb; + +} + + int opencv_lab2uint8(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img,dst; + float L,a,b; + int *piAddrNew = NULL; + //checking output argument + CheckOutputArgument(pvApiCtx,1,1); + //checking if number of input arguments are 3 + + int k=0; + // SciErr getMatrixOfDoubleInList(void* _pvCtx, int* _piParent, int _iItemPos, int* _piRows, int* _piCols, double** _pdblReal) + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int m=0; + double *r1,*g1,*b1; + r1=(double *)malloc(sizeof(double)*iRows*iCols); + g1=(double *)malloc(sizeof(double)*iRows*iCols); + b1=(double *)malloc(sizeof(double)*iRows*iCols); + for(int i=0;i(i,j); + L= (pstDataR[k]); + a= (pstDataG[k]); + b= (pstDataB[k++]); + out=lab2uint8(L,a,b); + r1[m]=(out.R); + g1[m]=(out.G); + b1[m++]=(out.B); + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Adding the R value matrix to the list + //Syntax : createMatrixOfInteger32InList(void* _pvCtx, int _iVar, int* _piParent, int _iItemPos, int _iRows, int _iCols, const int* _piData) + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 1, iRows,iCols, r1); + free(r1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 2, iRows, iCols, g1); + free(g1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 3, iRows, iCols, b1); + free(b1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} +} + + diff --git a/sci_gateway/cpp/opencv_lab2xyz.cpp b/sci_gateway/cpp/opencv_lab2xyz.cpp new file mode 100644 index 0000000..dc5b97a --- /dev/null +++ b/sci_gateway/cpp/opencv_lab2xyz.cpp @@ -0,0 +1,393 @@ +/*-------------------------------------- + function-lab2xyz + Author :Tess Zacharias +------------------------------------- */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; + struct Color + { + float X,Y,Z,L,a,b; + }; + Color lab2xyz1(float L, float a, float b) +{ + float y = (L + 16) / 116; + float x = a / 500 + y; + float z = y - b / 200; + + if ( pow( y , 3 ) > 0.008856 ) { y = pow( y , 3 ); } + else { y = ( y - 16 / 116 ) / 7.787; } + if ( pow( x , 3 ) > 0.008856 ) { x = pow( x , 3 ); } + else { x = ( x - 16 / 116 ) / 7.787; } + if ( pow( z , 3 ) > 0.008856 ) { z = pow( z , 3 ); } + else { z = ( z - 16 / 116 ) / 7.787; } + + Color xyz; + xyz.X= x * REF_X; + xyz.Y = y * REF_Y; + xyz.Z = z * REF_Z; + return xyz; + + +} +Color lab2xyz2(float L, float a, float b,char *s) +{ + double REF_X1; + double REF_Y1; + double REF_Z1; + if(!strcasecmp(s,"a")) + { + REF_X1=1.0985; + REF_Y1= 1.0000; + REF_Z1=0.3558; + } + else if(!strcasecmp(s,"c")) + { + REF_X1=0.9807; + REF_Y1= 1.0000; + REF_Z1=1.1822; + } + else if(!strcasecmp(s,"d50")) + { + REF_X1=0.9642; + REF_Y1= 1.0000; + REF_Z1=0.8251; + } + else if(!strcasecmp(s,"d65")) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(!strcasecmp(s,"icc")) + { + REF_X1=0.962; + REF_Y1=1.000; + REF_Z1= 0.8249; + } + else if(!strcasecmp(s,"d55")) + { + REF_X1=0.962; + REF_Y1=1.000; + REF_Z1= 0.8249; + } + float y = (L + 16) / 116; + float x = a / 500 + y; + float z = y - b / 200; + + if ( pow( y , 3 ) > 0.008856 ) { y = pow( y , 3 ); } + else { y = ( y - 16 / 116 ) / 7.787; } + if ( pow( x , 3 ) > 0.008856 ) { x = pow( x , 3 ); } + else { x = ( x - 16 / 116 ) / 7.787; } + if ( pow( z , 3 ) > 0.008856 ) { z = pow( z , 3 ); } + else { z = ( z - 16 / 116 ) / 7.787; } + + Color xyz; + xyz.X= x * REF_X1; + xyz.Y = y * REF_Y1; + xyz.Z = z * REF_Z1; + return xyz; +} +Color lab2xyz3(float L, float a, float b,char *s) +{ + double REF_X1; + double REF_Y1; + double REF_Z1; + if(!strcasecmp(s,"sRGB")) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(!strcasecmp(s,"adobe-rgb-1998")) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + float y = (L + 16) / 116; + float x = a / 500 + y; + float z = y - b / 200; + + if ( pow( y , 3 ) > 0.008856 ) { y = pow( y , 3 ); } + else { y = ( y - 16 / 116 ) / 7.787; } + if ( pow( x , 3 ) > 0.008856 ) { x = pow( x , 3 ); } + else { x = ( x - 16 / 116 ) / 7.787; } + if ( pow( z , 3 ) > 0.008856 ) { z = pow( z , 3 ); } + else { z = ( z - 16 / 116 ) / 7.787; } + + Color xyz; + xyz.X= x * REF_X1; + xyz.Y = y * REF_Y1; + xyz.Z = z * REF_Z1; + return xyz; +} +int opencv_lab2xyz(char *fname, unsigned long fname_len) +{ + SciErr sciErr; + int* piAddr1=NULL; + int* piAddr2=NULL; + int* piAddr3=NULL; + int* piAddrNew=NULL; + int iRows = 0; + int iCols = 0; + char* pstData = NULL; + char* pstData1 = NULL; + CheckInputArgument(pvApiCtx,1,3); + CheckOutputArgument(pvApiCtx,1,1); + int m=0; + int iType = 0; + double *value=NULL; + int iPrec= 0; + Mat img,k; + int iRet = 0; + int iRet1 = 0; + Color out; + float L,a,b; + sciErr =getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + sciErr = getVarType(pvApiCtx, piAddr1, &iType); + if(nbInputArgument(pvApiCtx)==3) + { + /*retrieve second argument */ + sciErr = getVarAddressFromPosition(pvApiCtx, 2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getAllocatedSingleString(pvApiCtx, piAddr2, &pstData); + } + } + else + { + sciprint(" The second argument should be string "); + return 0; + } + if((strcasecmp(pstData,"whitepoint")!=0)&&(strcasecmp(pstData,"Colorspace")!=0)) + { + sciprint("\n Second argument should be any of these string :'whitepoint','Colorspace'"); + return 0; + } + /*retrieve third argument */ + sciErr = getVarAddressFromPosition(pvApiCtx, 3,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr3)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet1 = getAllocatedSingleString(pvApiCtx, piAddr3, &pstData1); + } + } + else + { + sciprint(" The third argument should be string "); + return 0; + } + if((strcasecmp(pstData,"whitepoint")==0)) + { + if((strcasecmp(pstData1,"d65")!=0)&&(strcasecmp(pstData1,"d50")!=0)&&(strcasecmp(pstData1,"d55")!=0)&&(strcasecmp(pstData1,"icc")!=0)&&(strcasecmp(pstData1,"icc")!=0)&&(strcasecmp(pstData1,"a")!=0)&&(strcasecmp(pstData1,"c")!=0)) + { + sciprint("\nIf Second argument is 'whitepoint' then Third argument should be any of these string :'a','d65','d55','d50','icc','c'"); + return 0; + } + } + if((strcasecmp(pstData,"Colorspace")==0)) + { + if((strcasecmp(pstData1,"sRGB")!=0)&&(strcasecmp(pstData1,"adobe-rgb-1998")!=0)) + { + sciprint("\nIf Second argument is 'Colorspace' then Third argument should be any of these string :'sRGB','adobe-rgb-1998'"); + return 0; + } + } + } + switch(iType) + { + case sci_matrix: + { + /* retrieve matrix values */ + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols,&value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + double *output=NULL; + output=( double *)malloc(sizeof(double)*iRows*iCols); + int j=0; + if(nbInputArgument(pvApiCtx)==1) + { + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_laplacian(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **ddepth = NULL; + int i,j,k; + double ksize,scale,delta; + + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of ksize + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&ksize); + if(intErr) + return intErr; + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&scale); + if(intErr) + return intErr; + + //for value of delta + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&delta); + if(intErr) + return intErr; + + + Mat new_image(image.rows,image.cols,CV_8UC3); + if((int)ksize!=1 && (int)ksize!=3 && (int)ksize!=5 && (int)ksize!=7) + { + sciprint("Value of 1,3,5,7 Required for ksize. Default value of 3 was used instead"); + ksize=3; + } + + if(strcmp(ddepth[0],"CV_8U")==0) + Laplacian(image,new_image,CV_8U,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_16U")==0) + Laplacian(image,new_image,CV_16U,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_16S")==0) + Laplacian(image,new_image,CV_16S,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_32F")==0) + Laplacian(image,new_image,CV_32F,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_64F")==0) + Laplacian(image,new_image,CV_64F,ksize,scale,delta); + else + { + Laplacian(image,new_image,CV_8U,ksize,scale,delta); + sciprint("Wrong depth given,using CV_8U instead"); + } + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_line.cpp b/sci_gateway/cpp/opencv_line.cpp new file mode 100644 index 0000000..c047381 --- /dev/null +++ b/sci_gateway/cpp/opencv_line.cpp @@ -0,0 +1,211 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + return_image = line(image , x1 , y1 , x2,y2,r_value,g_value,b_value,thickness,linetype,shift); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_line(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11=NULL; + int i,j,k; + double thickness=1,linetype=8,shift=0 ,x1,y1,x2,y2,r_value,g_value,b_value; + + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 11); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&x1); + if(intErr) + return intErr; + + //for value of y coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&y1); + if(intErr) + return intErr; + + //for value of x coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4 ,&x2); + if(intErr) + return intErr; + + ///for value of y coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&y2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&r_value); + if(intErr) + return intErr; + + // for G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7 ,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&b_value); + if(intErr) + return intErr; + + // for thickness of line default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr9,&thickness); + if(intErr) + return intErr; + + //for line type of line default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr10 ,&linetype); + if(intErr) + return intErr; + + // for shift in line defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Syntax: sciErr = getMatrixOfInteger8(pvApiCtx, piAddr8, &iRows8, &iCols8, &pcData); + intErr = getScalarDouble(pvApiCtx, piAddr11 ,&shift); + if(intErr) + return intErr; + + + //temporary pt variable, to use in function as centre + Point pt1(x1,y1); + Point pt2(x2,y2); + /// checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead"); + } + +//calling the opencv function + line( src, pt1, pt2, Scalar(b_value,g_value,r_value), thickness, linetype, shift); + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway/cpp/opencv_makecform.cpp b/sci_gateway/cpp/opencv_makecform.cpp new file mode 100644 index 0000000..c1d8481 --- /dev/null +++ b/sci_gateway/cpp/opencv_makecform.cpp @@ -0,0 +1,99 @@ +/******************************************************** +Function :makecform +Syntax :B=makecform(string) +*string : 'xyz2lab' 'lab2xyz' 'srgb2xyz' + 'xyz2srgb' 'srgb2lab' 'lab2srgb' +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_makecform(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int* piAddr1=NULL; + int error; + int iRet = 0; + char* pstData = NULL; + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + /*retrieve first argument */ + sciErr = getVarAddressFromPosition(pvApiCtx, 1,&piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr1)) + { + if(isScalar(pvApiCtx, piAddr1)) + { + + iRet = getAllocatedSingleString(pvApiCtx, piAddr1, &pstData); + } + } + int k=0; + char** output=NULL; + output=( char **)malloc(sizeof(char*)); + if(strcasecmp(pstData,"xyz2lab")==0) + { + output[0]="xyz2lab"; + sciprint(" \nc_func: @xyz2lab \nColorSpace_in: 'xyz' \nColorSpace_out: 'lab'\nencoding: 'double'\ncdata: [1x1 struct]"); + } + else if(strcasecmp(pstData,"lab2xyz")==0) + { + output[0]="lab2xyz"; + sciprint("\nc_func: @lab2xyz\nColorSpace_in: 'lab'\nColorSpace_out: 'xyz'\nencoding: 'double' \ncdata: [1x1 struct]"); + } + else if(strcasecmp(pstData,"srgb2xyz")==0) + { + output[0]="srgb2xyz"; + sciprint(" \nc_func: @applycformsequence \nColorSpace_in: 'rgb'\nColorSpace_out: 'xyz' \nencoding: 'double'\ncdata: [1x1 struct]"); + } + else if(strcasecmp(pstData,"xyz2srgb")==0) + { + output[0]="xyz2srgb"; + sciprint("\nc_func: @applycformsequence\nColorSpace_in: 'xyz'\nColorSpace_out: 'rgb'\nencoding: 'double' \ncdata: [1x1 struct]"); + } + else if(strcasecmp(pstData,"srgb2lab")==0) + { + output[0]="srgb2lab"; + sciprint("\nc_func: @applycformsequence\nColorSpace_in: 'rgb'\nColorSpace_out: 'lab'\nencoding: 'double'\ncdata: [1x1 struct]"); + } + else if(strcasecmp(pstData,"lab2srgb")==0) + { + output[0]="lab2srgb"; + sciprint("\nc_func: @applycformsequence\nColorSpace_in: 'lab'\nColorSpace_out: 'rgb' \nencoding: 'double'\ncdata: [1x1 struct]"); + } + else + { + sciprint("Expected input argument 'xyz2lab' 'lab2xyz' 'srgb2xyz' 'xyz2srgb' 'srgb2lab' 'lab2srgb'"); + return 0; + } + sciErr = createMatrixOfString(pvApiCtx, nbInputArgument(pvApiCtx) + 1,1,1,output); + free(output); // Data have been copied into Scilab memory + if (sciErr.iErr) + { + //Make sure everything is cleanup in case of error + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} +} diff --git a/sci_gateway/cpp/opencv_mean1.cpp b/sci_gateway/cpp/opencv_mean1.cpp new file mode 100644 index 0000000..4b85513 --- /dev/null +++ b/sci_gateway/cpp/opencv_mean1.cpp @@ -0,0 +1,64 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : return_mean=mean(sourceimage) */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include +#include "../common.h" +int mean1(char *fname, unsigned long fname_len) +{ + + // Error management variable + SciErr sciErr; + // Variables declaration // + int t,i,j,k; + Scalar mean11; + //function to check number of input variable + CheckInputArgument(pvApiCtx, 1, 1); + //function to check number of output variable + CheckOutputArgument(pvApiCtx, 1, 1); + // get Address of first input + Mat image; + retrieveImage(image,1); + //block of code for calculating meanimage + mean11=mean(image,noArray()); + double m=0; + int count=0; + for(int i=0;i<4;i++) + { + if(mean11.val[i]!=0) + { + m=m+mean11.val[i]; + count++; + } + + } + m=m/count; + int error; + error=createScalarDouble(pvApiCtx,nbInputArgument(pvApiCtx)+1,m); + if(error!=0) + { + sciprint("error occurred"); + return 0; + } + ////////// Return the output arguments to the Scilab engine ////////// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_medianblur.cpp b/sci_gateway/cpp/opencv_medianblur.cpp new file mode 100644 index 0000000..8886af5 --- /dev/null +++ b/sci_gateway/cpp/opencv_medianblur.cpp @@ -0,0 +1,81 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_medianblur(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double ksize; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + //for value of ksize + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &ksize); + if(intErr) + { + return intErr; + } + + Mat new_image(image.rows,image.cols,CV_8UC3); + + //checking input parameters + if((int)ksize != 1 && (int)ksize != 3 && (int)ksize != 5 && (int)ksize != 7) + { + sciprint("Value of 1,3,5,7 Required for ksize. Default value of 3 was used instead"); + ksize = 3; + } + medianBlur(image,new_image,ksize); + + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_minAreaRect.cpp b/sci_gateway/cpp/opencv_minAreaRect.cpp new file mode 100644 index 0000000..ab066b5 --- /dev/null +++ b/sci_gateway/cpp/opencv_minAreaRect.cpp @@ -0,0 +1,125 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : return_rotatedrect=minAreaRect(InputArray points); +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + int minAreaRect(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double* pstData = NULL; + int* pstData2 = NULL; + double *rrows; + double *rcols; + double sigmaSpace; + int error; + ////checking input argument + CheckInputArgument(pvApiCtx,1,1); + //checking output argument + CheckOutputArgument(pvApiCtx,5,5); + //for first argument + // get Address of first input + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + //check for any error + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrieve input array + //SciErr getMatrixOfDouble(void* _pvCtx, int* _piAddress, int* _piRows, int* _piCols, double** _pdblReal) + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols,&pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int k=0; + vector contours; + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int minimumenclosingcirlce(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double *pstData = NULL; + char **pstData2 = NULL; + double *rrows; + double *rcols; + Point2f center; + float radius; + int error; + //checking input argument + CheckInputArgument(pvApiCtx,1,1); + //checking output argument + CheckOutputArgument(pvApiCtx,3,3); + //for first argument + // get Address of first input + sciErr =getVarAddressFromPosition(pvApiCtx,1,&piAddr); + //check for any error + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrieve input array + //SciErr getMatrixOfDouble(void* _pvCtx, int* _piAddress, int* _piRows, int* _piCols, double** _pdblReal) + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols,&pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int k=0; + vector contours; + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + +int opencv_montage(char *fname, unsigned long fname_len) +{ + SciErr sciErr; + int iLen = 0; + //variable info + int iRows = 0; + int iCols = 0; + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddrChild = NULL; + int *piAddrNew = NULL; + int piRows = 0; + int piCols = 0; + int *piLen = NULL; + char **pstData = NULL; + int **pstData1 = NULL; + int i,j,k=0,iItem=0,height,width,intErr=0; + double new_rows,new_cols; + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + vector image; + Mat tempimage; + + + //Checking number of input and output arguments (enviromnet variable, min arguments, max arguments) + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int x=0;x(i,j)[2]=pstDataR[k]; + tempimage.at(i,j)[1]=pstDataG[k]; + tempimage.at(i,j)[0]=pstDataB[k++]; + } + } + image.push_back(tempimage); + } + + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &new_rows); + if(intErr) + return intErr; + + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &new_cols); + if(intErr) + return intErr; + + for(i=1;i= iItem) || ((new_rows*new_cols) < iItem) ) + { + sciprint("Given Row and Column Dimensions do not match with number of images in list. Montage cannot be created. Please check arguments\n"); + return 0; + } + height=new_rows*image[0].rows; + width=new_cols*image[0].cols; + + Mat new_image = Mat::zeros(height,width,CV_8UC3); + + k=0; + for(int x=0;x=iItem) + { + new_image.at(image[0].rows*x+i,image[0].cols*y+j)[2]=black_image.at(i,j)[2]; + new_image.at(image[0].rows*x+i,image[0].cols*y+j)[1]=black_image.at(i,j)[1]; + new_image.at(image[0].rows*x+i,image[0].cols*y+j)[0]=black_image.at(i,j)[0]; + }*/ + if(k(image[k].rows*x+i,image[k].cols*y+j)[2]=image[k].at(i,j)[2]; + new_image.at(image[k].rows*x+i,image[k].cols*y+j)[1]=image[k].at(i,j)[1]; + new_image.at(image[k].rows*x+i,image[k].cols*y+j)[0]=image[k].at(i,j)[0]; + } + } + } + k++; + } + } + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_morphologyEx.cpp b/sci_gateway/cpp/opencv_morphologyEx.cpp new file mode 100644 index 0000000..cc1666a --- /dev/null +++ b/sci_gateway/cpp/opencv_morphologyEx.cpp @@ -0,0 +1,232 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = morphologyEx(input_image,"",kernel_matrix,anchor_x,anchor_y,iterations,""); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_morphologyEx(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **op = NULL; + char **borderType = NULL; + int i,j,k; + double *kernel,anchorX,anchorY,iterations,border; + + //checking input argument + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for op + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + op = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + op[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, op); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for kernel matrix + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&kernel); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n=iRows; + double kernelArray[n][n]; + //assigning values to actual kernelMatrix + for(i=0;i= n || anchorY >=n) + { + sciprint("Invalid anchor point given. Centre point (-1,-1) was used instead"); + anchorY = -1; + anchorX = -1; + } + Mat new_image(image.rows,image.cols,image.type()); + Point pt(anchorX,anchorY); + + int actualop; + + if(strcmp(op[0],"MORPH_OPEN")==0) + actualop=MORPH_OPEN; + else if(strcmp(op[0],"MORPH_CLOSE")==0) + actualop=MORPH_CLOSE; + else if(strcmp(op[0],"MORPH_GRADIENT")==0) + actualop=MORPH_GRADIENT; + else if(strcmp(op[0],"MORPH_TOPHAT")==0) + actualop=MORPH_TOPHAT; + else if(strcmp(op[0],"MORPH_BLACKHAT")==0) + actualop=MORPH_BLACKHAT; + else + { + sciprint("Invalid type %s used. MORPH_OPEN was used instead by default",op[0]); + actualop = MORPH_OPEN; + } + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + //applying function + filter2D(image,new_image,actualop,kernelMatrix,pt,iterations,border); + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_ntsc2rgb.cpp b/sci_gateway/cpp/opencv_ntsc2rgb.cpp new file mode 100644 index 0000000..9857c63 --- /dev/null +++ b/sci_gateway/cpp/opencv_ntsc2rgb.cpp @@ -0,0 +1,192 @@ +/* ==================================================================== */ +/* Author :Tess Zacharias */ +/* ==================================================================== */ +/* overloaded function */ +/* Syntax : return_matrix_3_xyz_values=lab2xyz(matrix_of_3_labvalues a) */ +/* Syntax : return_matrix_3_xyz_values=lab2xyz(matrix_of_3_labvalues a,String1 Name,String2 value) */ +/* ==================================================================== */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + // #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; + +struct Color +{ + float R,G,B,Y,I,Q; +}; + + +Color ntsc2rgb1(float Y, float I, float Q) +{ + + // float y=Y/255.0; + // float i=I/255.0; + // float q=Q/255.0; + float r= 1.000*Y+0.956*I+0.621*Q; + float g =1.000*Y-0.272*I-0.647*Q; + float b =1.000*Y-1.106*I+1.703*Q; + + + Color rgb; + rgb.R= r; + rgb.G= g; + rgb.B= b; + return rgb; + +} + int opencv_ntsc2rgb(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img,dst; + float Y,I,Q; + int *piAddrNew = NULL; + //checking output argument + CheckOutputArgument(pvApiCtx,1,1); + //checking if number of input arguments are 3 + + int k=0; + // SciErr getMatrixOfDoubleInList(void* _pvCtx, int* _piParent, int _iItemPos, int* _piRows, int* _piCols, double** _pdblReal) + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int m=0; + double *r,*g,*b; + r=(double *)malloc(sizeof(double)*iRows*iCols); + g=(double *)malloc(sizeof(double)*iRows*iCols); + b=(double *)malloc(sizeof(double)*iRows*iCols); + for(int i=0;i(i,j); + Y= (pstDataR[k]); + I= (pstDataG[k]); + Q= (pstDataB[k++]); + out=ntsc2rgb1(Y,I,Q); + // cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_puttext(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piLen2 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11 = NULL; + int *piAddr12 = NULL; + int i,j,k , bottom , fontFace; + char **fontface = NULL,**text= NULL; + double thickness=1 ,linetype=8,bottomleft ; + double x1,y1,fontscale,r_value,g_value,b_value; + bool bottomLeftOrigin; + + //checking input argument + CheckInputArgument(pvApiCtx, 9, 12); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of point of bootom-left corner of the text string + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,&x1); + if(intErr) + return intErr; + + //for value of y coordinate of point of bootom-left corner of the text string + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&y1); + if(intErr) + return intErr; + + //for value of fontscale + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&fontscale); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&r_value); + if(intErr) + return intErr; + + //for value of G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&g_value); + if(intErr) + return intErr; + + //for value of Bvalue of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7 ,&b_value); + if(intErr) + return intErr; + + /// for text to be written on the image + //Syntax : SciErr getMatrixOfString(void* _pvCtx, int* _piAddress, int* _piRows, int* _piCols, int* _piLength, char** _pstStrings) + //fisrt call to retrieve dimensions + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr8, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr8, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + text = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + text[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr8, &iRows, &iCols, piLen, text); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ///for the fontface of text + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr9, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen2 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr9, &iRows, &iCols, piLen2, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + fontface = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + fontface[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr9, &iRows, &iCols, piLen2, fontface); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for thickness of text + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr10,&thickness); + if(intErr) + return intErr; + + //for line type of text default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr11,&linetype); + if(intErr) + return intErr; + + // for shift in line defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,12,&piAddr12); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr12,&bottomleft); + if(intErr) + return intErr; + + + if (bottomleft == 0) + bottomLeftOrigin = false; + else if (bottomleft == 1) + bottomLeftOrigin = true; + else + { + bottomLeftOrigin = false; + sciprint("Only 0 and 1 acceptable , taking false instead"); + } + /// setting fontface FONT_HERSHEY_SIMPLEX, FONT_HERSHEY_PLAIN, FONT_HERSHEY_DUPLEX, FONT_HERSHEY_COMPLEX, FONT_HERSHEY_TRIPLEX, FONT_HERSHEY_COMPLEX_SMALL, + // FONT_HERSHEY_SCRIPT_SIMPLEX, or FONT_HERSHEY_SCRIPT_COMPLEX, + if(strcmp(fontface[0],"FONT_HERSHEY_SIMPLEX")==0) + fontFace = FONT_HERSHEY_SIMPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_PLAIN")==0) + fontFace= FONT_HERSHEY_PLAIN; + else if (strcmp(fontface[0],"FONT_HERSHEY_DUPLEX")==0) + fontFace= FONT_HERSHEY_DUPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_COMPLEX")==0) + fontFace= FONT_HERSHEY_COMPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_TRIPLEX")==0) + fontFace = FONT_HERSHEY_TRIPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_COMPLEX_SMALL")==0) + fontFace = FONT_HERSHEY_COMPLEX_SMALL; + else if (strcmp(fontface[0],"FONT_HERSHEY_SCRIPT_SIMPLEX")==0) + fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_SCRIPT_COMPLEX")==0) + fontFace = FONT_HERSHEY_SCRIPT_COMPLEX; + else + { + sciprint("Invalid fontface given. FONT_HERSHEY_PLAIN was used instead"); + fontFace = FONT_HERSHEY_PLAIN; + } + //temporary pt variable, to use in function as bottom left corner + Point pt1(x1,y1); + /// checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead"); + } + + + + //syntax: putText(Mat& img, const string& text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=8, bool bottomLeftOrigin=false ) + putText( src, text[0], pt1, fontFace, fontscale, Scalar(b_value,g_value,r_value), thickness, linetype, bottomLeftOrigin ); + + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway/cpp/opencv_pyrDown.cpp b/sci_gateway/cpp/opencv_pyrDown.cpp new file mode 100644 index 0000000..7a8df72 --- /dev/null +++ b/sci_gateway/cpp/opencv_pyrDown.cpp @@ -0,0 +1,156 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** + return_image = pyrDown(input_image, dstsize_width, distsize_height, border; +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_pyrDown(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + char **borderType = NULL; + double dstsize_width,dstsize_height; + int border; + int i; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of dstsize_width + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &dstsize_width); + if(intErr) + return intErr; + + //for value of dstsize_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&dstsize_height); + if(intErr) + return intErr; + + //for border + sciErr = getVarAddressFromPosition(pvApiCtx,4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //error checks + if(dstsize_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead\n"); + dstsize_height=1; + } + if(dstsize_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead\n"); + dstsize_width=1; + } + if((dstsize_width != image.cols*2) && (dstsize_width != image.cols*2+1) && (dstsize_width != image.cols*2-1)) + { + sciprint("Incorrect dstsize_width value was used. src.cols*2+1 value was calculated and used instead\n"); + dstsize_width = image.cols*2+1; + } + if((dstsize_height != image.rows*2) && (dstsize_height != image.rows*2+1) && (dstsize_height != image.rows*2-1)) + { + sciprint("Incorrect dstsize_height value was used. src.rows*2+1 value was calculated and used instead\n"); + dstsize_height = image.rows*2+1; + } + + Mat new_image(dstsize_height,dstsize_width,image.type()); + + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + pyrDown(image,new_image,Size(dstsize_width,dstsize_height),border); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_pyrMeanShiftFiltering.cpp b/sci_gateway/cpp/opencv_pyrMeanShiftFiltering.cpp new file mode 100644 index 0000000..7070083 --- /dev/null +++ b/sci_gateway/cpp/opencv_pyrMeanShiftFiltering.cpp @@ -0,0 +1,91 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax :return_image=pyrMeanShiftFiltering(InputArray src, double sp, double sr); */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int pyrMeanShiftFiltering(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double* pstData = NULL; + int* pstData2 = NULL; + double *rrows; + double *rcols; + double sp; + double sr; + int error; + TermCriteria termcrit=TermCriteria( TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) ; + + // checking input argument + CheckInputArgument(pvApiCtx,3,3); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + Mat img; + retrieveImage(img,1); + //second argument + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + error=getScalarDouble(pvApiCtx,piAddr2,&sp); + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //third argument + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + error=getScalarDouble(pvApiCtx,piAddr3,&sr); + if(error!=0) + { + sciprint("error in retrieving second argument"); + } + //creating variable of type Mat + Mat dst; + //open cv function + pyrMeanShiftFiltering(img,dst, sp,sr,1,termcrit); + string tempstring = type2str(dst.type()); + char* checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + + } +} diff --git a/sci_gateway/cpp/opencv_pyrUp.cpp b/sci_gateway/cpp/opencv_pyrUp.cpp new file mode 100644 index 0000000..18459c3 --- /dev/null +++ b/sci_gateway/cpp/opencv_pyrUp.cpp @@ -0,0 +1,156 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** + return_image = pyrUp(input_image, dstsize_width, distsize_height, border; +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_pyrUp(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + char **borderType = NULL; + double dstsize_width,dstsize_height; + int border; + int i; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of dstsize_width + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &dstsize_width); + if(intErr) + return intErr; + + //for value of dstsize_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&dstsize_height); + if(intErr) + return intErr; + + sciErr = getVarAddressFromPosition(pvApiCtx,4, &piAddr4); + + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //error checks + if(dstsize_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead\n"); + dstsize_height=1; + } + if(dstsize_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead\n"); + dstsize_width=1; + } + if((dstsize_width != image.cols*2) && (dstsize_width != image.cols*2+1) && (dstsize_width != image.cols*2-1)) + { + sciprint("Incorrect dstsize_width value was used. src.cols*2+1 value was calculated and used instead\n"); + dstsize_width = image.cols*2+1; + } + if((dstsize_height != image.rows*2) && (dstsize_height != image.rows*2+1) && (dstsize_height != image.rows*2-1)) + { + sciprint("Incorrect dstsize_height value was used. src.rows*2+1 value was calculated and used instead\n"); + dstsize_height = image.rows*2+1; + } + + Mat new_image(dstsize_height,dstsize_width,image.type()); + + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + pyrUp(image,new_image,Size(dstsize_width,dstsize_height),border); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_rectangle.cpp b/sci_gateway/cpp/opencv_rectangle.cpp new file mode 100644 index 0000000..36d24f0 --- /dev/null +++ b/sci_gateway/cpp/opencv_rectangle.cpp @@ -0,0 +1,207 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + return_image = circle(input_image , x_coordinate_of_point1 , y_coordinate_point1, x_coordinate_of_point2 , y_coordinate_point2 , r_value,g_value,b_value,thickness,linetype,shift); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_rectangle(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11 = NULL; + int i,j,k; + double thickness=1 , linetype=8,shift =0 ,point1_x ,point1_y,point2_x ,point2_y,r_value,g_value,b_value; + + + //checking input argument + CheckInputArgument(pvApiCtx, 11, 11); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of point1 + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,&point1_x); + if(intErr) + return intErr; + + + //for value of y coordinate of point1 + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&point1_y); + if(intErr) + return intErr; + + //for value of x coordinate of point2 + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&point2_x); + if(intErr) + return intErr; + + + //for value of y coordinate of point2 + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&point2_y); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&r_value); + if(intErr) + return intErr; + + //for value of G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7 ,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&b_value); + if(intErr) + return intErr; + + // for thickness of circle default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr9,&thickness); + if(intErr) + return intErr; + + //for line type of circle default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr10,&linetype); + if(intErr) + return intErr; + + // for shift in circle defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr11,&shift); + if(intErr) + return intErr; + + // checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead\n"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead\n"); + } + + // Draw the circles detected + rectangle( src, Point(point1_x,point1_y), Point(point2_x,point2_y), Scalar(r_value,g_value,b_value), thickness, linetype, shift); + +//// sending the image + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway/cpp/opencv_regionfill.cpp b/sci_gateway/cpp/opencv_regionfill.cpp new file mode 100644 index 0000000..85d89b3 --- /dev/null +++ b/sci_gateway/cpp/opencv_regionfill.cpp @@ -0,0 +1,180 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = regionfill(input_image, column_list, row_list) + return_image = regionfill(input_image, mask) +Example: + im = regionfill(image, [0 100 100 0], [0 0 100 100]) + im = regionfill(image, mask) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_regionfill(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRowsR=0,iColsR=0,iColsC=0,iRowsC=0; + int *piAddr = NULL; + int *piAddr2 = NULL; + double *pstDataR = NULL; + double *pstDataC = NULL; + int i, number_of_points; + int lineType = 8; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 3); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get the input image from the Scilab environment + Mat image, mask, mask_orig, fin_image; + retrieveImage(image, 1); + + + if (nbInputArgument(pvApiCtx) == 2) + { + retrieveImage(mask_orig, 2); + + for (int i = 0; i < mask_orig.cols; i++) + { + for (int j = 0; j < mask_orig.rows; j++) + { + unsigned char val = mask_orig.at(i,j); + if (!(val == 0 || val == 1 || val == 255)) + { + sciprint("Please enter binary mask (second argument)."); + return 0; + } + } + } + + if (mask_orig.type() != CV_8UC1) + { + cvtColor(mask_orig, mask, CV_BGR2GRAY); + } + else + { + mask = mask_orig.clone(); + } + } + else + { + // Get the address of 2nd argument, the column list + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the address of the 3rd agument, the row list + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the column list in the form of a matrix + // No. of columns = No. of elements in the list + // No. of rows = 1 + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRowsC, &iColsC, &pstDataC); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iRowsC != 1) + { + sciprint("Please enter a list of column coordinates.\n"); + return 0; + } + + // Get the row list in the form of a matrix + // No. of columns = No. of elements in the list + // No. of rows = 1 + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRowsR, &iColsR, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iRowsR != 1) + { + sciprint("Please enter a list of row coordinates.\n"); + return 0; + } + + if (iColsC != iColsR) + { + sciprint("Please ensure number of elements in both row and column lists are equal.\n"); + return 0; + } + + // Number of points is number of columns + number_of_points = iColsC; + + // Create the n points which define + // the polygon + Point points[1][number_of_points]; + + for (i = 0; i < number_of_points; i++) + { + if (pstDataR[i] < 0 || pstDataC[i] < 0) + { + sciprint("Coordinates cannot be negative.\n"); + return 0; + } + else + points[0][i] = Point(pstDataR[i], pstDataC[i]); + } + + const Point* ppt[1] = { points[0] }; + + int npt[] = { number_of_points }; + + // Create a new, black, blank image same size as of input + mask_orig = Mat::zeros(image.size(), image.type()); + cvtColor(mask_orig, mask, CV_BGR2GRAY); + + // Call the fillPoly OpenCV function + // Fill the blank image in the polygon specified + // by the points + fillPoly(mask, ppt, npt, 1, Scalar(255, 255, 255), lineType); + } + + inpaint(image, mask, fin_image, 3, INPAINT_TELEA); + + string tempstring = type2str(fin_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, fin_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_rgb2lab.cpp b/sci_gateway/cpp/opencv_rgb2lab.cpp new file mode 100644 index 0000000..c031790 --- /dev/null +++ b/sci_gateway/cpp/opencv_rgb2lab.cpp @@ -0,0 +1,244 @@ +/* function-rgb2lab + Author :Tess Zacharias */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; + +struct Color +{ + float R,G,B,X,Y,Z,Ls,as,bs,r,g,b; +}; +Color rgb2lab1(float R, float G, float B) +{ + + float r = R/255.f; //R 0..1 + float g = G/255.f; //G 0..1 + float b = B/255.f; //B 0..1 + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz; + if (r <= 0.04045) + r = r/12.92; + else + r = (float)pow((r+0.055)/1.055,2.4); + if (g <= 0.04045) + g = g/12.92; + else + g = (float)pow((g+0.055)/1.055,2.4); + if (b <= 0.04045) + b = b/12.92; + else + b = (float)pow((b+0.055)/1.055,2.4); + float X = 0.4124*r + 0.3576*g + 0.1805*b; + float Y = 0.2126*r + 0.7152 *g + 0.0722 *b; + float Z = 0.0193*r + 0.1192*g + 0.9505 *b; + float xr = X/REF_X; + float yr = Y/REF_Y; + float zr = Z/REF_Z; + if ( xr > eps ) + fx = (float)pow(xr, 1/3.); + else + fx = (float) ((k * xr + 16.) / 116.); + if ( yr > eps ) + fy = (float)pow(yr, 1/3.); + else + fy = (float) ((k * yr + 16.) / 116.); + if ( zr > eps ) + fz = (float)pow(zr, 1/3.); + else + fz = (float) ((k * zr + 16.) / 116); + Color lab; + lab.Ls= ( 116 * fy ) - 16; + lab.as= 500*(fx-fy); + lab.bs= 200*(fy-fz); + return lab; + +} + Color rgb2lab2(float R, float G, float B,char *s) +{ + double REF_X1; + double REF_Y1; + double REF_Z1; + if(!strcmp(s,"a")) + { + REF_X1=1.0985; + REF_Y1= 1.0000; + REF_Z1=0.3558; + } + else if(!strcmp(s,"c")) + { + REF_X1=0.9807; + REF_Y1= 1.0000; + REF_Z1=1.1822; + } + else if(!strcmp(s,"d50")) + { + REF_X1=0.9642; + REF_Y1= 1.0000; + REF_Z1=0.8251; + } + else if(!strcmp(s,"d65")) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(!strcmp(s,"icc")) + { + REF_X1=0.962; + REF_Y1=1.000; + REF_Z1= 0.8249; + } + else if(!strcmp(s,"e")) + { + REF_X1=1.000; + REF_Y1=1.000; + REF_Z1=1.000; + } + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz,X,Y,Z,xr,yr,zr; + float r = R/255.f; //R 0..1 + float g = G/255.f; //G 0..1 + float b = B/255.f; //B 0..1 + if (r <= 0.04045) + r = r/12.92; + else + r = (float)pow((r+0.055)/1.055,2.4); + if (g <= 0.04045) + g = g/12.92; + else + g = (float)pow((g+0.055)/1.055,2.4); + if (b <= 0.04045) + b = b/12.92; + else + b = (float)pow((b+0.055)/1.055,2.4); + X = 0.4124*r + 0.3576*g + 0.1805*b; + Y = 0.2126*r + 0.7152 *g + 0.0722 *b; + Z = 0.0193*r + 0.1192*g + 0.9505 *b; + xr = X/REF_X1; + yr = Y/REF_Y1; + zr = Z/REF_Z1; + if ( xr > eps ) + fx = (float)pow(xr, 1/3.); + else + fx = (float) ((k * xr + 16.) / 116.); + if ( yr > eps ) + fy = (float)pow(yr, 1/3.); + else + fy = (float) ((k * yr + 16.) / 116.); + if ( zr > eps ) + fz = (float)pow(zr, 1/3.); + else + fz = (float) ((k * zr + 16.) / 116); + Color lab; + lab.Ls= ( 116 * fy ) - 16; + lab.as= 500*(fx-fy); + lab.bs= 200*(fy-fz); + return lab; + +} +int opencv_rgb2lab(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr2=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + int* piaddr=NULL; + char *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img; + float R,G,B; + int *piAddrNew = NULL; + CheckOutputArgument(pvApiCtx,1,1); + int m=0; + retrieveImage(img,1); + double *r1,*g1,*b1; + r1=(double *)malloc(sizeof(double)*img.rows*img.cols); + g1=(double *)malloc(sizeof(double)*img.rows*img.cols); + b1=(double *)malloc(sizeof(double)*img.rows*img.cols); + for(int i=0;i(i,j); + R= intensity.val[2]; + G= intensity.val[1]; + B= intensity.val[0]; + out=rgb2lab1(R,G,B); + r1[m]=out.Ls; + g1[m]=out.as; + b1[m++]=out.bs; + } + } + //creating the list that will be passed to the Scilab enviroment + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Adding the R value matrix to the list + //Syntax : createMatrixOfInteger32InList(void* _pvCtx, int _iVar, int* _piParent, int _iItemPos, int _iRows, int _iCols, const int* _piData) + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 1, img.rows,img.cols, r1); + free(r1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 2, img.rows, img.cols, g1); + free(g1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 3, img.rows, img.cols, b1); + free(b1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} +} diff --git a/sci_gateway/cpp/opencv_rgb2ntsc.cpp b/sci_gateway/cpp/opencv_rgb2ntsc.cpp new file mode 100644 index 0000000..00be9cd --- /dev/null +++ b/sci_gateway/cpp/opencv_rgb2ntsc.cpp @@ -0,0 +1,132 @@ +/* function-rgb2lab + Author :Tess Zacharias */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; +struct Color +{ + float R,G,B,Y,I,Q; +}; +Color rgb2ntsc(float R, float G, float B) +{ + + float r=R/255.0; + float g=G/255.0; + float b=B/255.0; + float y = 0.299*r + 0.587*g + 0.114*b; + float i = 0.596*r -0.274 *g - 0.322 *b; + float q = 0.211*r - 0.523*g + 0.312 *b; + + + Color ntsc; + ntsc.Y= y; + ntsc.I= i; + ntsc.Q= q; + return ntsc; + +} + +int opencv_rgb2ntsc(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr2=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + int* piaddr=NULL; + char *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img; + float R,G,B; + int *piAddrNew = NULL; + CheckOutputArgument(pvApiCtx,1,1); + int m=0; + retrieveImage(img,1); + double *r1,*g1,*b1; + r1=(double *)malloc(sizeof(double)*img.rows*img.cols); + g1=(double *)malloc(sizeof(double)*img.rows*img.cols); + b1=(double *)malloc(sizeof(double)*img.rows*img.cols); + for(int i=0;i(i,j); + R= intensity.val[2]; + G= intensity.val[1]; + B= intensity.val[0]; + out=rgb2ntsc(R,G,B); + r1[m]=out.Y; + g1[m]=out.I; + b1[m++]=out.Q; + } + } + //creating the list that will be passed to the Scilab enviroment + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Adding the R value matrix to the list + //Syntax : createMatrixOfInteger32InList(void* _pvCtx, int _iVar, int* _piParent, int _iItemPos, int _iRows, int _iCols, const int* _piData) + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 1, img.rows,img.cols, r1); + free(r1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 2, img.rows, img.cols, g1); + free(g1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 3, img.rows, img.cols, b1); + free(b1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} +} diff --git a/sci_gateway/cpp/opencv_rgb2xyz.cpp b/sci_gateway/cpp/opencv_rgb2xyz.cpp new file mode 100644 index 0000000..f15f5a4 --- /dev/null +++ b/sci_gateway/cpp/opencv_rgb2xyz.cpp @@ -0,0 +1,220 @@ +/* function-rgb2lab + Author :Tess Zacharias */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; +struct Color +{ + float R,G,B,X,Y,Z,Ls,as,bs,r,g,b; +}; +Color rgb2xyz1(float R, float G, float B) +{ + + float r = R/255.f; //R 0..1 + float g = G/255.f; //G 0..1 + float b = B/255.f; //B 0..1 + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz; + if (r <= 0.04045) + r = r/12.92; + else + r = (float)pow((r+0.055)/1.055,2.4); + if (g <= 0.04045) + g = g/12.92; + else + g = (float)pow((g+0.055)/1.055,2.4); + if (b <= 0.04045) + b = b/12.92; + else + b = (float)pow((b+0.055)/1.055,2.4); + float X1 = 0.4124*r + 0.3576*g + 0.1805*b; + float Y1 = 0.2126*r + 0.7152 *g + 0.0722 *b; + float Z1 = 0.0193*r + 0.1192*g + 0.9505 *b; + float xr = X1; + float yr = Y1; + float zr = Z1; + Color xyz; + xyz.X= xr; + xyz.Y= yr; + xyz.Z= zr; + return xyz; + +} + Color rgb2xyz2(float R, float G, float B,char *s) +{ + double REF_X1; + double REF_Y1; + double REF_Z1; + if(!strcmp(s,"a")) + { + REF_X1=1.0985; + REF_Y1= 1.0000; + REF_Z1=0.3558; + } + else if(!strcmp(s,"c")) + { + REF_X1=0.9807; + REF_Y1= 1.0000; + REF_Z1=1.1822; + } + else if(!strcmp(s,"d50")) + { + REF_X1=0.9642; + REF_Y1= 1.0000; + REF_Z1=0.8251; + } + else if(!strcmp(s,"d65")) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(!strcmp(s,"icc")) + { + REF_X1=0.962; + REF_Y1=1.000; + REF_Z1= 0.8249; + } + else if(!strcmp(s,"e")) + { + REF_X1=1.000; + REF_Y1=1.000; + REF_Z1=1.000; + } + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz,X1,Y1,Z1,xr,yr,zr; + float r = R/255.f; //R 0..1 + float g = G/255.f; //G 0..1 + float b = B/255.f; //B 0..1 + if (r <= 0.04045) + r = r/12.92; + else + r = (float)pow((r+0.055)/1.055,2.4); + if (g <= 0.04045) + g = g/12.92; + else + g = (float)pow((g+0.055)/1.055,2.4); + if (b <= 0.04045) + b = b/12.92; + else + b = (float)pow((b+0.055)/1.055,2.4); + X1 = 0.4124*r + 0.3576*g + 0.1805*b; + Y1 = 0.2126*r + 0.7152 *g + 0.0722 *b; + Z1 = 0.0193*r + 0.1192*g + 0.9505 *b; + xr = X1; + yr = Y1; + zr = Z1; + Color xyz; + xyz.X= xr; + xyz.Y= yr; + xyz.Z= zr; + return xyz; + +} + +int opencv_rgb2xyz(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr2=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + int* piaddr=NULL; + char *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img; + float R,G,B; + int *piAddrNew = NULL; + CheckOutputArgument(pvApiCtx,1,1); + int m=0; + retrieveImage(img,1); + double *r1,*g1,*b1; + r1=(double *)malloc(sizeof(double)*img.rows*img.cols); + g1=(double *)malloc(sizeof(double)*img.rows*img.cols); + b1=(double *)malloc(sizeof(double)*img.rows*img.cols); + for(int i=0;i(i,j); + R= intensity.val[2]; + G= intensity.val[1]; + B= intensity.val[0]; + out=rgb2xyz1(R,G,B); + r1[m]=out.X; + g1[m]=out.Y; + b1[m++]=out.Z; + } + } + //creating the list that will be passed to the Scilab enviroment + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Adding the R value matrix to the list + //Syntax : createMatrixOfInteger32InList(void* _pvCtx, int _iVar, int* _piParent, int _iItemPos, int _iRows, int _iCols, const int* _piData) + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 1, img.rows,img.cols, r1); + free(r1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 2, img.rows, img.cols, g1); + free(g1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 3, img.rows, img.cols, b1); + free(b1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} +} diff --git a/sci_gateway/cpp/opencv_roifill.cpp b/sci_gateway/cpp/opencv_roifill.cpp new file mode 100644 index 0000000..919be08 --- /dev/null +++ b/sci_gateway/cpp/opencv_roifill.cpp @@ -0,0 +1,180 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = roifill(input_image, column_list, row_list) + return_image = roifill(input_image, mask) +Example: + im = roifill(image, [0 100 100 0], [0 0 100 100]) + im = roifill(image, mask) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_roifill(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRowsR=0,iColsR=0,iColsC=0,iRowsC=0; + int *piAddr = NULL; + int *piAddr2 = NULL; + double *pstDataR = NULL; + double *pstDataC = NULL; + int i, number_of_points; + int lineType = 8; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 3); + CheckOutputArgument(pvApiCtx, 1, 1); + + // Get the input image from the Scilab environment + Mat image, image_orig, mask, fin_image, mask_orig; + retrieveImage(image_orig, 1); + cvtColor(image_orig, image, CV_BGR2GRAY); + + if (nbInputArgument(pvApiCtx) == 2) + { + retrieveImage(mask_orig, 2); + + for (int i = 0; i < mask_orig.cols; i++) + { + for (int j = 0; j < mask_orig.rows; j++) + { + unsigned char val = mask_orig.at(i,j); + if (!(val == 0 || val == 1 || val == 255)) + { + sciprint("Please enter binary mask (second argument)."); + return 0; + } + } + } + + if (mask_orig.type() != CV_8UC1) + { + cvtColor(mask_orig, mask, CV_BGR2GRAY); + } + else + { + mask = mask_orig.clone(); + } + + } + else + { + // Get the address of 2nd argument, the column list + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the address of the 3rd agument, the row list + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the column list in the form of a matrix + // No. of columns = No. of elements in the list + // No. of rows = 1 + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRowsC, &iColsC, &pstDataC); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iRowsC != 1) + { + sciprint("Please enter a list of column coordinates.\n"); + return 0; + } + + // Get the row list in the form of a matrix + // No. of columns = No. of elements in the list + // No. of rows = 1 + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRowsR, &iColsR, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iRowsR != 1) + { + sciprint("Please enter a list of row coordinates.\n"); + return 0; + } + + if (iColsC != iColsR) + { + sciprint("Please ensure number of elements in both row and column lists are equal.\n"); + return 0; + } + + // Number of points is number of columns + number_of_points = iColsC; + + // Create the n points which define + // the polygon + Point points[1][number_of_points]; + + for (i = 0; i < number_of_points; i++) + { + if (pstDataR[i] < 0 || pstDataC[i] < 0) + { + sciprint("Coordinates cannot be negative.\n"); + return 0; + } + else + points[0][i] = Point(pstDataR[i], pstDataC[i]); + } + + const Point* ppt[1] = { points[0] }; + + int npt[] = { number_of_points }; + + // Create a new, black, blank image same size as of input + mask = Mat::zeros(image.size(), image.type()); + + // Call the fillPoly OpenCV function + // Fill the blank image in the polygon specified + // by the points + fillPoly(mask, ppt, npt, 1, Scalar(255, 255, 255), lineType); + } + + inpaint(image, mask, fin_image, 3, INPAINT_TELEA); + + string tempstring = type2str(fin_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, fin_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_roipoly.cpp b/sci_gateway/cpp/opencv_roipoly.cpp new file mode 100644 index 0000000..0b11364 --- /dev/null +++ b/sci_gateway/cpp/opencv_roipoly.cpp @@ -0,0 +1,145 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = roipoly(input_image, column_list, row_list) +Example: + im = roipoly(image, [0 100 100 0], [0 0 100 100]) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_roipoly(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRowsR=0,iColsR=0,iColsC=0,iRowsC=0; + int *piAddr = NULL; + int *piAddr2 = NULL; + double *pstDataR = NULL; + double *pstDataC = NULL; + int i, number_of_points; + int lineType = 8; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Get the input image from the Scilab environment + Mat image; + retrieveImage(image, 1); + + // Get the address of 2nd argument, the column list + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the address of the 3rd agument, the row list + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the column list in the form of a matrix + // No. of columns = No. of elements in the list + // No. of rows = 1 + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRowsC, &iColsC, &pstDataC); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iRowsC != 1) + { + sciprint("Please enter a list of column coordinates.\n"); + return 0; + } + + // Get the row list in the form of a matrix + // No. of columns = No. of elements in the list + // No. of rows = 1 + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRowsR, &iColsR, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iRowsR != 1) + { + sciprint("Please enter a list of row coordinates.\n"); + return 0; + } + + if (iColsC != iColsR) + { + sciprint("Please ensure number of elements in both row and column lists are equal.\n"); + return 0; + } + + // Number of points is number of columns + number_of_points = iColsC; + + // Create the n points which define + // the polygon + Point points[1][number_of_points]; + + for (i = 0; i < number_of_points; i++) + { + if (pstDataR[i] < 0 || pstDataC[i] < 0) + { + sciprint("Coordinates cannot be negative.\n"); + return 0; + } + else + points[0][i] = Point(pstDataR[i], pstDataC[i]); + } + + const Point* ppt[1] = { points[0] }; + + int npt[] = { number_of_points }; + + // Create a new, white, blank image same size as of input + Mat img = Mat::zeros(image.size(), image.type()); + + // Call the fillPoly OpenCV function + // Fill the blank image in the polygon specified + // by the points + fillPoly(img, ppt, npt, 1, Scalar(255, 255, 255), lineType); + + string tempstring = type2str(img.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, img, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_scharr.cpp b/sci_gateway/cpp/opencv_scharr.cpp new file mode 100644 index 0000000..cb897d0 --- /dev/null +++ b/sci_gateway/cpp/opencv_scharr.cpp @@ -0,0 +1,155 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + return_image = scharr(input_image, ddepth, dx, dy, scale, delta); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_scharr(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **ddepth = NULL; + int i,j,k; + double dx,dy,scale,delta; + + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of dx + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &dx); + if(intErr) + return intErr; + + //for value of dy + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &dy); + if(intErr) + return intErr; + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &scale); + if(intErr) + return intErr; + + //for value of delta + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &delta); + if(intErr) + return intErr; + + Mat new_image(image.rows,image.cols,image.type()); + + if(strcmp(ddepth[0],"CV_8U")==0) + Scharr(image,new_image,CV_8U,dx,dy,scale,delta); + else if(strcmp(ddepth[0],"CV_16U")==0) + Scharr(image,new_image,CV_16U,dx,dy,scale,delta); + else if(strcmp(ddepth[0],"CV_16S")==0) + Scharr(image,new_image,CV_16S,dx,dy,scale,delta); + else if(strcmp(ddepth[0],"CV_32F")==0) + Scharr(image,new_image,CV_32F,dx,dy,scale,delta); + else if(strcmp(ddepth[0],"CV_64F")==0) + Scharr(image,new_image,CV_64F,dx,dy,scale,delta); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_sepFilter2D.cpp b/sci_gateway/cpp/opencv_sepFilter2D.cpp new file mode 100644 index 0000000..cf3db15 --- /dev/null +++ b/sci_gateway/cpp/opencv_sepFilter2D.cpp @@ -0,0 +1,261 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = sepFilter2D(input_image,"",kernel_x,kernel_y,anchor_x,anchor_y,delta,""); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_sepFilter2D(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + char **ddepth = NULL; + char **borderType = NULL; + int i,j,k; + double *kernelX,*kernelY,anchorX,anchorY,delta,border,actualddepth; + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 8); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); +cout<<"here1"; + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for kernel_x matrix + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&kernelX); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n; + + if(iRows == 1) + n=iCols; + else + n=iRows; + double kernelArray_x[n]; + //assigning values to actual kernelMatrix + for(i=0;i= n || anchorY >=n) + { + sciprint("Invalid anchor point given. Centre point (-1,-1) was used instead"); + anchorY = -1; + anchorX = -1; + } + Mat new_image(image.rows,image.cols,image.type()); + Point pt(anchorX,anchorY); + + if(strcmp(ddepth[0],"CV_8U")==0) + actualddepth=CV_8U; + else if(strcmp(ddepth[0],"CV_16U")==0) + actualddepth=CV_16U; + else if(strcmp(ddepth[0],"CV_16S")==0) + actualddepth=CV_16S; + else if(strcmp(ddepth[0],"CV_32F")==0) + actualddepth=CV_32F; + else if(strcmp(ddepth[0],"CV_64F")==0) + actualddepth=CV_64F; + else + { + sciprint("Invalid type %s used. CV_8U was used instead by default",ddepth[0]); + actualddepth = CV_8U; + } + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + //applying function + sepFilter2D(image,new_image,actualddepth,kernelMatrix_x,kernelMatrix_y,pt,delta,border); + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_sobel.cpp b/sci_gateway/cpp/opencv_sobel.cpp new file mode 100644 index 0000000..912be4d --- /dev/null +++ b/sci_gateway/cpp/opencv_sobel.cpp @@ -0,0 +1,172 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + return_image = sobel(input_image, ddepth, dx, dy, kszie, scale, delta); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_sobel(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **ddepth = NULL; + int i,j,k; + double dx,dy,ksize,scale,delta; + + + //checking input argument + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of dx + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &dx); + if(intErr) + return intErr; + + //for value of dy + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &dy); + if(intErr) + return intErr; + + //for value of ksize + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &ksize); + if(intErr) + return intErr; + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &scale); + if(intErr) + return intErr; + + //for value of delta + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7, &delta); + if(intErr) + return intErr; + + Mat new_image(image.rows,image.cols,CV_8UC3); + if((int)ksize!=1 && (int)ksize!=3 && (int)ksize!=5 && (int)ksize!=7) + { + sciprint("Value of 1,3,5,7 Required for ksize. Default value of 3 was used instead"); + ksize=3; + } + + //void Sobel(InputArray src, OutputArray dst, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT ) + if(strcmp(ddepth[0],"CV_8U")==0) + Sobel(image,new_image,CV_8U,dx,dy,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_16U")==0) + Sobel(image,new_image,CV_16U,dx,dy,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_16S")==0) + Sobel(image,new_image,CV_16S,dx,dy,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_32F")==0) + Sobel(image,new_image,CV_32F,dx,dy,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_64F")==0) + Sobel(image,new_image,CV_64F,dx,dy,ksize,scale,delta); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_ssim.cpp b/sci_gateway/cpp/opencv_ssim.cpp new file mode 100644 index 0000000..b570704 --- /dev/null +++ b/sci_gateway/cpp/opencv_ssim.cpp @@ -0,0 +1,94 @@ +/******************************************************** +Author: Dhruti Shah +******************************************************** +Usage: ssim_value = ssim (image, reference) +Example: + val = ssim(image, ref) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_ssim(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Get the input image from the Scilab environment + Mat image; + retrieveImage(image, 1); + + Mat ref; + retrieveImage(ref, 2); + + double mean_img=0, mean_ref=0, std_img=0, std_ref=0, covar=0, mean_xy=0; + + for (int i = 0; i < image.rows; ++i) + { + for (int j = 0; j < image.cols; ++j) + { + mean_img += image.at(i,j); + mean_ref += ref.at(i,j); + mean_xy += (image.at(i,j)*ref.at(i,j)); + } + } + + int no_of_pixels = image.rows * image.cols; + mean_img = mean_img/no_of_pixels; + mean_ref = mean_ref/no_of_pixels; + mean_xy = mean_xy/no_of_pixels; + covar = mean_xy - (mean_img * mean_ref); + + for (int m = 0; m < image.rows; ++m) + { + for (int n = 0; n < image.cols; ++n) + { + std_img += ( (image.at(m,n) - mean_img) * (image.at(m,n) - mean_img) ) ; + std_ref += ( (ref.at(m,n) - mean_ref) * (ref.at(m,n) - mean_ref) ) ; + } + } + + std_ref = std_ref/no_of_pixels; + std_ref = sqrt(std_ref); + + std_img = std_img/no_of_pixels; + std_img = sqrt(std_img); + + double value; + value = ( (2*mean_img*mean_ref) + 0.0001 ) * ( (2*covar) + 0.0009 ); + value = value / ( ((mean_img*mean_img) + (mean_ref*mean_ref) + 0.0001) * ((std_img*std_img) + (std_ref*std_ref) + 0.0009) ); + + intErr = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1,value); + if(intErr) + { + return intErr; + } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_threshold.cpp b/sci_gateway/cpp/opencv_threshold.cpp new file mode 100644 index 0000000..1210d47 --- /dev/null +++ b/sci_gateway/cpp/opencv_threshold.cpp @@ -0,0 +1,146 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + [return_image, double] = threshold(input_image, threshold_value, max_value, ""); +********************************************************/ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_threshold(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddrNew2 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + double thresh,max_val; + int type; + char **gettype = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 2, 2) ; + Mat image; + retrieveImage(image,1); + if(no_of_channels(image.type()) != 1) + cvtColor( image, image, CV_BGR2GRAY ); + + + //for threshold value (thresh) + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,&thresh); + if(intErr) + return intErr; + + //for max_value + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&max_val); + if(intErr) + return intErr; + + +// for type of thresholding (gettype) + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + gettype= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + gettype[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, gettype); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //for value of type + + Mat new_image(image.rows,image.cols,CV_8UC1); + if(strcmp(gettype[0],"THRESH_BINARY")==0) + type = 0; + else if (strcmp(gettype[0],"THRESH_BINARY_INV")==0) + type=1; + else if (strcmp(gettype[0],"THRESH_TRUNC")==0) + type=2; + else if (strcmp(gettype[0],"THRESH_TOZERO")==0) + type=3; + else if (strcmp(gettype[0],"THRESH_TOZERO_INV")==0) + type=4; + else + { + sciprint("Wrong type given. THRESH_BINARY used instead"); + type=0; + } + double res = threshold( image ,new_image, thresh, max_val,type); + // Converting resultant image to 3D matrix + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + intErr = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2,res); + if(intErr) + { + return intErr; + } + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_undistort.cpp b/sci_gateway/cpp/opencv_undistort.cpp new file mode 100644 index 0000000..da72d8e --- /dev/null +++ b/sci_gateway/cpp/opencv_undistort.cpp @@ -0,0 +1,118 @@ +/*************************************************** +Author : Sukul Bagai, Shubheksha Jalan +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_undistort(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double cameraMatrix [3][3], newCameraMatrix [3][3]; + double *temp = NULL; + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat image; + retrieveImage(image, 1); + + //for camera matrix + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &temp); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(i=0;i<3;i++) + for(j=0;j<3;j++) + cameraMatrix[i][j]=temp[(j*3)+i]; + Mat cameraMat(3,3,CV_64F,&cameraMatrix); + +//for array of coefficients + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols, &temp); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n; + if(iRows==1) + n=iCols; + else + n=iRows; + + double distCoeffs[n]; + for(i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_viscircles(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr1= NULL; + int *piAddr2= NULL; + int *piAddr3= NULL; + int *piAddr4= NULL; + char* pstData = NULL; + char* pstData1 = NULL; + Mat src_gray,image; + CheckInputArgument(pvApiCtx, 3, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + double *Cx = NULL; + double *Cy = NULL; + double *radius=NULL; + int iRows1 = 0; + int iCols1 = 0; + int iRows2 = 0; + int iCols2 = 0; + int iRet = 0; + int iRet1 = 0; + double n; + retrieveImage(image, 1); + Mat drawing = Mat::zeros(image.size(), CV_8UC3 ); + sciErr =getVarAddressFromPosition(pvApiCtx,2,&piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr1, 1, &iRows1, &iCols1, &Cx); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr1, 2, &iRows1, &iCols1, &Cy); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr =getVarAddressFromPosition(pvApiCtx,3,&piAddr2); + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows2, &iCols2,&radius); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for( int i = 1; i < iCols1; i++ ) + { + Point center(cvRound(Cx[i]), cvRound(Cy[i])); + int r = cvRound(radius[i]); + if(Rhs==3) + { + circle( drawing, center, r, Scalar(0,0,255), 1, 8, 0 ); + } + else if(Rhs==5) + { + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr3)) + { + if(isScalar(pvApiCtx, piAddr3)) + { + iRet = getAllocatedSingleString(pvApiCtx, piAddr3, &pstData); + } + } + else + { + sciprint(" The Fourth argument should be string "); + return 0; + } + if((strcasecmp(pstData,"LineWidth")!=0)&&(strcasecmp(pstData,"EdgeColor")!=0)) + { + sciprint("\nIf Fourth argument must be a string :'LineWidth' or 'EdgeColor'"); + return 0; + } + if(strcasecmp(pstData,"EdgeColor")==0) + { + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr4)) + { + if(isScalar(pvApiCtx, piAddr4)) + { + iRet1 = getAllocatedSingleString(pvApiCtx, piAddr4, &pstData1); + } + } + else + { + sciprint(" The Fifth argument should be string "); + return 0; + } + if(strcasecmp(pstData1,"b")==0) + { + circle( drawing, center, r, Scalar(0,255,255), 2, 8, 0 ); + } + else if(strcasecmp(pstData1,"r")==0) + { + circle( drawing, center, r, Scalar(255,255,0), 2, 8, 0 ); + } + else if(strcasecmp(pstData1,"g")==0) + { + circle( drawing, center, r, Scalar(255,0,255), 2, 8, 0 ); + } + else if(strcasecmp(pstData1,"y")==0) + { + circle( drawing, center, r, Scalar(255,0,0), 2, 8, 0 ); + } + else if(strcasecmp(pstData1,"m")==0) + { + circle( drawing, center, r, Scalar(0,255,0), 2, 8, 0 ); + } + else if(strcasecmp(pstData1,"c")==0) + { + circle( drawing, center, r, Scalar(0,0,255), 2, 8, 0 ); + } + else if(strcasecmp(pstData1,"k")==0) + { + circle( drawing, center, r, Scalar(255,255,255), 2, 8, 0 ); + } + else if(strcasecmp(pstData1,"w")==0) + { + circle( drawing, center, r, Scalar(0,0,0), 2, 8, 0 ); + } + else + { + sciprint("\nThe Fifth argument must be a either of string value 'b','c','r','g''m','k','w','y'"); + return 0; + } + } + if(strcasecmp(pstData,"LineWidth")==0) + { + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &n); + if(intErr) + { + return intErr; + } + // The error checks for the function + if(n != round(n) || n<=0) + { + sciprint("The value of n must be an integer\n"); + return 0; + } + circle( drawing, center, r, Scalar(255,255,0), n, 8, 0 ); + } + } + } + bitwise_not(drawing,drawing); + string tempstring = type2str(drawing.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,drawing,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +} diff --git a/sci_gateway/cpp/opencv_watershed.cpp b/sci_gateway/cpp/opencv_watershed.cpp new file mode 100644 index 0000000..b6379ba --- /dev/null +++ b/sci_gateway/cpp/opencv_watershed.cpp @@ -0,0 +1,105 @@ +/******************************************************** +Author: Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_watershed(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat src; + retrieveImage(src, 1); + // Create binary image from source image + Mat bw; + cvtColor(src, bw, CV_BGR2GRAY); + threshold(bw, bw, 40, 255, CV_THRESH_BINARY); + // Perform the distance transform algorithm + Mat dist; + distanceTransform(bw, dist, CV_DIST_L2, 3); + // Normalize the distance image for range = {0.0, 1.0} + // so we can visualize and threshold it + normalize(dist, dist, 0, 1., cv::NORM_MINMAX); + //imshow("dist", dist); + // Threshold to obtain the peaks + // This will be the markers for the foreground objects + threshold(dist, dist, .5, 1., CV_THRESH_BINARY); + //imshow("dist2", dist); + // Create the CV_8U version of the distance image + // It is needed for cv::findContours() + Mat dist_8u; + dist.convertTo(dist_8u, CV_8U); + // Find total markers + vector > contours; + findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); + int ncomp = contours.size(); + // Create the marker image for the watershed algorithm + Mat markers = Mat::zeros(dist.size(), CV_32SC1); + // Draw the foreground markers + for (int i = 0; i < ncomp; i++) + drawContours(markers, contours, i, Scalar::all(i+1), -1); + // Draw the background marker + circle(markers, cv::Point(5,5), 3, CV_RGB(255,255,255), -1); + //imshow("markers", markers*10000); + // Perform the watershed algorithm + watershed(src, markers); + // Generate random colors + vector colors; + for (int i = 0; i < ncomp; i++) + { + int b = theRNG().uniform(0, 255); + int g = theRNG().uniform(0, 255); + int r = theRNG().uniform(0, 255); + + colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r)); + } + // Create the result image + Mat dst = Mat::zeros(markers.size(), CV_8UC3); + // Fill labeled objects with random colors + for (int i = 0; i < markers.rows; i++) + { + for (int j = 0; j < markers.cols; j++) + { + int index = markers.at(i,j); + if (index > 0 && index <= ncomp) + dst.at(i,j) = colors[index-1]; + else + dst.at(i,j) = Vec3b(0,0,0); + } + } + + //imshow("dst", dst); + + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +} diff --git a/sci_gateway/cpp/opencv_whitepoint.cpp b/sci_gateway/cpp/opencv_whitepoint.cpp new file mode 100644 index 0000000..c4d3725 --- /dev/null +++ b/sci_gateway/cpp/opencv_whitepoint.cpp @@ -0,0 +1,108 @@ +/******************************************************** +Function :whitepoint +Syntax :B=whitepoint(string) +*string :'d65' , 'd50','a','icc','c','d55' +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_whitepoint(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + + int* piAddr1=NULL; + int error; + int iRet = 0; + char* pstData = NULL; + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + sciErr = getVarAddressFromPosition(pvApiCtx, 1,&piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isStringType(pvApiCtx, piAddr1)) + { + if(isScalar(pvApiCtx, piAddr1)) + { + + iRet = getAllocatedSingleString(pvApiCtx, piAddr1, &pstData); + } + } + double *output=NULL;int k=0; + output=( double *)malloc(sizeof(double)*3); + if(strcmp(pstData,"d65")==0) + { + output[0]=0.9504; + output[1]=1.0000; + output[2]=1.0889; + } + else if(strcmp(pstData,"d50")==0) + { + output[0]=0.9642; + output[1]=1.0000; + output[2]=0.8251; + } + else if(strcmp(pstData,"icc")==0) + { + output[0]=0.9642; + output[1]=1.0000; + output[2]=0.8249; + } + else if(strcmp(pstData,"a")==0) + { + output[0]=1.0985; + output[1]=1.0000; + output[2]=0.3558; + } + else if(strcmp(pstData,"d55")==0) + { + output[0]=0.9568; + output[1]=1.0000; + output[2]=0.9214; + } + else if(strcmp(pstData,"c")==0) + { + output[0]=0.9807; + output[1]=1.0000; + output[2]=1.1823; + } + else + { + sciprint("Expected input number 1:to match one of these strings:'icc', 'd50', 'd55', 'd65', 'c', 'a'"); + return 0; + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1,1,3,output); + free(output); // Data have been copied into Scilab memory + if (sciErr.iErr) + { + //Make sure everything is cleanup in case of error + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} +} diff --git a/sci_gateway/cpp/opencv_wiener2.cpp b/sci_gateway/cpp/opencv_wiener2.cpp new file mode 100644 index 0000000..802d9ce --- /dev/null +++ b/sci_gateway/cpp/opencv_wiener2.cpp @@ -0,0 +1,178 @@ +/*//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +Created By: Riddhish Bhalodia +Date: 14th October 2015 + +Usage: + +void wiener2(Mat img,int n, double sigma) + +1) img : Input image, grayscale only +2) n : filt size +3) sigma : noise var, if sigma = 0 then the variance is estimated from data + +*///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_wiener2(char *fname, unsigned long fname_len){ + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + double n; + double sigma; + + + Mat img; + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + retrieveImage(img, 1); + //for value of the scale factor + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &n); + if(intErr) + { + return intErr; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &sigma); + if(intErr) + { + return intErr; + } + + // The error checks for the function + if(n != round(n) || n<=0 || int(n)%2==0) + { + sciprint("The value of n must be an odd integer \n"); + return 0; + } + + Mat out = Mat::zeros(img.size(),img.type()); + sigma=255*255*sigma; + if(img.channels() !=1){ + sciprint("Grayscale images only!"); + return 0; + } + + int i_min,i_max,j_min,j_max; + int w = (n-1)/2; + + if(sigma==0){ + double noise_est = 0; + for(int i=0;i(ii,jj)); + + } + } + mean = mean/(n*n); + + double var=0; + for(int ii=i_min;ii<(i_max+1);ii++){ + for(int jj=j_min;jj<(j_max+1);jj++){ + var = var + ((double)img.at(ii,jj))*((double)img.at(ii,jj)); + } + } + var = var/(n*n); + var = var - mean*mean; + noise_est = noise_est + var; + } + } + noise_est = noise_est/(img.rows*img.cols); + sigma = noise_est; + } + + for(int i=0;i(ii,jj)); + + } + } + mean = mean/(n*n); + + // find variance of the patch + double var=0; + for(int ii=i_min;ii<(i_max+1);ii++){ + for(int jj=j_min;jj<(j_max+1);jj++){ + var = var + ((double)img.at(ii,jj))*((double)img.at(ii,jj)); + } + } + var = var/(n*n); + var = var - mean*mean; + + double temp; + double sum=0; + + temp = mean + (fmax(0,(var - sigma))/fmax(var,sigma))*(((double)img.at(i,j)) - mean); + out.at(i,j) = temp; + } + } + + + // out is the return image + string tempstring = type2str(out.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,out,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + ReturnArguments(pvApiCtx); + return 0; + + + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_xyz2double.cpp b/sci_gateway/cpp/opencv_xyz2double.cpp new file mode 100644 index 0000000..3d82a7d --- /dev/null +++ b/sci_gateway/cpp/opencv_xyz2double.cpp @@ -0,0 +1,159 @@ +/* Function-xyz2double +Author :Tess Zacharias */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + // #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 +#define REF_Y 1.00000; +#define REF_Z 1.08883; + + +int opencv_xyz2double(char *fname, unsigned long fname_len) +{ + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + + double check; + Mat img,dst; + float X,Y,Z; + int *piAddrNew = NULL; + //checking output argument + CheckOutputArgument(pvApiCtx,1,1); + //checking if number of input arguments are 3 + + int k=0; + // SciErr getMatrixOfDoubleInList(void* _pvCtx, int* _piParent, int _iItemPos, int* _piRows, int* _piCols, double** _pdblReal) + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + double *r1,*g1,*b1; + r1=(double *)malloc(sizeof(double)*iRows*iCols); + g1=(double *)malloc(sizeof(double)*iRows*iCols); + b1=(double *)malloc(sizeof(double)*iRows*iCols); + for(int i=0;i(i,j); + X= (pstDataR[k]); + Y= (pstDataG[k]); + Z= (pstDataB[k++]); + r1[j*iRows+i]=X; + g1[j*iRows+i]=Y; + b1[j*iRows+i]=Z; + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Adding the R value matrix to the list + //Syntax : createMatrixOfInteger32InList(void* _pvCtx, int _iVar, int* _piParent, int _iItemPos, int _iRows, int _iCols, const int* _piData) + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 1, iRows,iCols, r1); + free(r1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 2, iRows, iCols, g1); + free(g1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1 , piAddrNew, 3, iRows, iCols, b1); + free(b1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} +} + diff --git a/sci_gateway/cpp/opencv_xyz2lab.cpp b/sci_gateway/cpp/opencv_xyz2lab.cpp new file mode 100644 index 0000000..2563e1c --- /dev/null +++ b/sci_gateway/cpp/opencv_xyz2lab.cpp @@ -0,0 +1,423 @@ +/*-------------------------------------- + function-xyz2lab + Author :Tess Zacharias +------------------------------------- */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; + struct Color + { + float R,G,B,L,a,b; + }; + Color xyz2lab1(float X, float Y, float Z) + { + /* colorspace conversion from xyz to lab */ + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz,Ls,as,bs; + float xr=X/REF_X; + float yr=Y/REF_Y; + float zr=Z/REF_Z; + if ( xr > eps ) + fx = (float)pow(xr, 1/3.); + else + fx = (float) ((k * xr + 16.) / 116.); + if ( yr > eps ) + fy = (float)pow(yr, 1/3.); + else + fy = (float) ((k * yr + 16.) / 116.); + if ( zr > eps ) + fz = (float)pow(zr, 1/3.); + else + fz = (float) ((k * zr + 16.) / 116); + Ls = ( 116 * fy ) - 16; + as = 500*(fx-fy); + bs = 200*(fy-fz); + Color lab; + lab.L =Ls; + lab.a =as; + lab.b =bs; + return lab; + } + Color xyz2lab2(float X, float Y, float Z,char *s) + { + double REF_X1; + double REF_Y1; + double REF_Z1; + if(strcasecmp(s,"a")==0) + { + REF_X1=1.0985; + REF_Y1= 1.0000; + REF_Z1=0.3558; + } + else if(strcasecmp(s,"c")==0) + { + REF_X1=0.9807; + REF_Y1= 1.0000; + REF_Z1=1.1822; + } + else if(strcasecmp(s,"d50")==0) + { + REF_X1=0.9642; + REF_Y1= 1.0000; + REF_Z1=0.8251; + } + else if(strcasecmp(s,"d65")==0) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(strcasecmp(s,"icc")==0) + { + REF_X1=0.962; + REF_Y1=1.000; + REF_Z1= 0.8249; + } + else if(strcasecmp(s,"d55")==0) + { + REF_X1=0.95682; + REF_Y1= 1.0000; + REF_Z1= 0.92149; + } + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz,Ls,as,bs; + float xr=X/REF_X1; + float yr=Y/REF_Y1; + float zr=Z/REF_Z1; + if ( xr > eps ) + fx = (float)pow(xr, 1/3.); + else + fx = (float) ((k * xr + 16.) / 116.); + if ( yr > eps ) + fy = (float)pow(yr, 1/3.); + else + fy = (float) ((k * yr + 16.) / 116.); + if ( zr > eps ) + fz = (float)pow(zr, 1/3.); + else + fz = (float) ((k * zr + 16.) / 116); + Ls = ( 116 * fy ) - 16; + as = 500*(fx-fy); + bs = 200*(fy-fz); + Color lab; + lab.L =Ls; + lab.a =as; + lab.b =bs; + return lab; +} +Color xyz2lab3(float X, float Y, float Z,char *s) + { + double REF_X1; + double REF_Y1; + double REF_Z1; + if(strcasecmp(s,"sRGB")==0) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(strcasecmp(s,"adobe-rgb-1998")==0) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz,Ls,as,bs; + float xr=X/REF_X1; + float yr=Y/REF_Y1; + float zr=Z/REF_Z1; + if ( xr > eps ) + fx = (float)pow(xr, 1/3.); + else + fx = (float) ((k * xr + 16.) / 116.); + if ( yr > eps ) + fy = (float)pow(yr, 1/3.); + else + fy = (float) ((k * yr + 16.) / 116.); + if ( zr > eps ) + fz = (float)pow(zr, 1/3.); + else + fz = (float) ((k * zr + 16.) / 116); + Ls = ( 116 * fy ) - 16; + as = 500*(fx-fy); + bs = 200*(fy-fz); + Color lab; + lab.L =Ls; + lab.a =as; + lab.b =bs; + return lab; +} +int opencv_xyz2lab(char *fname, unsigned long fname_len) +{ + SciErr sciErr; + int* piAddr1=NULL; + int* piAddr2=NULL; + int* piAddr3=NULL; + int* piAddrNew=NULL; + int iRows = 0; + int iCols = 0; + char* pstData = NULL; + char* pstData1 = NULL; + CheckInputArgument(pvApiCtx,1,3); + CheckOutputArgument(pvApiCtx,1,1); + int m=0; + int iType = 0; + double *value=NULL; + int iPrec= 0; + Mat img,k; + int iRet = 0; + int iRet1 = 0; + Color out; + float X,Y,Z; + sciErr =getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + sciErr = getVarType(pvApiCtx, piAddr1, &iType); + if(nbInputArgument(pvApiCtx)==3) + { + /*retrieve second argument */ + sciErr = getVarAddressFromPosition(pvApiCtx, 2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr2)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet = getAllocatedSingleString(pvApiCtx, piAddr2, &pstData); + } + } + else + { + sciprint(" The second argument should be string "); + return 0; + } + if((strcasecmp(pstData,"whitepoint")!=0)&&(strcasecmp(pstData,"Colorspace")!=0)) + { + sciprint("\n Second argument should be any of these string :'whitepoint','Colorspace'"); + return 0; + } + /*retrieve third argument */ + sciErr = getVarAddressFromPosition(pvApiCtx, 3,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr3)) + { + if(isScalar(pvApiCtx, piAddr2)) + { + iRet1 = getAllocatedSingleString(pvApiCtx, piAddr3, &pstData1); + } + } + else + { + sciprint(" The third argument should be string "); + return 0; + } + if((strcasecmp(pstData,"whitepoint")==0)) + { + if((strcasecmp(pstData1,"d65")!=0)&&(strcasecmp(pstData1,"d50")!=0)&&(strcasecmp(pstData1,"d55")!=0)&&(strcasecmp(pstData1,"icc")!=0)&&(strcasecmp(pstData1,"icc")!=0)&&(strcasecmp(pstData1,"a")!=0)&&(strcasecmp(pstData1,"c")!=0)) + { + sciprint("\nIf Second argument is 'whitepoint' then Third argument should be any of these string :'a','d65','d55','d50','icc','c'"); + return 0; + } + } + if((strcasecmp(pstData,"Colorspace")==0)) + { + if((strcasecmp(pstData1,"sRGB")!=0)&&(strcasecmp(pstData1,"adobe-rgb-1998")!=0)) + { + sciprint("\nIf Second argument is 'Colorspace' then Third argument should be any of these string :'sRGB','adobe-rgb-1998'"); + return 0; + } + } + } + switch(iType) + { + case sci_matrix: + { + /*retrieve matrix values */ + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols,&value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + double *output=NULL; + output=( double *)malloc(sizeof(double)*iRows*iCols); + int j=0; + if(nbInputArgument(pvApiCtx)==1) + { + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + // #include "../common.h" + #define REF_X .95047; // Observer= 2°, Illuminant= D65 +#define REF_Y 1.00000; +#define REF_Z 1.08883; + +struct Color +{ + float X,Y,Z,R,G,B; +}; + +Color xyz2rgb1(float X, float Y, float Z) +{ + + float x = X; + float y = Y; + float z = Z; + float R1 = 3.2404542*x-1.5371385*y-0.4985314*z; + float G1 =-0.9692660*x+1.8760108*y+0.0415560 *z; + float B1 = 0.0556434*x-0.2040259*y+1.0572252*z; + if( R1 > 0.0031308 ) + R1 = 1.055 * ( pow( R1, 1./2.4 ) ) - 0.055; + else + R1 = 12.92 * R1; + if( G1 > 0.0031308 ) + G1 = 1.055 * ( pow( G1, 1./2.4 ) ) - 0.055; + else + G1 = 12.92 * G1; + if( B1 > 0.0031308 ) + B1 = 1.055 * ( pow( B1, 1./2.4 ) ) - 0.055; + else + B1= 12.92 * B1; + + /*R1 = R1*REF_X; + G1 = G1*REF_Y; + B1 = B1*REF_Z; */ + /* R1 = R1 * 255; + G1 = G1 * 255; + B1 = B1 * 255;*/ + Color rgb; + rgb.R= R1; + rgb.G= G1; + rgb.B= B1; + return rgb; + +} + Color xyz2rgb2(float X, float Y, float Z,char *s) +{ + double REF_X1; + double REF_Y1; + double REF_Z1; + if(!strcmp(s,"a")) + { + REF_X1=1.0985; + REF_Y1= 1.0000; + REF_Z1=0.3558; + } + else if(!strcmp(s,"c")) + { + REF_X1=0.9807; + REF_Y1= 1.0000; + REF_Z1=1.1822; + } + else if(!strcmp(s,"d50")) + { + REF_X1=0.9642; + REF_Y1= 1.0000; + REF_Z1=0.8251; + } + else if(!strcmp(s,"d65")) + { + REF_X1=0.9504; + REF_Y1= 1.0000; + REF_Z1= 1.0888; + } + else if(!strcmp(s,"icc")) + { + REF_X1=0.962; + REF_Y1=1.000; + REF_Z1= 0.8249; + } + else if(!strcmp(s,"e")) + { + REF_X1=1.000; + REF_Y1=1.000; + REF_Z1=1.000; + } + float x = X; + float y = Y; + float z = Z; + float R1 = 3.2406*x-1.5372*y-0.4986*z; + float G1 =-0.9689*x+1.8758*y+0.0415 *z; + float B1 = 0.0557*x-0.2040*y+1.0570*z; + if( R1 > 0.0031308 ) + R1 = 1.055 * ( pow( R1, 1./2.4 ) ) - 0.055; + else + R1 = 12.92 * R1; + if( G1 > 0.0031308 ) + G1 = 1.055 * ( pow( G1, 1./2.4 ) ) - 0.055; + else + G1 = 12.92 * G1; + if( B1 > 0.0031308 ) + B1 = 1.055 * ( pow( B1, 1./2.4 ) ) - 0.055; + else + B1= 12.92 * B1; + + /*R1 = R1*REF_X; + G1 = G1*REF_Y; + B1 = B1*REF_Z;*/ + /* R1 = R1 * 255; + G1 = G1 * 255; + B1 = B1 * 255; */ + Color rgb; + rgb.R= R1; + rgb.G= G1; + rgb.B= B1; + return rgb; + + +} + int opencv_xyz2rgb(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + int* piLen = NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img,dst; + float X,Y,Z; + int *piAddrNew = NULL; + //checking output argument + CheckOutputArgument(pvApiCtx,1,1); + //checking if number of input arguments are 3 + + int k=0; + // SciErr getMatrixOfDoubleInList(void* _pvCtx, int* _piParent, int _iItemPos, int* _piRows, int* _piCols, double** _pdblReal) + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int m=0; + double *r,*g,*b; + r=(double *)malloc(sizeof(double)*iRows*iCols); + g=(double *)malloc(sizeof(double)*iRows*iCols); + b=(double *)malloc(sizeof(double)*iRows*iCols); + for(int i=0;i(i,j); + X= (pstDataR[k]); + Y= (pstDataG[k]); + Z= (pstDataB[k++]); + out=xyz2rgb1(X,Y,Z); + //cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + struct Color + { + float R,G,B,X,Y,Z; + }; + Color xyz2uint16(float X, float Y, float Z) + { + /* xyz to uint16 conversion */ + float R1=round((65535 * (X / (1 + (32767/32768))))/2); + float G1=round((65535 * (Y / (1 + (32767/32768))))/2); + float B1=round((65535 * (Z / (1 + (32767/32768))))/2); + + Color rgb; + rgb.R = R1; + rgb.G = G1; + rgb.B = B1; + return rgb; + } + int opencv_xyz2uint16(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int iRows= 0; + int iCols= 0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + int i,j; + int error; + Color out; + float X,Y,Z; + int *piAddrNew = NULL; + CheckOutputArgument(pvApiCtx,1,1); + int k=0; + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int m=0; + unsigned short *r1,*g1,*b1; + r1=(unsigned short *)malloc(sizeof(unsigned short )*iRows*iCols); + g1=(unsigned short *)malloc(sizeof(unsigned short )*iRows*iCols); + b1=(unsigned short *)malloc(sizeof(unsigned short )*iRows*iCols); + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" //new header file for cvtColor function of opencv +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include +#include "../common.h" +int opencv_rgb2gray(char *fname, unsigned long fname_len) +{ + + // Error management variable + SciErr sciErr; + //checking input argument + CheckInputArgument(pvApiCtx,1,1); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1) + Mat img,grayimg; + //calling function + retrieveImage(img,1); + cvtColor(img, grayimg, CV_BGR2GRAY ); + string tempstring = type2str(grayimg.type()); + char* checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,grayimg,1); + //returning output variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} + +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/rgb2gray.cpp~ b/sci_gateway/cpp/rgb2gray.cpp~ new file mode 100644 index 0000000..c422638 --- /dev/null +++ b/sci_gateway/cpp/rgb2gray.cpp~ @@ -0,0 +1,46 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : return_image=rgb2gray(sourceimage) */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" //new header file for cvtColor function of opencv +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include +#include "../common.h" +int rgb2gray(char *fname, unsigned long fname_len) +{ + + // Error management variable + SciErr sciErr; + //checking input argument + CheckInputArgument(pvApiCtx,1,1); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1) + Mat img,grayimg; + //calling function + retrieveImage(img,1); + cvtColor(img, grayimg, CV_BGR2GRAY ); + string tempstring = type2str(grayimg.type()); + char* checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,grayimg,1); + //returning output variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; +} + +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/warpaffine.cpp b/sci_gateway/cpp/warpaffine.cpp new file mode 100644 index 0000000..014876b --- /dev/null +++ b/sci_gateway/cpp/warpaffine.cpp @@ -0,0 +1,214 @@ +/* ==================================================================== */ +/* Author :Priyanka Hiranandani NIT Surat */ +/* ==================================================================== */ +/* Syntax : rotated_image=warpaffline(InputArray src, InputArray M,int size_x,int size_y ,string flag, string borderMode); */ +/* ==================================================================== */ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + extern "C" + { + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + int warpaffine(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int* piAddr = NULL; + int *piAddr7 =NULL; + int* piAddr2 = NULL; + int* piAddr3 = NULL; + int* piAddr4 = NULL; + int* piAddr5 = NULL; + int* piAddr6 = NULL; + int* piAddrNew = NULL; + int* piLen = NULL; + double* pstData = NULL; + int* pstData2 = NULL; + double *rrows; + double *rcols; + int error; + + ////checking input argument + CheckInputArgument(pvApiCtx,6,6); + //checking output argument + CheckOutputArgument(pvApiCtx, 1, 1); + //for first argument + // get Address of first input + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + //retrieving image + Mat img,dst; + retrieveImage(img,1); + //second argument + double *m; + //fetching address of second argument + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //this function will fetch value of second argument + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols ,&m); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Mat m1=Mat(2,3,CV_64F); + int k=0; + for(int j=0;j<3;j++) + { + for(int i=0;i<2;i++) + { + m1.at(i,j)=m[k++]; + // cout<< m1.at(i,j)<<"\n"; + } + } + + cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +#include +#include +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + #define REF_X .9504; // Observer= 2°, Illuminant= D65 + #define REF_Y 1.00000; + #define REF_Z 1.08883; + + struct Color + { + float R,G,B,X,Y,Z,L,a,b; + }; + + Color xyz2lab3(float X,float Y, float Z) + { + float eps = 216.f/24389.f; + float k = 24389.f/27.f; + float fx,fy,fz,Ls,as,bs; + float xr=X/REF_X; + float yr=Y/REF_Y; + float zr=Z/REF_Z; + if (xr > eps) + fx = (float)pow(xr, 1/3.); + else + fx = (float) ((k * xr + 16.) / 116.); + if (yr > eps) + fy = (float)pow(yr, 1/3.); + else + fy = (float) ((k * yr + 16.) / 116.); + if (zr > eps) + fz = (float)pow(zr, 1/3.); + else + fz = (float) ((k * zr + 16.) / 116); + Ls = ( 116 * fy ) - 16; + as = 500*(fx-fy); + bs = 200*(fy-fz); + Color lab; + lab.L =Ls; + lab.a =as; + lab.b =bs; + return lab; + } + + + int xyz2lab(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + //variable info + int iRows=0; + int iCols=0; + int piRows=0; + int piCols=0; + int* piAddr=NULL; + int* piAddr1=NULL; + int* piAddr3=NULL; + double *value=NULL; + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + int* piLen= NULL; + char **val; + int i,j; + int error; + Color out; + double check; + Mat img,dst; + float R,G,B; + int *piAddrNew = NULL; + double *r,*g,*b; + int k=0; + //checking output argument + CheckOutputArgument(pvApiCtx,1,1); + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //dynamically allocating memory to varibles + r=(double *)malloc(sizeof(double)*iRows*iCols); + g=(double *)malloc(sizeof(double)*iRows*iCols); + b=(double *)malloc(sizeof(double)*iRows*iCols); + for(int i=0;i [] then + mdelete('loader.sce'); +end +// ------------------------------------------------------ +if fileinfo('libskeleton_imageprocessing.so') <> [] then + mdelete('libskeleton_imageprocessing.so'); +end +// ------------------------------------------------------ +if fileinfo('libskeleton_imageprocessing.c') <> [] then + mdelete('libskeleton_imageprocessing.c'); +end +// ------------------------------------------------------ +chdir(curdir); +// ------------------------------------------------------ diff --git a/sci_gateway1/cpp/common.cpp b/sci_gateway1/cpp/common.cpp new file mode 100644 index 0000000..c19f58a --- /dev/null +++ b/sci_gateway1/cpp/common.cpp @@ -0,0 +1,890 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +//takes the matrix type as parameter and returns a string of the type +string type2str(int type) +{ + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + switch ( depth ) + { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + return r; +} + +//takes matrix type as parameter and returns no. of channels of the image matrix +int no_of_channels(int type) +{ + uchar chans = 1 + (type >> CV_CN_SHIFT); + return chans; +} + +//function used to retrieve image matrix from Scilab environment +/* + We need to retrieve image from the Scilab environment + We do this by getting a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int retrieveImage(Mat &image,int pos) +{ + + SciErr sciErr; + int iRows=0,iCols=0,i,j,k=0; + int *piAddr = NULL; + int *piAddrChild = NULL; + int iPrec = 0,iItem = 0; + + //retrieving number of items in the list and type of data(integer/float) + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + if(iItem==3) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC3); + + /*Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + if(iItem==3) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + //i+iRows*j + } + } + } + else + { + short unsigned int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC1); + + //Assigning matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT16: //for signed integer 16 + { + if(iItem==3) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + } + } + else //for floating point/ double precision values + { + if(iItem==3) + { + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + double *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + } + return 1; +} + +//function that returns an image matrix to the Scilab environment +/* + What we now need, is to pass the image matrix to the Scilab environment. + We do this by passing a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int returnImage(char *checker,Mat img,int pos) +{ + int i,j,k=0; + int *piAddrNew = NULL; + SciErr sciErr; + int num=no_of_channels(img.type()); //num now contains no. of channels of the image + //creating the list that will be passed to the Scilab enviroment + if(num==3) + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 3, &piAddrNew); + else + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 1, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(strcmp(checker,"8U")==0) //for Unsigned Integer 8 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *b = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r [i+img.rows*j]=intensity.val[2]; + g [i+img.rows*j]=intensity.val[1]; + b [i+img.rows*j]=intensity.val[0]; + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"16U")==0) //for Unsigned Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *b = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + } + else if(strcmp(checker,"16S")==0) //for Signed Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *b = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"32S")==0) //for Signed Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *b = (int *)malloc(img.rows * img.cols * sizeof(int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + + else if(strcmp(checker,"32F")==0) //for Float 32 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + else if(strcmp(checker,"64F")==0) //For Float 64 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + +} diff --git a/sci_gateway1/cpp/common.cpp~ b/sci_gateway1/cpp/common.cpp~ new file mode 100644 index 0000000..82d542f --- /dev/null +++ b/sci_gateway1/cpp/common.cpp~ @@ -0,0 +1,924 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +//takes the matrix type as parameter and returns a string of the type +string type2str(int type) +{ + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + switch ( depth ) + { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + return r; +} + +//takes matrix type as parameter and returns no. of channels of the image matrix +int no_of_channels(int type) +{ + uchar chans = 1 + (type >> CV_CN_SHIFT); + return chans; +} + +//function used to retrieve image matrix from Scilab environment +/* + We need to retrieve image from the Scilab environment + We do this by getting a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int retrieveImage(Mat &image,int pos) +{ + + SciErr sciErr; + int iRows=0,iCols=0,i,j,k=0; + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piAddrChild = NULL; + int iPrec = 0,iItem = 0; + int r=0,c=0; + + //retrieving number of items in the list and type of data(integer/float) + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + + + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + + if(iItem==3) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(r=0;r(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + + if(iItem==3) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + //i+iRows*j + } + } + } + else + { + short unsigned int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC1); + + //Assigning matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT16: //for signed integer 16 + { + + if(iItem==3) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + } + } + else //for floating point/ double precision values + { + + if(iItem==3) + { + + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + + } + else + { + + double *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr1, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + } + return 1; +} + +//function that returns an image matrix to the Scilab environment +/* + What we now need, is to pass the image matrix to the Scilab environment. + We do this by passing a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int returnImage(char *checker,Mat img,int pos) +{ + int i,j,k=0; + int *piAddrNew = NULL; + SciErr sciErr; + int num=no_of_channels(img.type()); //num now contains no. of channels of the image + //creating the list that will be passed to the Scilab enviroment + if(num==3) + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 3, &piAddrNew); + else + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 1, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(strcmp(checker,"8U")==0) //for Unsigned Integer 8 + { + if(num==3) + { + + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *b = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + j*img.rows + i)=intensity.val[2]; + *(g + j*img.rows + i)=intensity.val[1]; + *(b + j*img.rows + i)=intensity.val[0]; + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"16U")==0) //for Unsigned Integer 16 + { + + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *b = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + } + else if(strcmp(checker,"16S")==0) //for Signed Integer 16 + { + + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *b = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"32S")==0) //for Signed Integer 16 + { + + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *b = (int *)malloc(img.rows * img.cols * sizeof(int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + + else if(strcmp(checker,"32F")==0) //for Float 32 + { + if(num==3) + { + + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + else if(strcmp(checker,"64F")==0) //For Float 64 + { + + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + +} diff --git a/sci_gateway1/cpp/common.h b/sci_gateway1/cpp/common.h new file mode 100644 index 0000000..5de6cf7 --- /dev/null +++ b/sci_gateway1/cpp/common.h @@ -0,0 +1,9 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +string type2str(int type); +int no_of_channels(int type); +int retrieveImage(Mat &image,int pos); +int returnImage(char *checker,Mat img,int pos); + diff --git a/sci_gateway1/cpp/common.h~ b/sci_gateway1/cpp/common.h~ new file mode 100644 index 0000000..d42d9c7 --- /dev/null +++ b/sci_gateway1/cpp/common.h~ @@ -0,0 +1,10 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ +//using namespace std; + +string type2str(int type); +int no_of_channels(int type); +int retrieveImage(Mat &image,int pos); +int returnImage(char *checker,Mat img,int pos); +//int retrieveImageFromAddress(Mat &image,int *piAddr); diff --git a/sci_gateway1/cpp/common1.cpp b/sci_gateway1/cpp/common1.cpp new file mode 100644 index 0000000..41752e6 --- /dev/null +++ b/sci_gateway1/cpp/common1.cpp @@ -0,0 +1,897 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +//takes the matrix type as parameter and returns a string of the type +string type2str(int type) +{ + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + switch ( depth ) + { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + return r; +} + +//takes matrix type as parameter and returns no. of channels of the image matrix +int no_of_channels(int type) +{ + uchar chans = 1 + (type >> CV_CN_SHIFT); + return chans; +} + +//function used to retrieve image matrix from Scilab environment +/* + We need to retrieve image from the Scilab environment + We do this by getting a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int retrieveImage(Mat &image,int pos) +{ + + SciErr sciErr; + int iRows=0,iCols=0,i,j,k=0; + int *piAddr = NULL; + int *piAddrChild = NULL; + int iPrec = 0,iItem = 0; + + //retrieving number of items in the list and type of data(integer/float) + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciprint("Test1"); + sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciprint("Testing1"); + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + if(iItem==3) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC3); + + /*Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + if(iItem==3) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + //i+iRows*j + } + } + } + else + { + short unsigned int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC1); + + //Assigning matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT16: //for signed integer 16 + { + if(iItem==3) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + } + } + else //for floating point/ double precision values + { + sciprint("Test2"); + if(iItem==3) + { + sciprint("Again"); + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; +// sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + sciprint("Testing2"); + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list +// sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + sciprint("Testing3"); + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list +// sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + sciprint("Testing4"); + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + sciprint("Test3"); + } + else + { + double *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + } + return 1; +} + +//function that returns an image matrix to the Scilab environment +/* + What we now need, is to pass the image matrix to the Scilab environment. + We do this by passing a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int returnImage(char *checker,Mat img,int pos) +{ + int i,j,k=0; + int *piAddrNew = NULL; + SciErr sciErr; + int num=no_of_channels(img.type()); //num now contains no. of channels of the image + //creating the list that will be passed to the Scilab enviroment + if(num==3) + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 3, &piAddrNew); + else + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 1, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(strcmp(checker,"8U")==0) //for Unsigned Integer 8 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *b = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + j*img.rows + i)=intensity.val[2]; + *(g + j*img.rows + i)=intensity.val[1]; + *(b + j*img.rows + i)=intensity.val[0]; + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"16U")==0) //for Unsigned Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *b = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + } + else if(strcmp(checker,"16S")==0) //for Signed Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *b = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"32S")==0) //for Signed Integer 16 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *b = (int *)malloc(img.rows * img.cols * sizeof(int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + + else if(strcmp(checker,"32F")==0) //for Float 32 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + else if(strcmp(checker,"64F")==0) //For Float 64 + { + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + +} diff --git a/sci_gateway1/cpp/common2.cpp b/sci_gateway1/cpp/common2.cpp new file mode 100644 index 0000000..0f296ed --- /dev/null +++ b/sci_gateway1/cpp/common2.cpp @@ -0,0 +1,2527 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +//takes the matrix type as parameter and returns a string of the type +string type2str(int type) +{ + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + switch ( depth ) + { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + return r; +} + +//takes matrix type as parameter and returns no. of channels of the image matrix +//int no_of_channels(int type) +//{ +// uchar chans = 1 + (type >> CV_CN_SHIFT); +// return chans; +//} +//function used to retrieve image matrix from Scilab environment +//int retrieveImageFromAddress(Mat &image,int *piAddr) +//{ +// SciErr sciErr; +// int iRows=0,iCols=0,i,j,k=0; +// int *piChild = NULL; +// int iPrec = 0,iItem = 0; +// sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piChild); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// if(isIntegerType(pvApiCtx, piChild)) +// { +// //getting precision +// sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piChild, &iPrec); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// switch(iPrec) +// { +// case SCI_UINT8: //for unsigned integer 8 +// { +// if(iItem==3) +// { +// unsigned char *pstDataR = NULL; +// unsigned char *pstDataG = NULL; +// unsigned char *pstDataB = NULL; +// //retrive the matrix of the R values +// sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// //retrive the matrix of the G values +// sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// //retrive the matrix of the B values +// sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers +// image = Mat(iRows,iCols,CV_8UC3); + +// /*Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this*/ +// k=0; +// for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; +// image.at(i,j)[1]=pstDataG[i+iRows*j]; +// image.at(i,j)[0]=pstDataB[i+iRows*j]; +// } +// } +// } +// else +// { +// unsigned char *pstDataR = NULL; +// //retrive the matrix of the R values +// sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers +// image = Mat(iRows,iCols,CV_8UC1); + +// //Assigning matrix value to pixel +// k=0; +// for(i=0;i(i,j)=pstDataR[i+iRows*j]; +// } +// break; +// } +// case SCI_UINT16: //for unsigned integer 16 +// { +// if(iItem==3) +// { +// short unsigned int *pstDataR = NULL; +// short unsigned int *pstDataG = NULL; +// short unsigned int *pstDataB = NULL; +// //retrive the matrix of the R values +// sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// //retrive the matrix of the G values +// sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// //retrive the matrix of the B values +// sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers +// image = Mat(iRows,iCols,CV_16UC3); + +// //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this +// k=0; +// for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; +// image.at(i,j)[1]=pstDataG[i+iRows*j]; +// image.at(i,j)[0]=pstDataB[i+iRows*j]; +// //i+iRows*j +// } +// } +// } +// else +// { +// short unsigned int *pstDataR = NULL; +// //retrive the matrix of the R values +// sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers +// image = Mat(iRows,iCols,CV_16UC1); + +// //Assigning matrix values to pixels +// k=0; +// for(i=0;i(i,j)=pstDataR[i+iRows*j]; +// } +// break; +// } +// case SCI_INT16: //for signed integer 16 +// { +// if(iItem==3) +// { +// short int *pstDataR = NULL; +// short int *pstDataG = NULL; +// short int *pstDataB = NULL; +// //retrive the matrix of the R values +// sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// //retrive the matrix of the G values +// sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// //retrive the matrix of the B values +// sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers +// image = Mat(iRows,iCols,CV_16SC3); + +// //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this +// k=0; +// for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; +// image.at(i,j)[1]=pstDataG[i+iRows*j]; +// image.at(i,j)[0]=pstDataB[i+iRows*j]; +// } +// } +// } +// else +// { +// short int *pstDataR = NULL; +// //retrive the matrix of the R values +// sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers +// image = Mat(iRows,iCols,CV_16SC1); + +// //Assigning image matrix values to pixels +// k=0; +// for(i=0;i(i,j)=pstDataR[i+iRows*j]; +// } +// break; +// } +// } +// } +// else //for floating point/ double precision values +// { +// if(iItem==3) +// { +// double *pstDataR = NULL; +// double *pstDataG = NULL; +// double *pstDataB = NULL; +// //retrive the matrix of the R values +// sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// //retrive the matrix of the G values +// sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } +// //retrive the matrix of the B values +// sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers +// image = Mat(iRows,iCols,CV_64FC3); + +// //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this +// k=0; +// for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; +// image.at(i,j)[1]=pstDataG[i+iRows*j]; +// image.at(i,j)[0]=pstDataB[i+iRows*j]; +// } +// } +// } +// else +// { +// double *pstDataR = NULL; +// //retrive the matrix of the R values +// sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); +// if(sciErr.iErr) +// { +// printError(&sciErr, 0); +// return 0; +// } + +// //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers +// image = Mat(iRows,iCols,CV_64FC1); + +// //Assigning image matrix values to pixels +// k=0; +// for(i=0;i(i,j)=pstDataR[i+iRows*j]; +// } +// } +// return 1; +//} +//function used to retrieve image matrix from Scilab environment +/* + We need to retrieve image from the Scilab environment + We do this by getting a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int retrieveImage(Mat &image,int pos) +{ + + SciErr sciErr; + int iRows=0,iCols=0,i,j,k=0; + int *piAddr = NULL; + int *piAddrChild = NULL; + int iPrec = 0,iItem = 0; + + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + if(iItem==2) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC2); + + /*Now that we have the 2 matrices(R,G), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[1]=pstDataR[i+iRows*j]; + image.at(i,j)[0]=pstDataG[i+iRows*j]; + } + } + } + else if(iItem==3) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC3); + + /*Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else if(iItem==4) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + unsigned char *pstDataA = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the A values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 4, &iRows, &iCols, &pstDataA); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC4); + + /*Now that we have the 4 matrices(R,G,B,A), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[3]=pstDataA[i+iRows*j]; + image.at(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT8: //for signed integer 8 + { + if(iItem==2) + { + char *pstDataR = NULL; + char *pstDataG = NULL; + + //retrive the matrix of the R values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8SC2); + + /*Now that we have the 2 matrices(R,G), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[1]=pstDataR[i+iRows*j]; + image.at(i,j)[0]=pstDataG[i+iRows*j]; + } + } + } + else if(iItem==3) + { + char *pstDataR = NULL; + char *pstDataG = NULL; + char *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8SC3); + + /*Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else if(iItem==4) + { + char *pstDataR = NULL; + char *pstDataG = NULL; + char *pstDataB = NULL; + char *pstDataA = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the A values + sciErr = getMatrixOfInteger8InList(pvApiCtx, piAddr, 4, &iRows, &iCols, &pstDataA); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8SC4); + + /*Now that we have the 4 matrices(R,G,B,A), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[3]=pstDataA[i+iRows*j]; + image.at(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + if(iItem==2) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 16-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC2); + + //Now that we have the 2 matrices(R,G), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[1]=pstDataR[i+iRows*j]; + image.at(i,j)[0]=pstDataG[i+iRows*j]; + + } + } + } + else if(iItem==3) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + //i+iRows*j + } + } + } + + else if(iItem==4) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + short unsigned int *pstDataA = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the A values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 4, &iRows, &iCols, &pstDataA); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC4); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[3]=pstDataA[i+iRows*j]; + image.at(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short unsigned int *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC1); + + //Assigning matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT16: //for signed integer 16 + { + if(iItem==2) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC2); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[1]=pstDataR[i+iRows*j]; + image.at(i,j)[0]=pstDataG[i+iRows*j]; + } + } + } + else if(iItem==3) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else if(iItem==4) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + short int *pstDataA = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 4, &iRows, &iCols, &pstDataA); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC4); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[3]=pstDataA[i+iRows*j]; + image.at(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short int *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + + case SCI_INT32: //for signed integer 32 + { + if(iItem==2) + { + int *pstDataR = NULL; + int *pstDataG = NULL; + + //retrive the matrix of the R values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_32SC2); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[1]=pstDataR[i+iRows*j]; + image.at(i,j)[0]=pstDataG[i+iRows*j]; + } + } + } + else if(iItem==3) + { + int *pstDataR = NULL; + int *pstDataG = NULL; + int *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_32SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else if(iItem==4) + { + int *pstDataR = NULL; + int *pstDataG = NULL; + int *pstDataB = NULL; + int *pstDataA = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 4, &iRows, &iCols, &pstDataA); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_32SC4); + + //Now that we have the 4 matrices(R,G,B,A), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[3]=pstDataA[i+iRows*j]; + image.at(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + int *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_32SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + } + } + + + else //for floating point/ double precision values + { + if(iItem==2) + { + double *pstDataR = NULL; + double *pstDataG = NULL; + + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC2); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + + } + } + } + else if(iItem==3) + { + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC3); + + //Now that we have the 4 matrices(R,G,B,A), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + if(iItem==4) + { + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + double *pstDataA = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the A values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 4, &iRows, &iCols, &pstDataA); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC4); + + //Now that we have the 4 matrices(R,G,B,A), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[3]=pstDataA[i+iRows*j]; + image.at(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + double *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + } + return 1; +} + + +//function that returns an image matrix to the Scilab environment +/* + What we now need, is to pass the image matrix to the Scilab environment. + We do this by passing a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int returnImage(char *checker,Mat img,int pos) +{ + int i,j,k=0; + int *piAddrNew = NULL; + SciErr sciErr; + + int num=img.channels(); //num now contains no. of channels of the image + //creating the list that will be passed to the Scilab enviroment + if(num==1) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 1, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + else if(num==2) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 2, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + } + else if(num==3) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 3, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + else if(num==4) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 4, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + else + sciprint("Image channel number not supported"); + + + if(strcmp(checker,"8U")==0) //for Unsigned Integer 8 + { + + if(num==2) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + + //The next block of code retrieves the RG values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r [i+img.rows*j]=intensity.val[1]; + g [i+img.rows*j]=intensity.val[0]; + + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *b = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + + //The next block of code retrieves the RGBA values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + r [i+img.rows*j]=intensity.val[2]; + g [i+img.rows*j]=intensity.val[1]; + b [i+img.rows*j]=intensity.val[0]; + + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + + else if(num==4) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *b = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *a = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the RGBA values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + a [i+img.rows*j]=intensity.val[3]; + r [i+img.rows*j]=intensity.val[2]; + g [i+img.rows*j]=intensity.val[1]; + b [i+img.rows*j]=intensity.val[0]; + + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Adding the A value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 4, img.rows,img.cols, a); + free(a); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"8S")==0) //for signed Integer 8 + { + + if(num==2) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + char *r = (char *)malloc(img.rows * img.cols * sizeof(char)); + char *g = (char *)malloc(img.rows * img.cols * sizeof(char)); + + + //The next block of code retrieves the RG values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r [i+img.rows*j]=intensity.val[1]; + g [i+img.rows*j]=intensity.val[0]; + + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + } + + else if(num==3) + { + + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + char *r = (char *)malloc(img.rows * img.cols * sizeof(char)); + char *g = (char *)malloc(img.rows * img.cols * sizeof(char)); + char *b = (char *)malloc(img.rows * img.cols * sizeof(char)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r [i+img.rows*j]=intensity.val[2]; + g [i+img.rows*j]=intensity.val[1]; + b [i+img.rows*j]=intensity.val[0]; + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else if(num==4) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + char *r = (char *)malloc(img.rows * img.cols * sizeof(char)); + char *g = (char *)malloc(img.rows * img.cols * sizeof(char)); + char *b = (char *)malloc(img.rows * img.cols * sizeof(char)); + char *a = (char *)malloc(img.rows * img.cols * sizeof(char)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + a [i+img.rows*j]=intensity.val[3]; + r [i+img.rows*j]=intensity.val[2]; + g [i+img.rows*j]=intensity.val[1]; + b [i+img.rows*j]=intensity.val[0]; + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Adding the A value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 4, img.rows,img.cols, a); + free(a); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + char *r = (char *)malloc(img.rows * img.cols * sizeof(char)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"16U")==0) //for Unsigned Integer 16 + { + + if(num==2) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[1]; + g[i+img.rows*j]=intensity.val[0]; + + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + + else if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *b = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else if(num==4) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *b = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *a = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + a[i+img.rows*j]=intensity.val[3]; + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 4, img.rows,img.cols, a); + free(a); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + } + else if(strcmp(checker,"16S")==0) //for Signed Integer 16 + { + + if(num==2) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + } + + else if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *b = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else if(num==4) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *b = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *a = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + a[i+img.rows*j]=intensity.val[3]; + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the A value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 4, img.rows,img.cols,a); + free(a); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"32S")==0) //for Signed Integer 16 + { + + if(num==2) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + + else if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *b = (int *)malloc(img.rows * img.cols * sizeof(int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else if(num==4) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *b = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *a = (int *)malloc(img.rows * img.cols * sizeof(int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + a[i+img.rows*j]=intensity.val[3]; + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the A value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 4, img.rows,img.cols,a); + free(a); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + + else if(strcmp(checker,"32F")==0) //for Float 32 + { + + + if(num==2) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else if(num==4) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *a = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + a[i+img.rows*j]=intensity.val[3]; + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the A value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 4, img.rows,img.cols,a); + free(a); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + else if(strcmp(checker,"64F")==0) //For Float 64 + { + + if(num==2) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else if(num==4) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *a = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + a[i+img.rows*j]=intensity.val[3]; + r[i+img.rows*j]=intensity.val[2]; + g[i+img.rows*j]=intensity.val[1]; + b[i+img.rows*j]=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Adding the A value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 4, img.rows,img.cols, a); + free(a); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + +} diff --git a/sci_gateway1/cpp/common3.cpp b/sci_gateway1/cpp/common3.cpp new file mode 100644 index 0000000..45879a4 --- /dev/null +++ b/sci_gateway1/cpp/common3.cpp @@ -0,0 +1,925 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +//takes the matrix type as parameter and returns a string of the type +string type2str(int type) +{ + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + switch ( depth ) + { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + return r; +} + +//takes matrix type as parameter and returns no. of channels of the image matrix +int no_of_channels(int type) +{ + uchar chans = 1 + (type >> CV_CN_SHIFT); + return chans; +} + +//function used to retrieve image matrix from Scilab environment +/* + We need to retrieve image from the Scilab environment + We do this by getting a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int retrieveImage(Mat &image,int pos) +{ + + SciErr sciErr; + int iRows=0,iCols=0,i,j,k=0; + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piAddrChild = NULL; + int iPrec = 0,iItem = 0; + int r=0,c=0; + + //retrieving number of items in the list and type of data(integer/float) + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + + + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + + if(iItem==3) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciprint("testing"); + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(r=0;r(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + + if(iItem==3) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + //i+iRows*j + } + } + } + else + { + short unsigned int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC1); + + //Assigning matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT16: //for signed integer 16 + { + + if(iItem==3) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short int *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + } + } + else //for floating point/ double precision values + { + + if(iItem==3) + { + + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive address of the list + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + + } + else + { + + double *pstDataR = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx,pos,&piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr1, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + } + return 1; +} + +//function that returns an image matrix to the Scilab environment +/* + What we now need, is to pass the image matrix to the Scilab environment. + We do this by passing a List of 2-D matrices. The size of the list, in our case is known(either 3 or 1, + 3 for a coloured image and 1 for a grayscale image), hence we do not require the dynamic capability of a list. + Incase of a 3-channel image, the first matrix will be a matrix with dimensions img.rows X img.cols, and will denote the R(red) + values of the image. Similarly, the second will have the G(green) values, and the third will have B(blue) values. + For a single-channeled image, we will have only a single matrix +*/ +int returnImage(char *checker,Mat img,int pos) +{ + int i,j,k=0; + int *piAddrNew = NULL; + SciErr sciErr; + int num=no_of_channels(img.type()); //num now contains no. of channels of the image + //creating the list that will be passed to the Scilab enviroment + if(num==3) + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 3, &piAddrNew); + else + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + pos, 1, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(strcmp(checker,"8U")==0) //for Unsigned Integer 8 + { + if(num==3) + { + + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *g = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + unsigned char *b = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + j*img.rows + i)=intensity.val[2]; + *(g + j*img.rows + i)=intensity.val[1]; + *(b + j*img.rows + i)=intensity.val[0]; + } + } + + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + unsigned char *r = (unsigned char *)malloc(img.rows * img.cols * sizeof(unsigned char)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"16U")==0) //for Unsigned Integer 16 + { + + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *g = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + short unsigned int *b = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short unsigned int *r = (short unsigned int *)malloc(img.rows * img.cols * sizeof(short unsigned int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfUnsignedInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + } + else if(strcmp(checker,"16S")==0) //for Signed Integer 16 + { + + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *g = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + short int *b = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols, g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + short int *r = (short int *)malloc(img.rows * img.cols * sizeof(short int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger16InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + else if(strcmp(checker,"32S")==0) //for Signed Integer 16 + { + + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *g = (int *)malloc(img.rows * img.cols * sizeof(int)); + int *b = (int *)malloc(img.rows * img.cols * sizeof(int)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + int *r = (int *)malloc(img.rows * img.cols * sizeof(int)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfInteger32InList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + } + } + + else if(strcmp(checker,"32F")==0) //for Float 32 + { + if(num==3) + { + + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols,b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1,img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + else if(strcmp(checker,"64F")==0) //For Float 64 + { + + if(num==3) + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *g = (double *)malloc(img.rows * img.cols * sizeof(double)); + double *b = (double *)malloc(img.rows * img.cols * sizeof(double)); + //The next block of code retrieves the RGB values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + *(r + i*img.rows + j)=intensity.val[2]; + *(g + i*img.rows + j)=intensity.val[1]; + *(b + i*img.rows + j)=intensity.val[0]; + } + } + + //Adding the R value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols,r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the G value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 2, img.rows,img.cols,g); + free(g); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Adding the B value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 3, img.rows,img.cols, b); + free(b); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //Since we need to pass a single pointer as an arguement, for a 2-D matrix, we define it in this manner + double *r = (double *)malloc(img.rows * img.cols * sizeof(double)); + + //The next block of code retrieves the image colour values at a specified pixel, and assigns it to the matrices + for(i=0;i(i, j); + + //Adding the image colour value matrix to the list + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+pos , piAddrNew, 1, img.rows,img.cols, r); + free(r); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + } + +} diff --git a/sci_gateway1/cpp/libFOSSEE_Image_Processing_Toolbox.c b/sci_gateway1/cpp/libFOSSEE_Image_Processing_Toolbox.c new file mode 100644 index 0000000..a504d15 --- /dev/null +++ b/sci_gateway1/cpp/libFOSSEE_Image_Processing_Toolbox.c @@ -0,0 +1,30 @@ +#ifdef __cplusplus +extern "C" { +#endif +#include +#include +#include +#include +static int direct_gateway(char *fname,void F(void)) { F();return 0;}; +extern Gatefunc opencv_imread; +static GenericTable Tab[]={ + {(Myinterfun)sci_gateway,opencv_imread,"opencv_imread"}, +}; + +int C2F(libFOSSEE_Image_Processing_Toolbox)() +{ + Rhs = Max(0, Rhs); + if (*(Tab[Fin-1].f) != NULL) + { + if(pvApiCtx == NULL) + { + pvApiCtx = (StrCtx*)MALLOC(sizeof(StrCtx)); + } + pvApiCtx->pstName = (char*)Tab[Fin-1].name; + (*(Tab[Fin-1].f))(Tab[Fin-1].name,Tab[Fin-1].F); + } + return 0; +} +#ifdef __cplusplus +} +#endif diff --git a/sci_gateway1/cpp/libFOSSEE_Image_Processing_Toolbox.so b/sci_gateway1/cpp/libFOSSEE_Image_Processing_Toolbox.so new file mode 100755 index 0000000..3a6dc7a Binary files /dev/null and b/sci_gateway1/cpp/libFOSSEE_Image_Processing_Toolbox.so differ diff --git a/sci_gateway1/cpp/libskeleton_imageprocessing.c b/sci_gateway1/cpp/libskeleton_imageprocessing.c new file mode 100644 index 0000000..4fbf87c --- /dev/null +++ b/sci_gateway1/cpp/libskeleton_imageprocessing.c @@ -0,0 +1,38 @@ +#ifdef __cplusplus +extern "C" { +#endif +#include +#include +#include +#include +static int direct_gateway(char *fname,void F(void)) { F();return 0;}; +extern Gatefunc opencv_imread; +extern Gatefunc opencv_imcrop; +extern Gatefunc opencv_imresize; +extern Gatefunc opencv_imsharpen; +extern Gatefunc opencv_imwrite; +static GenericTable Tab[]={ + {(Myinterfun)sci_gateway_without_putlhsvar,opencv_imread,"opencv_imread"}, + {(Myinterfun)sci_gateway_without_putlhsvar,opencv_imcrop,"opencv_imcrop"}, + {(Myinterfun)sci_gateway_without_putlhsvar,opencv_imresize,"opencv_imresize"}, + {(Myinterfun)sci_gateway_without_putlhsvar,opencv_imsharpen,"opencv_imsharpen"}, + {(Myinterfun)sci_gateway_without_putlhsvar,opencv_imwrite,"opencv_imwrite"}, +}; + +int C2F(libskeleton_imageprocessing)() +{ + Rhs = Max(0, Rhs); + if (*(Tab[Fin-1].f) != NULL) + { + if(pvApiCtx == NULL) + { + pvApiCtx = (StrCtx*)MALLOC(sizeof(StrCtx)); + } + pvApiCtx->pstName = (char*)Tab[Fin-1].name; + (*(Tab[Fin-1].f))(Tab[Fin-1].name,Tab[Fin-1].F); + } + return 0; +} +#ifdef __cplusplus +} +#endif diff --git a/sci_gateway1/cpp/libskeleton_imageprocessing.so b/sci_gateway1/cpp/libskeleton_imageprocessing.so new file mode 100755 index 0000000..0902c8b Binary files /dev/null and b/sci_gateway1/cpp/libskeleton_imageprocessing.so differ diff --git a/sci_gateway1/cpp/loader.sce b/sci_gateway1/cpp/loader.sce new file mode 100644 index 0000000..15332b1 --- /dev/null +++ b/sci_gateway1/cpp/loader.sce @@ -0,0 +1,25 @@ +// This file is released under the 3-clause BSD license. See COPYING-BSD. +// Generated by builder.sce : Please, do not edit this file +// ---------------------------------------------------------------------------- +// +libskeleton_imagepr_path = get_absolute_file_path('loader.sce'); +// +// ulink previous function with same name +[bOK, ilib] = c_link('libskeleton_imageprocessing'); +if bOK then + ulink(ilib); +end +// +list_functions = [ 'opencv_imread'; + 'opencv_imcrop'; + 'opencv_imresize'; + 'opencv_imsharpen'; + 'opencv_imwrite'; +]; +addinter(libskeleton_imagepr_path + filesep() + 'libskeleton_imageprocessing' + getdynlibext(), 'libskeleton_imageprocessing', list_functions); +// remove temp. variables on stack +clear libskeleton_imagepr_path; +clear bOK; +clear ilib; +clear list_functions; +// ---------------------------------------------------------------------------- diff --git a/sci_gateway1/cpp/opencv_BRISKPoints.cpp b/sci_gateway1/cpp/opencv_BRISKPoints.cpp new file mode 100644 index 0000000..224686d --- /dev/null +++ b/sci_gateway1/cpp/opencv_BRISKPoints.cpp @@ -0,0 +1,113 @@ +/*************************************************** +Author : Shashank Shekhar +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_BRISKPoints(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int *piAddr = NULL; + int *piChild = NULL; + int iType = 0; + int iRows, iCols = 0; + double *matData = NULL; + + double *OrientationData = NULL; + double *MetricData = NULL; + double *ScaleData = NULL; + + CheckInputArgument(pvApiCtx, 1,1); + CheckOutputArgument(pvApiCtx,1,5); + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix || isVarComplex(pvApiCtx, piAddr)) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &matData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iCols!=2) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + OrientationData = (double *)malloc(sizeof(double)*iRows); + MetricData = (double *)malloc(sizeof(double)*iRows); + ScaleData = (double *)malloc(sizeof(double)*iRows); + + for(int i = 0 ; i +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/imgproc/imgproc.hpp" + +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" + //#include "../common.cpp" + vector > > madv, msev; + vector, pair > > v; + + int opencv_BlockMatcher(char *fname, unsigned long fname_len) + { + //Error management variable + SciErr sciErr; + + //Variable declaration + int maxm, itr; + int iRows = 0; + int iCols = 0; + int nbInputArguments = 0; + int *piLen = NULL; + int *piAddr = NULL; + bool *providedArgs = NULL; + char *currentArg = NULL; + char **pstData = NULL; + double *blocksize = NULL; + double *maximumDisplacement = NULL; + double *overlap = NULL; + double *horizontal = NULL; + double *vertical = NULL; + double *magnitude = NULL; + string s,t; + string matchCriteria; + string outputValue; + Mat img, img1, img2; + + //Check input output arguments + CheckInputArgument(pvApiCtx, 2, 12); + CheckOutputArgument(pvApiCtx, 1, 1); + + nbInputArguments = *getNbInputArgument(pvApiCtx); + + if((nbInputArguments-2)%2!=0) + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + providedArgs = (bool*)malloc(sizeof(bool) * 5); + for(int i=0;i<5;i++) + providedArgs[i] = 0; + + //Read image + //retrieveImage(img, 1); + //cvtColor(img, img1, CV_BGR2GRAY); + //retrieveImage(img, 2); + //cvtColor(img, img2, CV_BGR2GRAY); + + //Reading first image + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + img1 = imread(pstData[0], CV_LOAD_IMAGE_GRAYSCALE); + + //Reading second image + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + img2 = imread(pstData[0], CV_LOAD_IMAGE_GRAYSCALE); + + for(int iter=3;iter<=nbInputArguments;iter++) + { + // Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + // providedArgs[] makes sure that no optional argument is provided more than once + + if(strcmp(currentArg,"BlockSize")==0) + { + if(!providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, iter); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &blocksize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows!=1 || iCols!=2) + { + Scierror(999, "Incorrect dimension of matrix for argument BlockSize.\n"); + return 0; + } + if(blocksize[0]<=0 || blocksize[1]<=0) + { + Scierror(999, "Positive inputs for BlockSize needed.\n"); + return 0; + } + providedArgs[0]=1; + } + else if(providedArgs[0]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "Overlap")==0) + { + if(!providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, iter); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &overlap); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows!=1 || iCols!=2) + { + Scierror(999, "Incorrect dimension of matrix for argument Overlap.\n"); + return 0; + } + if(overlap[0]<0 || overlap[1]<0) + { + Scierror(999, "Non-negative inputs for Overlap needed\n"); + return 0; + } + providedArgs[1]=1; + + } + else if(providedArgs[1]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "MaximumDisplacement")==0) + { + if(!providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, iter); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &maximumDisplacement); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows!=1 || iCols!=2) + { + Scierror(999, "Incorrect dimension of matrix for argument MaximumDisplacement.\n"); + return 0; + } + if(maximumDisplacement[0]<0 || maximumDisplacement[1]<0) + { + Scierror(999, "Non-negative inputs for Overlap needed\n"); + return 0; + } + providedArgs[2]=1; + + } + else if(providedArgs[2]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "MatchCriteria")==0) + { + if(!providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp(pstData[0],"Mean square error")==0) + matchCriteria = "Mean Square Error"; + else if(strcmp(pstData[0], "Mean absolute difference")==0) + matchCriteria = " "; + else + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are Mean square error or Mean absolute difference.\n", currentArg); + return 0; + } + providedArgs[3]=1; + } + else if(providedArgs[3]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "OutputValue")==0) + { + if(!providedArgs[4]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp(pstData[0],"Magnitude-squared")==0) + outputValue = "Magnitude-squared"; + else if(strcmp(pstData[0], "Horizontal and vertical components")==0) + outputValue = "Horizontal and vertical components"; + else + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are Magnitude-squared or Horizontal and vertical components.\n", currentArg); + return 0; + } + providedArgs[4]=1; + } + else if(providedArgs[4]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + } + + //Initialisaton + if(!providedArgs[0]) + { + blocksize = (double*)malloc(sizeof(double) * 2); + blocksize[0] = 17; + blocksize[1] = 17; + } + + if(!providedArgs[1]) + { + overlap = (double*)malloc(sizeof(double) * 2); + overlap[0] = 0; + overlap[1] = 0; + } + + if(!providedArgs[2]) + { + maximumDisplacement = (double*)malloc(sizeof(double) * 2); + maximumDisplacement[0] = 7; + maximumDisplacement[1] = 7; + } + + if(!providedArgs[3]) + { + matchCriteria = "Mean Square Error"; + } + + if(!providedArgs[4]) + { + outputValue = "Magnitude-squared"; + } + + if(outputValue == "Magnitude-squared") + { + magnitude = (double*)malloc(sizeof(double) * int(img1.rows) * int(img1.cols)); + } + else + { + horizontal = (double*)malloc(sizeof(double) * int(img1.rows) * int(img1.cols)); + vertical = (double*)malloc(sizeof(double) * int(img1.rows) * int(img1.cols)); + } + + iRows = iCols = 0; + for(int i=0;i=int(img1.rows)) + break; + for(int j=0;j=int(img1.cols)) + break; + madv.clear(); + msev.clear(); + for(int k=-int(maximumDisplacement[0]);k<=int(maximumDisplacement[0]);k++) + { + if((i+k>=int(img2.rows)) || (i+k+int(blocksize[0])>=int(img2.rows))) + break; + if(i+k<0) + continue; + for(int l=-int(maximumDisplacement[1]);l<=int(maximumDisplacement[1]);l++) + { + if((j+l>=int(img2.cols)) || (j+l+int(blocksize[1])>=int(img2.cols))) + break; + if(j+l<0) + continue; + + long double mad = 0; + long double mse = 0; + + for(int it1=0;it1(i+it1,j+it2); + long double cur2=img2.at(i+k+it1,j+l+it2); + long double error=cur1-cur2; + mad+=abs(error); + mad/=blocksize[0]*blocksize[1]; + mse+=error*error; + mse/=blocksize[0]*blocksize[1]; + } + } + + madv.push_back(make_pair(mad,make_pair(i+k,j+l))); + msev.push_back(make_pair(mse,make_pair(i+k,j+l))); + } + } + if(outputValue == "Magnitude-squared") + { + sort(msev.begin(),msev.end()); + if(msev.empty()) + v.push_back(make_pair(make_pair(i,j),make_pair(i+int(blocksize[0]),j+int(blocksize[1])))); + else + v.push_back(make_pair(make_pair(i,j),msev[0].second)); + } + else + { + sort(madv.begin(),madv.end()); + if(madv.empty()) + v.push_back(make_pair(make_pair(i,j),make_pair(i+int(blocksize[0]),j+int(blocksize[1])))); + else + v.push_back(make_pair(make_pair(i,j),madv[0].second)); + } + iCols++; + } + iRows++; + } + + if(outputValue == "Magnitude-squared") + { + //Creating list to store magnitude matrix in it + magnitude = (double*)malloc(sizeof(double) * int (v.size())); + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_CascadeObjectDetector(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piLen = NULL; + char **object = NULL; + double *bboxes = NULL; + int i,j,k=0,l=0; + double alpha, beta,x,n; + + CascadeClassifier face_cascade; + CascadeClassifier eyes_cascade; + CascadeClassifier mouth_cascade; + CascadeClassifier nose_cascade; + CascadeClassifier upperbody_cascade; + + vectorfaces,eyes,mouth,nose,upperbody; + face_cascade.load("/home/diwakar/Desktop/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_alt.xml"); + eyes_cascade.load("/home/diwakar/Desktop/opencv-2.4.9/data/haarcascades/haarcascade_mcs_eyepair_big.xml"); + mouth_cascade.load("/home/diwakar/Desktop/opencv-2.4.9/data/haarcascades/haarcascade_mcs_mouth.xml"); + nose_cascade.load("/home/diwakar/Desktop/opencv-2.4.9/data/haarcascades/haarcascade_mcs_nose.xml"); + upperbody_cascade.load("/home/diwakar/Desktop/opencv-2.4.9/data/haarcascades/haarcascade_mcs_upperbody.xml"); + + float eye_x1,eye_y1,eye_x2,eye_y2,mouth_x1,mouth_y1,mouth_x2,mouth_y2,nose_x1,nose_y1,nose_x2,nose_y2; + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat image,new_image,image_gray; + + retrieveImage(image, 1); + + sciErr = getVarAddressFromPosition(pvApiCtx,2, &piAddr2); + + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + object= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + object[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen,object); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + int m; + double *bboxes = NULL; + bboxes = (double*)malloc(sizeof(double) * (int)answer.size() * 4); + int tbox=0; + //Converting image from rgb to gray scale image. + cvtColor( image, image_gray, CV_BGR2GRAY ); + //equalizing the histrogram of gray scale image + equalizeHist( image_gray, image_gray ); + + face_cascade.detectMultiScale( image, faces, 1.2,2,CV_HAAR_SCALE_IMAGE | CV_HAAR_DO_CANNY_PRUNING, cvSize(0,0), cvSize(255,255) ); + if(strcmp(object[0],"face")==0) + { + for( int i = 0; i < faces.size(); i++ ) + { +<<<<<<< HEAD + tbox = (int)faces.Size(); + bboxes[i + 0 * (int)faces.size()] = faces[i].x; + bboxes[i + 1 * (int)faces.size()] = faces[i].y; + bboxes[i + 2 * (int)faces.size()] = faces[i].width; + bboxes[i + 3 * (int)faces.size()] = faces[i].height; +======= +>>>>>>> c6d5c3d8dbf4db6da6d7ac081918599b6977960a + + Point point1( faces[i].x + faces[i].width, faces[i].y + faces[i].height ); + Point point2( faces[i].x, faces[i].y); + rectangle(image,point1,point2,cvScalar(0,255,0),1,16,0); +<<<<<<< HEAD + Mat faceROI = image_gray( faces[i] ); + + eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + + for( int j = 0; j < eyes.size(); j++ ) + { + Point point3( faces[i].x + eyes[j].x + eyes[j].width, faces[i].y + eyes[j].y + eyes[j].height ); + Point point4(faces[i].x + eyes[j].x, faces[i].y + eyes[j].y ); + + //rectangle(image,point3,point4,cvScalar(0,0,255),1,8,0); + } + eye_x1=faces[0].x + eyes[0].x + eyes[0].width; + eye_y1=faces[0].y + eyes[0].y + eyes[0].height; + eye_x2=faces[0].x + eyes[0].x; + eye_y2=faces[0].y + eyes[0].y; + + nose_cascade.detectMultiScale( faceROI, nose, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + for( int j = 0; j < nose.size(); j++ ) + { + if((faces[i].x +nose[j].x + nose[j].width)eye_y1) + if((faces[i].x +nose[j].x)>eye_x2) + { + Point point7(faces[i].x +nose[j].x + nose[j].width,faces[i].y + nose[j].y + nose[j].height ); + Point point8(faces[i].x +nose[j].x,faces[i].y+nose[j].y ); + + //rectangle(image,point7,point8,cvScalar(255,255,0),1,8,0); + m=j; + + } + } + + nose_x1=faces[0].x + nose[m].x + nose[m].width; + nose_y1=faces[0].y + nose[m].y + nose[m].height; + nose_x2=faces[0].x + nose[m].x; + nose_y2=faces[0].y + nose[m].y; + + mouth_cascade.detectMultiScale( faceROI, mouth, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + for( int j = 0; j < mouth.size(); j++ ) + { + + if((faces[i].x +mouth[j].x + mouth[j].width)eye_x2) + { + if((faces[i].y + mouth[j].y + mouth[j].height)>(faces[i].y+nose[m].y +nose[m].height)) + { + Point point5(faces[i].x +mouth[j].x + mouth[j].width,faces[i].y + mouth[j].y + mouth[j].height ); + Point point6(faces[i].x +mouth[j].x,faces[i].y + mouth[j].y ); + + //rectangle(image,point5,point6,cvScalar(0,255,0),1,8,0); + m=j; + } + } + } + mouth_x1=faces[0].x + mouth[m].x + mouth[m].width; + mouth_y1=faces[0].y + mouth[m].y + mouth[m].height; + mouth_x2=faces[0].x + mouth[m].x; + mouth_y2=faces[0].y + mouth[m].y; +======= +>>>>>>> c6d5c3d8dbf4db6da6d7ac081918599b6977960a + + } + } + else if(strcmp(object[0],"eyes")==0) + { + for( int i = 0; i < faces.size(); i++ ) + { + Point point1( faces[i].x + faces[i].width, faces[i].y + faces[i].height ); + Point point2( faces[i].x, faces[i].y); + //rectangle(image,point1,point2,cvScalar(0,255,0),1,16,0); + Mat faceROI = image_gray( faces[i] ); + + eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + + for( int j = 0; j < eyes.size(); j++ ) + { + Point point3( faces[i].x + eyes[j].x + eyes[j].width, faces[i].y + eyes[j].y + eyes[j].height ); + Point point4(faces[i].x + eyes[j].x, faces[i].y + eyes[j].y ); + + rectangle(image,point3,point4,cvScalar(0,0,255),1,8,0); + } + eye_x1=faces[0].x + eyes[0].x + eyes[0].width; + eye_y1=faces[0].y + eyes[0].y + eyes[0].height; + eye_x2=faces[0].x + eyes[0].x; + eye_y2=faces[0].y + eyes[0].y; + + nose_cascade.detectMultiScale( faceROI, nose, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + for( int j = 0; j < nose.size(); j++ ) + { + if((faces[i].x +nose[j].x + nose[j].width)eye_y1) + if((faces[i].x +nose[j].x)>eye_x2) + { + Point point7(faces[i].x +nose[j].x + nose[j].width,faces[i].y + nose[j].y + nose[j].height ); + Point point8(faces[i].x +nose[j].x,faces[i].y+nose[j].y ); + + //rectangle(image,point7,point8,cvScalar(255,255,0),1,8,0); + m=j; + + } + } + + nose_x1=faces[0].x + nose[m].x + nose[m].width; + nose_y1=faces[0].y + nose[m].y + nose[m].height; + nose_x2=faces[0].x + nose[m].x; + nose_y2=faces[0].y + nose[m].y; + + mouth_cascade.detectMultiScale( faceROI, mouth, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + for( int j = 0; j < mouth.size(); j++ ) + { + + if((faces[i].x +mouth[j].x + mouth[j].width)eye_x2) + { + if((faces[i].y + mouth[j].y + mouth[j].height)>(faces[i].y+nose[m].y +nose[m].height)) + { + Point point5(faces[i].x +mouth[j].x + mouth[j].width,faces[i].y + mouth[j].y + mouth[j].height ); + Point point6(faces[i].x +mouth[j].x,faces[i].y + mouth[j].y ); + + //rectangle(image,point5,point6,cvScalar(0,255,0),1,8,0); + m=j; + } + } + } + mouth_x1=faces[0].x + mouth[m].x + mouth[m].width; + mouth_y1=faces[0].y + mouth[m].y + mouth[m].height; + mouth_x2=faces[0].x + mouth[m].x; + mouth_y2=faces[0].y + mouth[m].y; + + } + } + else if(strcmp(object[0],"nose")==0) + { + for( int i = 0; i < faces.size(); i++ ) + { + Point point1( faces[i].x + faces[i].width, faces[i].y + faces[i].height ); + Point point2( faces[i].x, faces[i].y); + //rectangle(image,point1,point2,cvScalar(0,255,0),1,16,0); + Mat faceROI = image_gray( faces[i] ); + + eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + + for( int j = 0; j < eyes.size(); j++ ) + { + Point point3( faces[i].x + eyes[j].x + eyes[j].width, faces[i].y + eyes[j].y + eyes[j].height ); + Point point4(faces[i].x + eyes[j].x, faces[i].y + eyes[j].y ); + + //rectangle(image,point3,point4,cvScalar(0,0,255),1,8,0); + } + eye_x1=faces[0].x + eyes[0].x + eyes[0].width; + eye_y1=faces[0].y + eyes[0].y + eyes[0].height; + eye_x2=faces[0].x + eyes[0].x; + eye_y2=faces[0].y + eyes[0].y; + + nose_cascade.detectMultiScale( faceROI, nose, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + for( int j = 0; j < nose.size(); j++ ) + { + if((faces[i].x +nose[j].x + nose[j].width)eye_y1) + if((faces[i].x +nose[j].x)>eye_x2) + { + Point point7(faces[i].x +nose[j].x + nose[j].width,faces[i].y + nose[j].y + nose[j].height ); + Point point8(faces[i].x +nose[j].x,faces[i].y+nose[j].y ); + + rectangle(image,point7,point8,cvScalar(255,255,0),1,8,0); + m=j; + + } + } + + nose_x1=faces[0].x + nose[m].x + nose[m].width; + nose_y1=faces[0].y + nose[m].y + nose[m].height; + nose_x2=faces[0].x + nose[m].x; + nose_y2=faces[0].y + nose[m].y; + + mouth_cascade.detectMultiScale( faceROI, mouth, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + for( int j = 0; j < mouth.size(); j++ ) + { + + if((faces[i].x +mouth[j].x + mouth[j].width)eye_x2) + { + if((faces[i].y + mouth[j].y + mouth[j].height)>(faces[i].y+nose[m].y +nose[m].height)) + { + Point point5(faces[i].x +mouth[j].x + mouth[j].width,faces[i].y + mouth[j].y + mouth[j].height ); + Point point6(faces[i].x +mouth[j].x,faces[i].y + mouth[j].y ); + //rectangle(image,point5,point6,cvScalar(0,255,0),1,8,0); + m=j; + } + } + } + mouth_x1=faces[0].x + mouth[m].x + mouth[m].width; + mouth_y1=faces[0].y + mouth[m].y + mouth[m].height; + mouth_x2=faces[0].x + mouth[m].x; + mouth_y2=faces[0].y + mouth[m].y; + + } + } + else if(strcmp(object[0],"mouth")==0) + { + for( int i = 0; i < faces.size(); i++ ) + { + Point point1( faces[i].x + faces[i].width, faces[i].y + faces[i].height ); + Point point2( faces[i].x, faces[i].y); + //rectangle(image,point1,point2,cvScalar(0,255,0),1,16,0); + Mat faceROI = image_gray( faces[i] ); + + eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + + for( int j = 0; j < eyes.size(); j++ ) + { + Point point3( faces[i].x + eyes[j].x + eyes[j].width, faces[i].y + eyes[j].y + eyes[j].height ); + Point point4(faces[i].x + eyes[j].x, faces[i].y + eyes[j].y ); + + //rectangle(image,point3,point4,cvScalar(0,0,255),1,8,0); + } + eye_x1=faces[0].x + eyes[0].x + eyes[0].width; + eye_y1=faces[0].y + eyes[0].y + eyes[0].height; + eye_x2=faces[0].x + eyes[0].x; + eye_y2=faces[0].y + eyes[0].y; + + nose_cascade.detectMultiScale( faceROI, nose, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + for( int j = 0; j < nose.size(); j++ ) + { + if((faces[i].x +nose[j].x + nose[j].width)eye_y1) + if((faces[i].x +nose[j].x)>eye_x2) + { + Point point7(faces[i].x +nose[j].x + nose[j].width,faces[i].y + nose[j].y + nose[j].height ); + Point point8(faces[i].x +nose[j].x,faces[i].y+nose[j].y ); + + //rectangle(image,point7,point8,cvScalar(255,255,0),1,8,0); + m=j; + + } + } + + nose_x1=faces[0].x + nose[m].x + nose[m].width; + nose_y1=faces[0].y + nose[m].y + nose[m].height; + nose_x2=faces[0].x + nose[m].x; + nose_y2=faces[0].y + nose[m].y; + mouth_cascade.detectMultiScale( faceROI, mouth, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) ); + for( int j = 0; j < mouth.size(); j++ ) + { + if((faces[i].x +mouth[j].x + mouth[j].width)eye_x2) + { + if((faces[i].y + mouth[j].y + mouth[j].height)>(faces[i].y+nose[m].y +nose[m].height)) + { + Point point5(faces[i].x +mouth[j].x + mouth[j].width,faces[i].y + mouth[j].y + mouth[j].height ); + Point point6(faces[i].x +mouth[j].x,faces[i].y + mouth[j].y ); + //if(strcmp(object[0],"mouth")==0) + rectangle(image,point5,point6,cvScalar(0,255,0),1,8,0); + m=j; + } + } + } + mouth_x1=faces[0].x + mouth[m].x + mouth[m].width; + mouth_y1=faces[0].y + mouth[m].y + mouth[m].height; + mouth_x2=faces[0].x + mouth[m].x; + mouth_y2=faces[0].y + mouth[m].y; + + } + } + else if(strcmp(object[0],"upper_body")==0) + { + upperbody_cascade.detectMultiScale( image_gray, upperbody, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(255, 255) ); + + for( int i = 0; i < upperbody.size(); i++ ) + { + Rect t = upperbody[i]; + t.y += 100; + rectangle(image, t, Scalar(255,255,255), 4, 8); + } + } + else + { + sciprint("Wrong object type"); + + } + + //new_image=image; + + + +<<<<<<< HEAD + /*int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); */ + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, tbox, 4, bboxes); +======= + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,image,1); + free(checker); + /*sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, tbox, 4, bboxes); +>>>>>>> c6d5c3d8dbf4db6da6d7ac081918599b6977960a + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_DCT.cpp b/sci_gateway1/cpp/opencv_DCT.cpp new file mode 100644 index 0000000..087c61b --- /dev/null +++ b/sci_gateway1/cpp/opencv_DCT.cpp @@ -0,0 +1,77 @@ +/******************************************************** +Author: Deepshikha +return_image = DCT(source_image) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_DCT(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j; + int flags = 0; + + /// checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + + Mat image; + retrieveImage(image, 1); + + if(image.channels() != 1){ + Scierror(999, "Input should be single channel\n"); + return 0; + } + + /// even sized rows and columns required + if(image.rows%2 != 0){ + Scierror(999, "%s: Odd number of rows found\n"); + return 0; + } + + if(image.cols%2 != 0){ + Scierror(999, "%s: Odd number of columns found\n"); + return 0; + } + + /// transformed_idct stores the transformed image + Mat new_image(image.rows, image.cols, image.type()); + dct(image, new_image, flags); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway1/cpp/opencv_Deinterlacer.cpp b/sci_gateway1/cpp/opencv_Deinterlacer.cpp new file mode 100644 index 0000000..04db27f --- /dev/null +++ b/sci_gateway1/cpp/opencv_Deinterlacer.cpp @@ -0,0 +1,201 @@ +/******************************************************** +Author: Deepshikha +return_image = Deinterlacer(source_image) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_Deinterlacer(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j, k , ch; + int iRows=0; + int iCols=0; + int *piAddr2=NULL; + int *piLen = 0; + + // to specify deinterlacing method + char **method=NULL; + + /// checking input argument + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + vector v; + + Mat image; + retrieveImage(image, 1); + + /// new_image contains deinterlaced image of original image + Mat new_image(image.rows, image.cols, image.type()); + + int nbInputArguments = *getNbInputArgument(pvApiCtx); + if(nbInputArguments == 2) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + /// Retrieve string from input parameter. (requires 3 calls) + /// first to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //second call to retrieve length of each string + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + method = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + method[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // if second argument, method is Linear interpolation + if(strcmp(method[0],"Linear interpolation")==0) + { + for(i = 0; i < image.rows; i++) + { + for(j = 0; j < image.cols; j++) + { + if(i == image.rows-1) + { + if(i%2!=0) + new_image.at (i,j) = image.at (i-1,j); + else + new_image.at (i,j) = image.at (i,j); + } + else if(i%2 != 0 and i+1 < image.rows and i-1 >= 0) + new_image.at (i,j) = ( image.at (i-1,j) + image.at (i+1,j) ) / 2; + else + new_image.at (i,j) = image.at (i,j); + } + } + } + + // if second argument, method is Vertical temporal median filtering + else if(strcmp(method[0],"Vertical temporal median filtering")==0) + { + for(i = 0; i < image.rows; i++) + { + for(j = 0;j < image.cols; j++) + { + v.clear(); + if(i == image.rows-1) + { + if(i%2!=0) + new_image.at (i,j) = image.at (i-1,j); + else + new_image.at (i,j) = image.at (i,j); + } + else if(i%2 != 0 and i-1 >= 0 and i+1 < image.rows) + { + v.push_back( image.at (i-1,j) ); + v.push_back( image.at (i,j) ); + v.push_back( image.at (i+1,j) ); + + sort( v.begin(),v.end() ); // to find median + + new_image.at (i,j) = v[1]; + } + else + new_image.at (i,j) = image.at (i,j); + } + } + } + + // if second argument, method is Line repetition + else if(strcmp(method[0],"Line repetition")==0) + { + for( i = 0; i < image.rows; i++ ) + { + for(j = 0; j < image.cols; j++ ) + { + if( i == image.rows-1 and i%2!=0 ) + new_image.at (i,j,k) = image.at (i,j,k); + if( i%2 != 0 and i-1 >=0 ) + new_image.at (i,j,k) = image.at (i-1,j,k); + else + new_image.at (i,j, k) = image.at (i,j,k); + } + } + } + // if any other invalid method name is passed as argument + else + { + Scierror(99,"Wrong method specified, use Linear interpolation or Vertical temporal median filtering or Line repetition as method name \n"); + return 0; + } + } + + // if no of input argument is 1 that is no method is specified then we use Line repetition method by default + else + { + for( i = 0; i < image.rows; i++ ) + { + for(j = 0; j < image.cols; j++ ) + { + if( i == image.rows-1 and i%2!=0 ) + new_image.at (i,j,k) = image.at (i,j,k); + if( i%2 != 0 and i-1 >=0 ) + new_image.at (i,j,k) = image.at (i-1,j,k); + else + new_image.at (i,j, k) = image.at (i,j,k); + } + } + } + + // writing to scilab memory + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } +/* ==================================================================== */ +} + diff --git a/sci_gateway1/cpp/opencv_FFT.cpp b/sci_gateway1/cpp/opencv_FFT.cpp new file mode 100644 index 0000000..1ac386e --- /dev/null +++ b/sci_gateway1/cpp/opencv_FFT.cpp @@ -0,0 +1,89 @@ +/**************************************** +Author: Sridhar Reddy +*****************************************/ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_fftnew(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0,i,j; + int *piAddr1=NULL; + double *ipReal=NULL; + double *ipImg=NULL; + double *rsltReal=NULL; + double *rsltImg=NULL; + vector > vec_out; + vector vec_in; + + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols, &ipReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + ipImg = (double*)malloc(sizeof(double) * iRows * iCols); + if(iRows==1){ + for(i = 0 ; i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + //#include "../common.cpp" + + bool fdAdaptLearningRate; + double fdNumTrainingFrames=-1; + double fdLearningRate=-1; + double fdMinimumBackgroundRatio=-1; + double fdNumGaussians=-1; + int iBool; + bool objectStatus; + Ptr fdObj; + + int opencv_ForegroundDetector(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int *piAddr = NULL; + int *piAddrVal = NULL; + int iRows=0; + int iCols=0; + int flag; + int *piLen = NULL; + char **pstData = NULL; + char *currentArg = NULL; + int noOfarguments; + double tempLearningRate; + Ptr obj; + int val_position; + int iv_bool=false; + int argPresence[6]; + for(int i=0;i<6;i++) + argPresence[i]=0; + + CheckInputArgument(pvApiCtx, 0,10); + CheckOutputArgument(pvApiCtx,1,5); + + noOfarguments = *getNbInputArgument(pvApiCtx); + + if(noOfarguments%2!=0) + { + Scierror(999," Invalid No of Arguments \n"); + return 0; + } + +//----------------------------------------------------------------- Retrieving Arguments ------------------------------------------------------------------------------- + + for(int i=1;i<=noOfarguments;i+=2) + { + + // first get the property + sciErr = getVarAddressFromPosition(pvApiCtx,i,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isStringType(pvApiCtx, piAddr)) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + //first call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j = 0 ; j < iRows * iCols ; j++) + { + pstData[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + currentArg = pstData[0]; + + if(strcmp(currentArg, "AdaptLearningRate")==0) + { + val_position = i+1; + + if(argPresence[0]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isBooleanType(pvApiCtx, piAddrVal)) + { + Scierror(999,"Invalid Value for AdaptLearningRate. A boolean value expected.\n"); + return 0; + } + + if(!isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999,"Invalid Value for AdaptLearningRate. A boolean value expected.\n"); + return 0; + } + getScalarBoolean(pvApiCtx, piAddrVal, &iBool); + if(iBool == 0) + fdAdaptLearningRate = false; + else + fdAdaptLearningRate = true; + + argPresence[0]=1; + + } + else if(strcmp(currentArg, "NumTrainingFrames")==0) + { + val_position=i+1; + + if(argPresence[1]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for NumTrainingFrames.\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &fdNumTrainingFrames); + + if(fdNumTrainingFrames<0) + { + Scierror(999," Invalid Value for NumTrainingFrames. Enter a positive value\n"); + return 0; + } + argPresence[1]=1; + } + else if(strcmp(currentArg, "LearningRate")==0) + { + val_position=i+1; + + if(argPresence[2]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for LearningRate.\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &tempLearningRate); + + if(tempLearningRate <0) + { + Scierror(999," Invalid Value for LearningRate. Enter a positive value\n"); + return 0; + } + argPresence[2]=1; + } + else if(strcmp(currentArg, "MinimumBackgroundRatio")==0) + { + val_position=i+1; + + if(argPresence[3]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for MinimumBackgroundRatio.\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &fdMinimumBackgroundRatio); + + if(fdMinimumBackgroundRatio<0) + { + Scierror(999," Invalid Value for MinimumBackgroundRatio. Enter a positive value\n"); + return 0; + } + argPresence[3]=1; + + } + else if(strcmp(currentArg, "NumGaussians")==0) + { + val_position=i+1; + + if(argPresence[4]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for NumGaussians.\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &fdNumGaussians); + + if(fdNumGaussians<0) + { + Scierror(999," Invalid Value for NumGaussians. Enter a positive value\n"); + return 0; + } + argPresence[4]=1; + + } + else + { + Scierror(999,"Invalid Argument %s\n", currentArg); + return 0; + } + } +// --------------------------------------------- End of OIA -------------------------------------------------------- + if(argPresence[1]==0) + fdNumTrainingFrames = 150; + if(argPresence[2]==0) + fdLearningRate = 0.005; + if(argPresence[3]==0) + fdMinimumBackgroundRatio = 0.7; + if(argPresence[4]==0) + fdNumGaussians = 5; + + fdObj = new BackgroundSubtractorMOG(fdNumTrainingFrames,fdNumGaussians,fdMinimumBackgroundRatio); + objectStatus = true; +// ------------------------------------------------------------------------------------------------------------------ + if(fdAdaptLearningRate==true) + createScalarBoolean(pvApiCtx, nbInputArgument(pvApiCtx)+1, 1); + else + createScalarBoolean(pvApiCtx, nbInputArgument(pvApiCtx)+1, 0); + + createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+2, fdNumTrainingFrames); + createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+3, fdMinimumBackgroundRatio); + createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+4, fdNumGaussians); + createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+5, fdLearningRate); + + for(int i=1;i<=5;i++) + { + AssignOutputVariable(pvApiCtx, i) = nbInputArgument(pvApiCtx) + i; + } + + ReturnArguments(pvApiCtx); + return 0; + } + + int opencv_readForegroundMask(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int *piAddr = NULL; + int *outList = NULL; + unsigned char *red = NULL; + unsigned char *green = NULL; + unsigned char *blue = NULL; + + if(!objectStatus) + { + Scierror(999,"Set up Foreground Detection properties through Foreground Detector first.\n"); + return 0; + } + CheckInputArgument(pvApiCtx, 1,1); + CheckOutputArgument(pvApiCtx,1,1); + Mat frame; + Mat foregoundMask; + retrieveImage(frame,1); + fdObj->operator()(frame, foregoundMask, fdLearningRate); + + if(foregoundMask.channels()==1) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + red = (unsigned char *)malloc(sizeof(unsigned char)*foregoundMask.rows*foregoundMask.cols); + + for(int k=0;k(k, p); + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, foregoundMask.rows, foregoundMask.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + } + else + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + red = (unsigned char *)malloc(sizeof(unsigned char)*foregoundMask.rows*foregoundMask.cols); + green = (unsigned char *)malloc(sizeof(unsigned char)*foregoundMask.rows*foregoundMask.cols); + blue = (unsigned char *)malloc(sizeof(unsigned char)*foregoundMask.rows*foregoundMask.cols); + + for(int k=0;k(k, p); + red[k+foregoundMask.rows*p]=intensity.val[2]; + green[k+foregoundMask.rows*p]=intensity.val[1]; + blue[k+foregoundMask.rows*p]=intensity.val[0]; + } + } + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, foregoundMask.rows, foregoundMask.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 2, foregoundMask.rows, foregoundMask.cols, green); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 3, foregoundMask.rows, foregoundMask.cols, blue); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + free(green); + free(blue); + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + + return 0; + } + + +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_GeometricShearer.cpp b/sci_gateway1/cpp/opencv_GeometricShearer.cpp new file mode 100644 index 0000000..382de19 --- /dev/null +++ b/sci_gateway1/cpp/opencv_GeometricShearer.cpp @@ -0,0 +1,178 @@ +/******************************************************** +Author: Diwakar Bhardwaj +********************************************************* +Usage :return_image=GeometricShearer(input_image,'direction',value) +value is the amount want to shear the image in given direction +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_GeometricShearer(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + char **direction = NULL; + int i,j,k; + double alpha, beta,x,n,value; + + + + //checking input argument + CheckInputArgument(pvApiCtx,3,3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat image,new_image; + + retrieveImage(image, 1); + + sciErr = getVarAddressFromPosition(pvApiCtx,2, &piAddr2); + + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + direction = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + direction[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, direction); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&value); + if(intErr) + return intErr; + + int r1, c1; // tranformed point + int rows, cols; // original image rows and columns + rows = image.rows; + cols = image.cols; + if(strcmp(direction[0], "Horizontal") == 0) + { + double Bx = value; + double By = 0; + + int maxXOffset = abs(cols * Bx); + int maxYOffset = abs(rows * By); + + Mat out = Mat::ones(image.rows+maxYOffset, image.cols+maxXOffset, image.type()); + + for(int r = 0; r < out.rows; r++) + { + for(int c = 0; c < out.cols; c++) + { + r1 = r + c * By - maxYOffset; + c1 = r * Bx + c - maxXOffset; + + if(r1 >= 0 && r1 <= rows && c1 >= 0 && c1 <= cols) + { + out.at(r, c) = image.at(r1, c1); + } + + } + } + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(out.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,out,1); + free(checker); + } + else if(strcmp(direction[0], "Vertical") == 0) + { + double Bx = 0; + double By = value; + + int maxXOffset = abs(cols * Bx); + int maxYOffset = abs(rows * By); + + Mat out = Mat::ones(image.rows+maxYOffset, image.cols+maxXOffset, image.type()); + + for(int r = 0; r < out.rows; r++) + { + for(int c = 0; c < out.cols; c++) + { + r1 = r + c * By - maxYOffset; + c1 = r * Bx + c - maxXOffset; + + if(r1 >= 0 && r1 <= rows && c1 >= 0 && c1 <= cols) + { + out.at(r, c) = image.at(r1, c1); + } + + } + } + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(out.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,out,1); + free(checker); + + } + else + { + sciprint("Not valid 2nd argument"); + } + + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_HistogramBasedTracker.cpp b/sci_gateway1/cpp/opencv_HistogramBasedTracker.cpp new file mode 100644 index 0000000..6d67886 --- /dev/null +++ b/sci_gateway1/cpp/opencv_HistogramBasedTracker.cpp @@ -0,0 +1,257 @@ +/*************************************************** +Author : Tanmay Chaudhari + ***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" +//#include "../common.cpp" + + Mat image; + bool backprojMode = false; + bool selectObject = false; + int trackObject = 0; + Point origin; + Rect selection; + int vmin = 10, vmax = 256, smin = 30; + double xcord[1<<20]; + double ycord[1<<20]; + double width[1<<20]; + double height[1<<20]; + double orientation[1<<20]; + + static void onMouse( int event, int x, int y, int, void* ) + { + if( selectObject ) + { + selection.x = MIN(x, origin.x); + selection.y = MIN(y, origin.y); + selection.width = std::abs(x - origin.x); + selection.height = std::abs(y - origin.y); + + selection &= Rect(0, 0, image.cols, image.rows); + } + + switch( event ) + { + case EVENT_LBUTTONDOWN: + origin = Point(x,y); + selection = Rect(x,y,0,0); + selectObject = true; + break; + case EVENT_LBUTTONUP: + selectObject = false; + if( selection.width > 0 && selection.height > 0 ) + trackObject = -1; + break; + } + } + + int opencv_HistogramBasedTracker() + { + SciErr sciErr; + int* piAddr = NULL; + int iRows = 0; + int iter = 0; + int iCols = 0; + int iRet = 0; + char** pstData = NULL; + + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 2); + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + iRet = getAllocatedMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, &pstData); + if(iRet) + { + freeAllocatedMatrixOfString(iRows, iCols, pstData); + return iRet; + } + sciErr = createMatrixOfString(pvApiCtx, nbInputArgument(pvApiCtx) + 1, iRows, iCols, pstData); + if(sciErr.iErr) + { + freeAllocatedMatrixOfString(iRows, iCols, pstData); + printError(&sciErr, 0); + return sciErr.iErr; + } + + VideoCapture cap(pstData[0]); + Rect trackWindow; + int hsize = 16; + float hranges[] = {0,180}; + const float* phranges = hranges; + namedWindow( "Video Player", 0 ); + setMouseCallback( "Video Player", onMouse, 0 ); + + Mat frame, hsv, hue, mask, hist, histimg = Mat::zeros(200, 320, CV_8UC3), backproj; + bool paused = false; + + for(;;) + { + if( !paused) + { + cap >> frame; + if( frame.empty() ) + break; + } + + frame.copyTo(image); + + if( !paused ) + { + cvtColor(image, hsv, COLOR_BGR2HSV); + + if( trackObject ) + { + int _vmin = vmin, _vmax = vmax; + + inRange(hsv, Scalar(0, smin, MIN(_vmin,_vmax)), + Scalar(180, 256, MAX(_vmin, _vmax)), mask); + int ch[] = {0, 0}; + hue.create(hsv.size(), hsv.depth()); + mixChannels(&hsv, 1, &hue, 1, ch, 1); + + if( trackObject < 0 ) + { + Mat roi(hue, selection), maskroi(mask, selection); + calcHist(&roi, 1, 0, maskroi, hist, 1, &hsize, &phranges); + normalize(hist, hist, 0, 255, NORM_MINMAX); + + trackWindow = selection; + trackObject = 1; + + histimg = Scalar::all(0); + int binW = histimg.cols / hsize; + Mat buf(1, hsize, CV_8UC3); + for( int i = 0; i < hsize; i++ ) + buf.at(i) = Vec3b(saturate_cast(i*180./hsize), 255, 255); + cvtColor(buf, buf, COLOR_HSV2BGR); + + for( int i = 0; i < hsize; i++ ) + { + int val = saturate_cast(hist.at(i)*histimg.rows/255); + rectangle( histimg, Point(i*binW,histimg.rows), + Point((i+1)*binW,histimg.rows - val), + Scalar(buf.at(i)), -1, 8 ); + } + } + + calcBackProject(&hue, 1, 0, hist, backproj, &phranges); + backproj &= mask; + RotatedRect trackBox = CamShift(backproj, trackWindow, + TermCriteria( TermCriteria::EPS | TermCriteria::COUNT, 10, 1 )); + if( trackWindow.area() <= 1 ) + { + int cols = backproj.cols, rows = backproj.rows, r = (MIN(cols, rows) + 5)/6; + trackWindow = Rect(trackWindow.x - r, trackWindow.y - r, + trackWindow.x + r, trackWindow.y + r) & + Rect(0, 0, cols, rows); + } + + if( backprojMode ) + cvtColor( backproj, image, COLOR_GRAY2BGR ); + ellipse( image, trackBox, Scalar(0,0,255), 3, 16 ); + xcord[iter] = trackBox.center.x; + ycord[iter] = trackBox.center.y; + width[iter] = trackBox.size.width; + height[iter] = trackBox.size.height; + orientation[iter] = trackBox.angle; + iter++; + } + + } + else if( trackObject < 0 ) + paused = false; + + if( selectObject && selection.width > 0 && selection.height > 0 ) + { + Mat roi(image, selection); + bitwise_not(roi, roi); + } + imshow( "Video Player", image ); + char c = (char)waitKey(20); + if( c == 27 ) + break; + else if(c == 32) + paused = !paused; + else; + waitKey(20); + } + destroyAllWindows(); + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 1, iter, xcord); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, 1, iter, ycord); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 3, 1, iter, width); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 4, 1, iter, height); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, piAddr, 1, 1, iter, orientation); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + ReturnArguments(pvApiCtx); + backprojMode = false; + selectObject = false; + trackObject = 0; + return 0; + } + /* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_IDCT.cpp b/sci_gateway1/cpp/opencv_IDCT.cpp new file mode 100644 index 0000000..a690982 --- /dev/null +++ b/sci_gateway1/cpp/opencv_IDCT.cpp @@ -0,0 +1,72 @@ +/******************************************************** +Author: Suraj Prakash +return_image = IDCT(source_image) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_IDCT(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j; + + int flags = 0; + + /// checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + + Mat image; + retrieveImage(image, 1); + + if(image.channels() != 1){ + Scierror(999, "%s: Image should be single channel\n"); + return 0; + } + /// even sized rows and columns required + if(image.rows%2 != 0 or image.cols%2 != 0){ + Scierror(999, "%s: Either of rows size or column size is odd\n"); + return 0; + } + + /// transformed_idct stores the transformed image + Mat transformed_idct(image.rows, image.cols, image.type()); + idct(image, transformed_idct, flags); + + string tempstring = type2str(transformed_idct.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, transformed_idct, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway1/cpp/opencv_IFFT.cpp b/sci_gateway1/cpp/opencv_IFFT.cpp new file mode 100644 index 0000000..6bbbcbc --- /dev/null +++ b/sci_gateway1/cpp/opencv_IFFT.cpp @@ -0,0 +1,69 @@ +/******************************************************** +Author: Diwakar +return_image = IFFT(source_image) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_IFFT(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j; + + + /// checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + + Mat img; + retrieveImage(img, 1); + + if(img.channels()>2){ + Scierror(999, "Input should be single or double channel\n"); + return 0; + } + Mat planes[] = {Mat_(img), Mat::zeros(img.size(), CV_32F)}; + Mat complexI; + merge(planes, 2, complexI); + dft(complexI, complexI); + Mat invDFT, invDFTcvt; + idft(complexI, invDFT, DFT_SCALE | DFT_REAL_OUTPUT ); + invDFT.convertTo(invDFTcvt, CV_8U); + + string tempstring = type2str(invDFTcvt.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,invDFTcvt, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway1/cpp/opencv_KalmanFilter.cpp b/sci_gateway1/cpp/opencv_KalmanFilter.cpp new file mode 100644 index 0000000..4d1e317 --- /dev/null +++ b/sci_gateway1/cpp/opencv_KalmanFilter.cpp @@ -0,0 +1,690 @@ +/******************************************************************************************************** + Author : Kevin George + +-> Syntax : KalmanFilter("FileName(string)",..,"dynamParams(int)",..,"measureParams(int)",.., + "controlParams(int)",..,"type(int)",..,"colour(string)",..) + +source-http://www.robot-home.it/blog/en/software/ball-tracker-con-filtro-di-kalman/ +*******************************************************************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + //#include "../common.cpp" + + int opencv_KalmanFilter(char *fname, unsigned long fname_len) + { + + //Error variables + SciErr sciErr; + int intErr = 0; + + //-> Address of Various Arguments + int *piAddr = NULL; + //int *piAddr2 = NULL; + + //-> Local variables + int num_InputArgs; //-> gives total number of arguments + int iRows, iCols; + int *piLen = NULL; + char **pstData = NULL; //-> why double pointer?? and what is it + char *currentArg = NULL; //-> Stores current string representing 'name' of name,value pair arguments + bool *providedArgs = NULL; //-> Used to check that optional argument is not entered more than once + //-> Name,Value Pair Variables + int dynamParams,measureParams; + int controlParams,type; + //char *colour = NULL; + string colour; + char *filename; + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + //-> Checks number of input and output arguments + CheckInputArgument(pvApiCtx, 0, 6); + CheckOutputArgument(pvApiCtx, 1, 6); + + //-> Count number of input arguments + num_InputArgs = *getNbInputArgument(pvApiCtx); + + providedArgs = (bool*) malloc(sizeof(bool) * 6); + + //-> Based on number of input arguments + + for(int iter = 1; iter<= num_InputArgs; iter++) + { + //-> Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Extracting name of next argument takes three calls to getMatrixOfString + //-> First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + //-> Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + //-> Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + +//****************************************************** Name,Value - Filename ***************************************************************** + if(strcmp(currentArg, "FileName")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[5]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Extracting name of next argument takes three calls to getMatrixOfString + //-> First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + //-> Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + //-> Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + filename = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + + providedArgs[5] = 1; + } + + else if(providedArgs[5]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + } +//****************************************************** Name,Value - dynamParams ***************************************************************** + + + if(strcmp(currentArg, "dynamParams")==0) + { + + if(iter+1<= num_InputArgs && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &dynamParams); + if(intErr) + { + return intErr; + } + + if( dynamParams < 0) + { + Scierror(999," Invalid Value for dynamParams. Please enter a non negative Double value\\n"); + return 0; + } + + providedArgs[0] = 1; + } + + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + +//****************************************************** Name,Value - measureParams ***************************************************************** + else if(strcmp(currentArg, "measureParams")==0) + { + + if(iter+1<= num_InputArgs && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &measureParams); + if(intErr) + { + return intErr; + } + + if( measureParams < 0) + { + Scierror(999," Invalid Value for measureParams. Please enter a non negative Double value\\n"); + return 0; + } + + providedArgs[1] = 1; + } + + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + +//****************************************************** Name,Value - controlParams ***************************************************************** + + else if(strcmp(currentArg, "controlParams")==0) + { + + if(iter+1<= num_InputArgs && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &controlParams); + if(intErr) + { + return intErr; + } + + if( controlParams < 0) + { + Scierror(999," Invalid Value for controlParams. Please enter a non negative Double value\\n"); + return 0; + } + + providedArgs[2] = 1; + } + + else if(providedArgs[2]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + +//****************************************************** Name,Value - type ***************************************************************** + + else if(strcmp(currentArg, "type")==0) + { + + if(iter+1<= num_InputArgs && !providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &type); + if(intErr) + { + return intErr; + } + + + providedArgs[3] = 1; + } + + else if(providedArgs[3]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + +//****************************************************** Name,Value - colour ***************************************************************** + + + else if(strcmp(currentArg, "colour")==0) + { + + if(iter+1<= num_InputArgs && !providedArgs[4]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Extracting name of next argument takes three calls to getMatrixOfString + //-> First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + //-> Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + //-> Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + colour = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + + providedArgs[4] = 1; + } + + else if(providedArgs[4]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + + }//-> Braces of big for loop + +//******************************************************** Giving Default Arguments ******************************************************************* + + if(providedArgs[0] == 0) + { + dynamParams = 6; + } + + + if(providedArgs[1] == 0) + { + measureParams = 4; + } + + if(providedArgs[2] == 0) + { + controlParams = 0; + } + + if(providedArgs[3] == 0) + { + type = CV_32F; + } + + if(providedArgs[4] == 0) + { + colour = "red"; + } + +//**************************************************************Actual Processing******************************************************************************* + + //objectStatus2 = true; + + //SciErr sciErr; + //int *piAddr = NULL; + //int *outList = NULL; + //unsigned char *red = NULL; + //unsigned char *green = NULL; + //unsigned char *blue = NULL; + + KalmanFilter kf(dynamParams, measureParams, controlParams, type); + + Mat state(dynamParams, 1, type); // [x,y,v_x,v_y,w,h] + Mat meas(measureParams, 1, type); // [z_x,z_y,z_w,z_h] + + setIdentity(kf.transitionMatrix); + + kf.measurementMatrix = cv::Mat::zeros(measureParams, dynamParams, type); + kf.measurementMatrix.at(0) = 1.0f; + kf.measurementMatrix.at(7) = 1.0f; + kf.measurementMatrix.at(16) = 1.0f; + kf.measurementMatrix.at(23) = 1.0f; + + + kf.processNoiseCov.at(0) = 1e-2; + kf.processNoiseCov.at(7) = 1e-2; + kf.processNoiseCov.at(14) = 5.0f; + kf.processNoiseCov.at(21) = 5.0f; + kf.processNoiseCov.at(28) = 1e-2; + kf.processNoiseCov.at(35) = 1e-2; + + // Measures Noise Covariance Matrix R + setIdentity(kf.measurementNoiseCov, cv::Scalar(1e-1)); + // <<<< Kalman Filter + + VideoCapture cap(filename); + cap.set(CV_CAP_PROP_FRAME_WIDTH, 1024); + cap.set(CV_CAP_PROP_FRAME_HEIGHT, 768); + + Mat frame; + //retrieveImage(frame,1); + + + char ch = 0; + double ticks = 0; + bool found = false; + + int notFoundCount = 0; + + //double precTick = ticks; + //-> measures a function execution time + //-> counting number of ticks after certain event + //ticks = (double) cv::getTickCount(); + + //double dT = (ticks - precTick) / cv::getTickFrequency(); //seconds + + //Mat res; + //frame.copyTo( res ); + + while (ch != 'q' && ch != 'Q') + { + double precTick = ticks; + ticks = (double) cv::getTickCount(); + + double dT = (ticks - precTick) / cv::getTickFrequency(); //seconds + + // Frame acquisition + cap >> frame; + + cv::Mat res; + frame.copyTo( res ); + + if (found) + { + // >>>> Matrix A + kf.transitionMatrix.at(2) = dT; + kf.transitionMatrix.at(9) = dT; + // <<<< Matrix A + + state = kf.predict(); + + Rect predRect; + predRect.width = state.at(4); + predRect.height = state.at(5); + predRect.x = state.at(0) - predRect.width / 2; + predRect.y = state.at(1) - predRect.height / 2; + + Point center; + center.x = state.at(0); + center.y = state.at(1); + circle(res, center, 2, CV_RGB(255,0,0), -1); + + rectangle(res, predRect, CV_RGB(255,0,0), 2); + } + + // >>>>> Noise smoothing + Mat blur; + GaussianBlur(frame, blur, cv::Size(5, 5), 3.0, 3.0); + // <<<<< Noise smoothing + + // >>>>> HSV conversion + Mat frmHsv; + cvtColor(blur, frmHsv, CV_BGR2HSV); + // <<<<< HSV conversion + + // >>>>> Color Thresholding + // Note: change parameters for different colors + Mat rangeRes = cv::Mat::zeros(frame.size(), CV_8UC1); + +//************************** Tracks object with range of RGB values ******************************************************************* + + if(colour == "red") + { cv::inRange(frmHsv, cv::Scalar(0, 153, 127), + cv::Scalar(4, 230, 179), rangeRes); + } + + else if(colour == "green") + { + cv::inRange(frmHsv, cv::Scalar(30,0,0), + cv::Scalar(100,255,255), rangeRes); + } + + else if(colour == "white") + { + cv::inRange(frmHsv, cv::Scalar(0,0,80), + cv::Scalar(255,50,120), rangeRes); + } + + else if(colour == "orange" || colour == "yellow") + { + cv::inRange(frmHsv, cv::Scalar(15, 204, 204), + cv::Scalar(20, 255, 255), rangeRes); + } + + + + // <<<<< Color Thresholding + + // >>>>> Improving the result + erode(rangeRes, rangeRes, cv::Mat(), cv::Point(-1, -1), 2); + dilate(rangeRes, rangeRes, cv::Mat(), cv::Point(-1, -1), 2); + // <<<<< Improving the result + + // Thresholding viewing + ///cv::imshow("Threshold", rangeRes); + + // >>>>> Contours detection + vector > contours; + findContours(rangeRes, contours, CV_RETR_EXTERNAL, + CV_CHAIN_APPROX_NONE); + // <<<<< Contours detection + + // >>>>> Filtering + vector > balls; + vector ballsBox; + + for (size_t i = 0; i < contours.size(); i++) + { + Rect bBox; + bBox = cv::boundingRect(contours[i]); + + float ratio = (float) bBox.width / (float) bBox.height; + if (ratio > 1.0f) + ratio = 1.0f / ratio; + + // Searching for a bBox almost square + if (ratio > 0.75 && bBox.area() >= 400) + { + balls.push_back(contours[i]); + ballsBox.push_back(bBox); + } + } + // <<<<< Filtering + + // >>>>> Detection result + for (size_t i = 0; i < balls.size(); i++) + { + drawContours(res, balls, i, CV_RGB(20,150,20), 1); + rectangle(res, ballsBox[i], CV_RGB(0,255,0), 2); + + Point center; + center.x = ballsBox[i].x + ballsBox[i].width / 2; + center.y = ballsBox[i].y + ballsBox[i].height / 2; + circle(res, center, 2, CV_RGB(20,150,20), -1); + + stringstream sstr; + sstr << "(" << center.x << "," << center.y << ")"; + putText(res, sstr.str(), + cv::Point(center.x + 3, center.y - 3), + cv::FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(20,150,20), 2); + } + // <<<<< Detection result + + // >>>>> Kalman Update + if (balls.size() == 0) + { + notFoundCount++; + if( notFoundCount >= 100 ) + { + found = false; + } + /*else + kf.statePost = state;*/ + } + else + { + notFoundCount = 0; + + meas.at(0) = ballsBox[0].x + ballsBox[0].width / 2; + meas.at(1) = ballsBox[0].y + ballsBox[0].height / 2; + meas.at(2) = (float)ballsBox[0].width; + meas.at(3) = (float)ballsBox[0].height; + + if (!found) // First detection! + { + // >>>> Initialization + kf.errorCovPre.at(0) = 1; // px + kf.errorCovPre.at(7) = 1; // px + kf.errorCovPre.at(14) = 1; + kf.errorCovPre.at(21) = 1; + kf.errorCovPre.at(28) = 1; // px + kf.errorCovPre.at(35) = 1; // px + + state.at(0) = meas.at(0); + state.at(1) = meas.at(1); + state.at(2) = 0; + state.at(3) = 0; + state.at(4) = meas.at(2); + state.at(5) = meas.at(3); + // <<<< Initialization + + found = true; + } + else + kf.correct(meas); // Kalman Correction + + } + // <<<<< Kalman Update + // Final result + cv::imshow("Tracking", res); + ch = cv::waitKey(1); + + } + //AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_SURFPoints.cpp b/sci_gateway1/cpp/opencv_SURFPoints.cpp new file mode 100644 index 0000000..93bd6c2 --- /dev/null +++ b/sci_gateway1/cpp/opencv_SURFPoints.cpp @@ -0,0 +1,123 @@ +/*************************************************** +Author : Shashank Shekhar +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_SURFPoints(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int *piAddr = NULL; + int *piChild = NULL; + int iType = 0; + int iRows, iCols = 0; + double *matData = NULL; + + double *OrientationData = NULL; + double *MetricData = NULL; + int *LaplacianData = NULL; + double *ScaleData = NULL; + + CheckInputArgument(pvApiCtx, 1,1); + CheckOutputArgument(pvApiCtx,1,6); + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix || isVarComplex(pvApiCtx, piAddr)) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &matData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if (iCols!=2) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + OrientationData = (double *)malloc(sizeof(double)*iRows); + MetricData = (double *)malloc(sizeof(double)*iRows); + LaplacianData = (int *)malloc(sizeof(int)*iRows); + ScaleData = (double *)malloc(sizeof(double)*iRows); + + for(int i = 0 ; i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_TemplateMatcher(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j; + + int iRows = 0; + int iCols = 0; + int *piLen = NULL; + int *piAddr = NULL; + + char **method = NULL; + + + /// checking input argument + CheckInputArgument(pvApiCtx, 2, 3); + CheckOutputArgument(pvApiCtx, 1, 1); + + Mat image, template_image; + retrieveImage(image, 1); + retrieveImage(template_image, 2); + + /// if there is inconsistency in data type of image and template image + if(type2str(image.type()) != type2str(template_image.type())){ + Scierror(999, "%s: Image and template image are not of same type\n"); + return 0; + } + + if(template_image.rows > image.rows or template_image.cols > image.cols){ + Scierror(999, "%s: Dimensions of template image is greater then image\n"); + return 0; + } + + /// generated image is (W - w + 1) * (H - h + 1) and it must be 32 bit floating number with single channel + + Mat new_image(image.rows, image.cols, image.type()); + + + int nbInputArguments = *getNbInputArgument(pvApiCtx); + if(nbInputArguments > 2){ + /// variable address + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + /// Retrieve string from input parameter. (requires 3 calls) + /// first to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + method = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + method[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, method); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + if(strcmp(method[0], "CV_TM_SQDIFF") == 0) + matchTemplate(image, template_image, new_image, CV_TM_SQDIFF); + else if(strcmp(method[0], "CV_TM_SQDIFF_NORMED") == 0) + matchTemplate(image, template_image, new_image, CV_TM_SQDIFF_NORMED); + else if(strcmp(method[0], "CV_TM_CCORR") == 0) + matchTemplate(image, template_image, new_image, CV_TM_CCORR); + else if(strcmp(method[0], "CV_TM_CCORR_NORMED") == 0) + matchTemplate(image, template_image, new_image, CV_TM_CCORR_NORMED); + else if(strcmp(method[0], "CV_TM_CCOEFF") == 0) + matchTemplate(image, template_image, new_image, CV_TM_CCOEFF); + else if(strcmp(method[0], "CV_TM_CCOEFF_NORMED") == 0) + matchTemplate(image, template_image, new_image, CV_TM_CCOEFF_NORMED); + else{ + matchTemplate(image, template_image, new_image, CV_TM_SQDIFF); + sciprint("Wrong method given, using CV_TM_SQDIFF instead"); + } + } + + else{ + matchTemplate(image, template_image, new_image, CV_TM_SQDIFF); + } + + + + + + + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_VideoPlayer.cpp b/sci_gateway1/cpp/opencv_VideoPlayer.cpp new file mode 100644 index 0000000..74b7405 --- /dev/null +++ b/sci_gateway1/cpp/opencv_VideoPlayer.cpp @@ -0,0 +1,78 @@ +/*************************************************** +Author : Tanmay Chaudhari +**************************************************/ + +#include "opencv2/video/tracking.hpp" +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + //#include "../common.cpp" + + int opencv_VideoPlayer() + { + SciErr sciErr; + int* piAddr = NULL; + int iRows = 0; + int iCols = 0; + int iRet = 0; + char** pstData = NULL; + + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + iRet = getAllocatedMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, &pstData); + if(iRet) + { + freeAllocatedMatrixOfString(iRows, iCols, pstData); + return iRet; + } + sciErr = createMatrixOfString(pvApiCtx, nbInputArgument(pvApiCtx) + 1, iRows, iCols, pstData); + if(sciErr.iErr) + { + freeAllocatedMatrixOfString(iRows, iCols, pstData); + printError(&sciErr, 0); + return sciErr.iErr; + } + VideoCapture cap(pstData[0]); + namedWindow("Video Player", 0 ); + Mat frame; + bool paused = false; + for(;;) + { + if( !paused ) + { + cap >> frame; + if(frame.empty()) + break; + } + imshow("Video Player", frame ); + char c = (char)waitKey(20); + if(c==27) + break; + else if(c==32) + paused=!paused; + else; + } + destroyAllWindows(); + ReturnArguments(pvApiCtx); + return 0; + } +} + diff --git a/sci_gateway1/cpp/opencv_VideoReader.cpp b/sci_gateway1/cpp/opencv_VideoReader.cpp new file mode 100644 index 0000000..51207bf --- /dev/null +++ b/sci_gateway1/cpp/opencv_VideoReader.cpp @@ -0,0 +1,389 @@ +/*************************************************** +Author : Tanmay Chaudhari, Shashank Shekhar +**************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + //#include "../common.cpp" + + Ptr capture; + double videoCurrentTime = 0; + double videoDuration = 0; + int videoFPS; + char *videoFileName = NULL; + bool videoObjectSt = false; + int opencv_VideoReader(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int *piAddr = NULL; + int *piAddrVal = NULL; + int *piLen = NULL; + int iRows, iCols = 0; + char ** pstData1 = NULL; + char ** pstData2 = NULL; + double frames; + double width; + double height; + double CT; + char full_path[PATH_MAX]; + bool stillLeft = false; + CheckInputArgument(pvApiCtx,1,3); + CheckOutputArgument(pvApiCtx,1,10); + int noOfarguments = *getNbInputArgument(pvApiCtx); + if(noOfarguments==2) + { + Scierror(999,"Invalid number of Arguments\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isStringType(pvApiCtx, piAddr)) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + //first call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData1 = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j = 0 ; j < iRows * iCols ; j++) + { + pstData1[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(noOfarguments>1) + { + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isStringType(pvApiCtx, piAddr)) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + //first call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData2 = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j = 0 ; j < iRows * iCols ; j++) + { + pstData2[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp(pstData2[0], "CurrentTime")==0) + { + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + + Scierror(999," Invalid Value for Current Time.\n"); + return 0; + } + getScalarDouble(pvApiCtx, piAddrVal, &CT); + if(CT<0) + { + Scierror(999,"Invalid Input for CurrentTime.\n"); + return 0; + } + CT*=1000; + } + else + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + } + + if(videoObjectSt==true && strcmp(videoFileName, pstData1[0])==0) + { + if(int(videoCurrentTime) >= int(videoDuration)) + { + stillLeft = false; + } + else if(int(videoCurrentTime) < int(videoDuration)) + stillLeft = true; + } + + if(!stillLeft) + { + capture = new VideoCapture(pstData1[0]); + if(!capture->isOpened()) + { + Scierror(999,"Could not open file\n"); + return 0; + } + } + + videoFileName = pstData1[0]; + videoFPS = capture->get(CV_CAP_PROP_FPS); + frames = capture->get(CV_CAP_PROP_FRAME_COUNT); + + if(noOfarguments!=1) + { + if(int(CT) >= int(videoDuration)) + { + Scierror(999,"Current Time should be less than Video Duration\n"); + return 0; + } + capture->set(CV_CAP_PROP_POS_MSEC, CT); + } + + videoObjectSt = true; + videoCurrentTime = capture->get(CV_CAP_PROP_POS_MSEC); + videoDuration = (1000/double(videoFPS))*double(frames); + width = capture->get(CV_CAP_PROP_FRAME_WIDTH); + height = capture->get(CV_CAP_PROP_FRAME_HEIGHT); + realpath(pstData1[0], full_path); + + createSingleString(pvApiCtx, nbInputArgument(pvApiCtx) + 1, pstData1[0]); + createScalarDouble(pvApiCtx,nbInputArgument(pvApiCtx) + 2, videoDuration); + createScalarDouble(pvApiCtx,nbInputArgument(pvApiCtx) + 3, videoFPS); + createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+4, videoCurrentTime); + createScalarDouble(pvApiCtx,nbInputArgument(pvApiCtx) + 5, frames); + createScalarDouble(pvApiCtx,nbInputArgument(pvApiCtx) + 6, width); + createScalarDouble(pvApiCtx,nbInputArgument(pvApiCtx) + 7, height); + createSingleString(pvApiCtx, nbInputArgument(pvApiCtx)+8, full_path); + + for(int i=0;i<=8;i++) + { + AssignOutputVariable(pvApiCtx, i) = nbInputArgument(pvApiCtx) + i; + } + + ReturnArguments(pvApiCtx); + return 0; + } + + int has_frame(char *fname, unsigned long fname_len) + { + SciErr sciErr; + + if(videoFileName==NULL) + { + Scierror(999,"No video has been read yet.\n"); + return 0; + } + + int *piAddr = NULL; + int *piLen = NULL; + int iRows, iCols = 0; + char ** pstData = NULL; + + CheckInputArgument(pvApiCtx,0,0); + CheckOutputArgument(pvApiCtx,0,1); + + if(videoObjectSt == false) + { + Scierror(999,"Invalid Video File. You need to read the video using VideoReader.\n"); + return 0; + } + + if(videoCurrentTime>videoDuration) + createScalarBoolean(pvApiCtx,nbInputArgument(pvApiCtx) + 1, false); + else if(int(videoCurrentTime) == int(videoDuration)) + createScalarBoolean(pvApiCtx,nbInputArgument(pvApiCtx) + 1, false); + else + createScalarBoolean(pvApiCtx,nbInputArgument(pvApiCtx) + 1, true); + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + + return 0; + } + + int read_frame(char *fname, unsigned long fname_len) + { + SciErr sciErr; + + if(videoFileName==NULL) + { + Scierror(999,"No video has been read yet.\n"); + return 0; + } + + if(int(videoCurrentTime)>=int(videoDuration)) + { + Scierror(999,"No more frames left to be read\n"); + return 0; + } + //sciprint("Current Time of the Video is : %f\n", videoCurrentTime); + + int *piAddr = NULL; + int *piLen = NULL; + int iRows, iCols = 0; + char ** pstData = NULL; + int *singleFrameList = NULL; + unsigned char *red = NULL; + unsigned char *green = NULL; + unsigned char *blue = NULL; + + CheckInputArgument(pvApiCtx,0,0); + CheckOutputArgument(pvApiCtx,0,1); + + if(videoObjectSt == false) + { + Scierror(999,"Invalid Video File. You need to read the video using VideoReader.\n"); + return 0; + } + +//--------------------------------------- Processing Frame at the particular frame ----------------------------------- + + Mat frame; + capture->set(CV_CAP_PROP_POS_MSEC, videoCurrentTime); + if(!capture->read(frame)) + { + Scierror(999, "Can't read Frame"); + return 0; + } +//-------------------------------------------------------------------------------------------------------------------- + + if(frame.channels()==1) + { + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &singleFrameList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + red = (unsigned char *)malloc(sizeof(unsigned char)*frame.rows*frame.cols); + + for(int k=0;k(k, p); + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, singleFrameList, 1, frame.rows, frame.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + free(red); + } + else + { + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &singleFrameList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + red = (unsigned char *)malloc(sizeof(unsigned char)*frame.rows*frame.cols); + green = (unsigned char *)malloc(sizeof(unsigned char)*frame.rows*frame.cols); + blue = (unsigned char *)malloc(sizeof(unsigned char)*frame.rows*frame.cols); + + for(int k=0;k(k, p); + red[k+frame.rows*p]=intensity.val[2]; + green[k+frame.rows*p]=intensity.val[1]; + blue[k+frame.rows*p]=intensity.val[0]; + } + } + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, singleFrameList, 1, frame.rows, frame.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, singleFrameList, 2, frame.rows, frame.cols, green); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, singleFrameList, 3, frame.rows, frame.cols, blue); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + free(red); + free(green); + free(blue); + } + + videoCurrentTime+=(1000/double(videoFPS)); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + ReturnArguments(pvApiCtx); + + return 0; + } +} + + + diff --git a/sci_gateway1/cpp/opencv_arrowedline.cpp b/sci_gateway1/cpp/opencv_arrowedline.cpp new file mode 100644 index 0000000..0e1643a --- /dev/null +++ b/sci_gateway1/cpp/opencv_arrowedline.cpp @@ -0,0 +1,221 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + return_image = arrowedline(image , x1 , y1 , x2,y2,r_value,g_value,b_value,thickness,linetype,shift,tiplength); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_arrowedline(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11 = NULL; + int *piAddr12 = NULL; + int i,j,k; + double thickness=1 , linetype=8,shift =0 ,tiplength=0.1; + double x1,y1,x2,y2,r_value,g_value,b_value; + + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 12); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&x1); + if(intErr) + return intErr; + + //for value of y coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&y1); + if(intErr) + return intErr; + + //for value of x coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4 ,&x2); + if(intErr) + return intErr; + + ///for value of y coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&y2); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6,&r_value); + if(intErr) + return intErr; + + // for G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&b_value); + if(intErr) + return intErr; + + // for thickness of circle default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr9 ,&thickness); + if(intErr) + return intErr; + + //for line type of circle default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr10,&linetype); + if(intErr) + return intErr; + + // for shift in line defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Syntax: sciErr = getMatrixOfInteger8(pvApiCtx, piAddr8, &iRows8, &iCols8, &pcData); + intErr = getScalarDouble(pvApiCtx, piAddr11 ,&shift); + if(intErr) + return intErr; + + // for tiplength of arrow default : 0.1 + sciErr = getVarAddressFromPosition(pvApiCtx,12,&piAddr12); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Syntax: sciErr = getMatrixOfInteger8(pvApiCtx, piAddr8, &iRows8, &iCols8, &pcData); + intErr = getScalarDouble(pvApiCtx, piAddr12,&tiplength); + if(intErr) + return intErr; + //temporary pt variable, to use in function as centre + Point pt1(x1,y1); + Point pt2(x2,y2); + + + /// checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead\n"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead"); + } + //calling the opencv function + arrowedLine( src, pt1, pt2, Scalar(b_value,g_value,r_value), thickness, linetype, shift, tiplength); + + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway1/cpp/opencv_bagOfFeatures.cpp b/sci_gateway1/cpp/opencv_bagOfFeatures.cpp new file mode 100644 index 0000000..e87c40c --- /dev/null +++ b/sci_gateway1/cpp/opencv_bagOfFeatures.cpp @@ -0,0 +1,550 @@ +/**************************************************************************************************** +* Author: Umang Agrawal * +* Code: bagOfFeatues.cpp * +* Function Format: bag = bagOfFeatures(imageSet, Oprional Arguments Name, Value) * +* Arguments: Name Value * +* 1. VocabularySize any integer value greater than 0 * +* 2. StrongestFeatures pecentage of best features to be Selected * +* 3. Upright Bool 0 or 1 * +* 4. Verbose Bool 0 or 1 * +****************************************************************************************************/ + +#include +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/imgproc/imgproc.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + bool response_comparator(const KeyPoint& p1, const KeyPoint& p2) { + return p1.response > p2.response; + } + + int opencv_bagOfFeatures(char *fname, unsigned long fname_len) + { + SciErr sciErr; + + int inp_params = 0; + + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + int *piChild = NULL; + char **pstData = NULL; + int iRows, iCols; + char **arg = NULL; + int count_ver = 0, count_up = 0, count_strength = 0, count_vocab = 0; + + int descriptionCount; + char *objectType = "bagOfFeatures"; + char **description = NULL; + int *count = NULL; + double vocab_size = 500, verbose = 1, upright = 1, strength = 1; + char ***location = NULL; + char **filepath = NULL; + char *file = "Bag-Of-Features.yml"; + int key_size=0, v_key_size=0; + + Mat image; + vector keypoints; + vector valid_key; + Mat feature_des; + Mat dictionary; + Mat featuresUnclustered; + + CheckInputArgument(pvApiCtx, 1, 9); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 1); //Check on Number of Output Arguments + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr1)) + { + Scierror(999, "Error: Invalid first argument. List Expected.\n"); + return 0; + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"imageSet")==0)) + { + Scierror(999, "Error: The input argument is not of type imageSet.\n"); + return 0; + } + + // Extracting Description attribute of input argument + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + description = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + description[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 2, &iRows, &iCols, piLen, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + descriptionCount = iRows; + + // Extracting Count attribute of input argument + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr1, 3, &iRows, &iCols, &count); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + location = (char***) malloc(sizeof(char**) * descriptionCount); + sciErr = getListItemAddress(pvApiCtx, piAddr1, 4, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int iter = 1; iter<=descriptionCount; iter++) + { + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + location[iter-1] = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int colIter = 0 ; colIter < iRows * iCols ; colIter++) + { + location[iter-1][colIter] = (char*)malloc(sizeof(char) * (piLen[colIter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, location[iter-1]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + /*sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !isStringType(pvApiCtx, piAddr2)) + { + Scierror(999, "%s: Wrong type of argument for Location. A string is expected.\n", fname); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(piLen); + return 0; + } + filepath = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + filepath[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1)); + } + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, filepath); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(piLen); + free(filepath); + return 0; + }*/ + + inp_params = *getNbInputArgument(pvApiCtx); + for(int i=2; i<=inp_params; i++) + { + if( inp_params%2 == 0) + { + Scierror(999,"Either Argument Name or its Value missing\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, i, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr3)) + { + Scierror(999, "%s: Wrong type of argument for Name of Optional Argument. A string is expected.\n", fname); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(piLen); + return 0; + } + arg = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + arg[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1)); + } + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(piLen); + free(arg); + return 0; + } + + if(strcmp(arg[0],"VocabularySize") == 0) + { + if( count_vocab != 0) + { + Scierror(999,"Voabulary Size has been called twice.\n"); + return 0; + } + free(arg); + free(piLen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !(isDoubleType(pvApiCtx, piAddr4)||isIntegerType(pvApiCtx, piAddr4))) + { + Scierror(999,"Not a valid type of value for VocabularySize.\n"); + return 0; + } + //Reading the Value of the argument + if(getScalarDouble(pvApiCtx, piAddr4, &vocab_size)) + { + Scierror(999,"Not a valid type of value for VocabularySize.\n"); + return 0; + } + if(vocab_size < 0) + { + Scierror(999,"Enter a valid value for Vacabulary Size\n"); + return 0; + } + i++; + count_vocab += 1; + } + else if(strcmp(arg[0],"StrongestFeatures") == 0) + { + if( count_strength != 0) + { + Scierror(999,"Strength Features has been called twice.\n"); + return 0; + } + free(arg); + free(piLen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !isDoubleType(pvApiCtx, piAddr4) ) + { + Scierror(999,"Not a valid type of value for StrongestFeatures.\n"); + return 0; + } + //Reading the Value of the argument + if(getScalarDouble(pvApiCtx, piAddr4, &strength)) + { + Scierror(999,"Not a valid type of value for StrongestFeatures.\n"); + return 0; + } + if( strength<0 || strength>=1 ) + { + Scierror(999,"Enter a valid value for Strongest Features (between 0 and 1)\n"); + return 0; + } + i++; + count_strength += 1; + } + else if(strcmp(arg[0],"Verbose") == 0) + { + if( count_ver != 0) + { + Scierror(999,"Verbose has been called twice.\n"); + return 0; + } + free(arg); + free(piLen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !(isDoubleType(pvApiCtx, piAddr4)||isIntegerType(pvApiCtx, piAddr4))) + { + Scierror(999,"Not a valid type of value for Verbose.\n"); + return 0; + } + //Reading the Value of the argument + if(getScalarDouble(pvApiCtx, piAddr4, &verbose)) + { + Scierror(999,"Not a valid type of value for Verbose.\n"); + return 0; + } + if( !(verbose == 1|| verbose == 0) ) + { + Scierror(999,"Enter a valid value for Verbose (Either 0 or 1)\n"); + return 0; + } + i++; + count_ver += 1; + } + else if(strcmp(arg[0],"Upright") == 0) + { + if( count_up != 0) + { + Scierror(999,"Upright has been called twice.\n"); + return 0; + } + free(arg); + free(piLen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !(isDoubleType(pvApiCtx, piAddr4)||isIntegerType(pvApiCtx, piAddr4))) + { + Scierror(999,"Not a valid type of value for Upright.\n"); + return 0; + } + //Reading the Value of the argument + if(getScalarDouble(pvApiCtx, piAddr4, &upright)) + { + Scierror(999,"Not a valid type of value for Upright.\n"); + return 0; + } + if(!(upright == 1 || upright == 0)) + { + Scierror(999,"Enter a valid value for Upright (Either 0 or 1)\n"); + return 0; + } + i++; + count_up += 1; + } + else + { + Scierror(999,"Invalid Argument Name\n"); + return 0; + } + } + + filepath = (char**)malloc(sizeof(char*)*1*1); + filepath[0] = (char*)malloc(sizeof(char)*20); + strcpy(filepath[0],file); + /*int flag=1; + for(int i=0; location[0][0][i]; i++) + { + common = location[0][0][i]; + for(int j=0; j extractor = new SurfDescriptorExtractor(1, 4, 2, 1, int(upright)); + if(int(verbose)) + { + sciprint("Creating Bag-Of-Features from %d image sets.\n\n",descriptionCount); + for(int i=0; icompute(image, valid_key, feature_des); + featuresUnclustered.push_back(feature_des); + key_size += keypoints.size(); + v_key_size += valid_key.size(); + } + if(int(verbose)) + { + sciprint("done. Extracted %d features.\n",key_size); + sciprint("Keeping %f percent of the strongest features.\n",(strength)*100); + sciprint("Net Extracted features : %d\n\n",v_key_size); + } + } + + if(vocab_size > featuresUnclustered.rows) + vocab_size = featuresUnclustered.rows; + TermCriteria tc(CV_TERMCRIT_ITER, 100, 0.001); + int retries = 3; + BOWKMeansTrainer bowTrainer(int(vocab_size), tc, retries, KMEANS_PP_CENTERS); + + if(int(verbose)) + { + sciprint("Using K-Means Clustering to create a %d word visual vocabulary.\n",int(vocab_size)); + sciprint("Number of Features : %d\n",featuresUnclustered.rows); + sciprint("Number of Clusters : %d\n\n",int(vocab_size)); + } + + dictionary = bowTrainer.cluster(featuresUnclustered); + + if(int(verbose)) + sciprint("Finished creating Bag-Of-Features\n"); + + FileStorage fs(filepath[0], FileStorage::WRITE); + fs<<"dictionary"< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + // #include "../common.h" + // #include "../common.cpp" + +int opencv_bbox2points() +{ + //Error management variable + SciErr sciErr; + + //Variable declaration + int i; + int iComplex = 0; + int iType = 0; + int iRows = 0; + int iCols = 0; + int *piAddr = NULL; + double *pdblReal = NULL; + double *points; + + //Check input output arguments + checkInputArgument(pvApiCtx, 1, 1); + checkOutputArgument(pvApiCtx, 1, 1); + + //Get variable address of the first input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr || iCols != 4 || iRows == 0) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, iRows, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for( i = 0; i < iRows; i++) //Rows in box + { + points = (double*)malloc(sizeof(double) * 8); + memset(points, 0, sizeof(points)); + + //Upper-left + points[0] = pdblReal[i]; + points[4] = pdblReal[i + iRows]; + + //Upper-right + points[1] = pdblReal[i] + pdblReal[i + 2 * iRows]; + points[5] = pdblReal[i + iRows]; + + //Lower-right + points[2] = pdblReal[i] + pdblReal[i + 2 * iRows]; + points[6] = pdblReal[i + iRows] + pdblReal[i+ 3 * iRows]; + + //Lower-left + points[3] = pdblReal[i]; + points[7] = pdblReal[i + iRows] + pdblReal[i + 3 * iRows]; + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, i + 1, 4, 2, points); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + //Return output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_bboxOverlapRatio.cpp b/sci_gateway1/cpp/opencv_bboxOverlapRatio.cpp new file mode 100644 index 0000000..e43cb12 --- /dev/null +++ b/sci_gateway1/cpp/opencv_bboxOverlapRatio.cpp @@ -0,0 +1,275 @@ +/*************************************************** +Author : Tanmay Chaudhari + ***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" + // #include "../common.h" + // #include "../common.cpp" + + int opencv_bboxOverlapRatio(char *fname, unsigned long fname_len) + { + //Error management variable + SciErr sciErr; + + //Variable declaration + int i, j; + int nbInputArguments = 0; + int iComplex = 0; + int iType = 0; + int iRows = 0; + int iCols = 0; + int rowsOfBboxA = 0; + int colsOfBboxA = 0; + int rowsOfBboxB = 0; + int colsOfBboxB = 0; + int unionOrMin = 0; //if 0 then union else min + int *piAddr = NULL; + int *piLen = NULL; + char **ratioType = NULL; + double x1 = 0; + double x2 = 0; + double y1 = 0; + double y2 = 0; + double w = 0; + double h = 0; + double intersectAB = 0; + double *overlapRatio = 0; + double *bboxA = NULL; + double *bboxB = NULL; + double *x1BboxA = NULL; + double *x2BboxA = NULL; + double *y1BboxA = NULL; + double *y2BboxA = NULL; + double *x1BboxB = NULL; + double *x2BboxB = NULL; + double *y1BboxB = NULL; + double *y2BboxB = NULL; + double *areaA = NULL; + double *areaB = NULL; + + //Check input output arguments + checkInputArgument(pvApiCtx, 2, 3); + checkOutputArgument(pvApiCtx, 1, 1); + + //Get number of input arguments + nbInputArguments = *getNbInputArgument(pvApiCtx); + + for( i = 1; i <= nbInputArguments; i++) + { + //Get variable address of the input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, i, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(i != 3) + { + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + if(i == 1) + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfBboxA, &colsOfBboxA, &bboxA); + else + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfBboxB, &colsOfBboxB, &bboxB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if((i == 1) && ((rowsOfBboxA == 0) || (colsOfBboxA !=4))) + { + printError(&sciErr, 0); + return 0; + } + + if((i == 2) && ((rowsOfBboxB == 0) || (colsOfBboxB != 4))) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr || iRows != 1 || iCols != 1) + { + printError(&sciErr, 0); + return 0; + } + + ratioType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i=0;i< iRows * iCols; i++) + { + ratioType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1)); //+1 for NULL character + } + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, ratioType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(piLen[0] != 5 && piLen[0] != 3) + { + printError(&sciErr, 0); + return 0; + } + if(piLen[0] == 5 && ratioType[0][0] == 'U' && ratioType[0][1] == 'n' && ratioType[0][2] == 'i' && ratioType[0][3] == 'o' && ratioType[0][4] == 'n') + unionOrMin = 0; + else if(piLen[0] == 3 && ratioType[0][0] == 'M' && ratioType[0][1] == 'i' && ratioType[0][2] == 'n') + unionOrMin = 1; + else + { + Scierror(999, "%s: Wrong type for input argument: Union or Min is expected.\n"); + return 0; + } + free(piLen); + free(ratioType); + } + } + + x1BboxA = (double*)malloc(sizeof(double) * rowsOfBboxA); + x2BboxA = (double*)malloc(sizeof(double) * rowsOfBboxA); + y1BboxA = (double*)malloc(sizeof(double) * rowsOfBboxA); + y2BboxA = (double*)malloc(sizeof(double) * rowsOfBboxA); + + x1BboxB = (double*)malloc(sizeof(double) * rowsOfBboxB); + x2BboxB = (double*)malloc(sizeof(double) * rowsOfBboxB); + y1BboxB = (double*)malloc(sizeof(double) * rowsOfBboxB); + y2BboxB = (double*)malloc(sizeof(double) * rowsOfBboxB); + + areaA = (double*)malloc(sizeof(double) * rowsOfBboxA); + areaB = (double*)malloc(sizeof(double) * rowsOfBboxB); + + overlapRatio = (double*)malloc(sizeof(double) * rowsOfBboxA * rowsOfBboxB); + + for( i = 0; i < rowsOfBboxA; i++) + { + //Left top corner + x1BboxA[i] = bboxA[i]; + y1BboxA[i] = bboxA[rowsOfBboxA + i]; + + //right bottom corner + x2BboxA[i] = x1BboxA[i] + bboxA[2 * rowsOfBboxA + i]; + y2BboxA[i] = y1BboxA[i] + bboxA[3 * rowsOfBboxA + i]; + } + + for( i = 0; i < rowsOfBboxB; i++) + { + //Left top corner + x1BboxB[i] = bboxB[i]; + y1BboxB[i] = bboxB[rowsOfBboxB + i]; + + //right bottom corner + x2BboxB[i] = x1BboxB[i] + bboxB[2 * rowsOfBboxB + i]; + y2BboxB[i] = y1BboxB[i] + bboxB[3 * rowsOfBboxB + i]; + } + + //Computing area for each rectangle in bboxA + for( i = 0; i < rowsOfBboxA; i++) + areaA[i] = bboxA[2 * rowsOfBboxA + i] * bboxA[3 * rowsOfBboxA + i]; + + //Computing area for each rectangle in bboxB + for( i = 0 ; i < rowsOfBboxB; i++) + areaB[i] = bboxB[2 * rowsOfBboxB + i] * bboxB[3 * rowsOfBboxB + i]; + + for( i = 0; i < rowsOfBboxA * rowsOfBboxB; i++) + overlapRatio[i] = 0; + + for( i = 0; i < rowsOfBboxA; i++) + { + for( j = 0; j < rowsOfBboxB; j++) + { + //Computing the corners of the intersect + x1 = (x1BboxA[i] > x1BboxB[j])?x1BboxA[i]:x1BboxB[j]; + y1 = (y1BboxA[i] > y1BboxB[j])?y1BboxA[i]:y1BboxB[j]; + x2 = (x2BboxA[i] < x2BboxB[j])?x2BboxA[i]:x2BboxB[j]; + y2 = (y2BboxA[i] < y2BboxB[j])?y2BboxA[i]:y2BboxB[j]; + + w = x2 - x1; + if(w <= 0) //Skip if no intersection + continue; + + h = y2 - y1; + if(h <= 0) //Skip if no intersection + continue; + + intersectAB = w * h; + + if(nbInputArguments == 2) + overlapRatio[j * rowsOfBboxA + i] = intersectAB/(areaA[i] + areaB[j] - intersectAB); + else + { + if(unionOrMin == 0) + overlapRatio[j * rowsOfBboxA + i] = intersectAB/(areaA[i] + areaB[j] - intersectAB); + else + overlapRatio[j * rowsOfBboxA + i] = intersectAB/((areaA[i] < areaB[j])?areaA[i]:areaB[j]); + } + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, rowsOfBboxA, rowsOfBboxB, overlapRatio); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Return Output Argument + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_blobAnalysis.cpp b/sci_gateway1/cpp/opencv_blobAnalysis.cpp new file mode 100644 index 0000000..4b23607 --- /dev/null +++ b/sci_gateway1/cpp/opencv_blobAnalysis.cpp @@ -0,0 +1,381 @@ +/******************************************************** +Author : Deepshikha +[key_value] : blobAnalysis(source_image) + +// 1 :image +Optional arguments :- +// 2 : filterByArea +// 3 : filterByThreshold +// 4 : filterByCircularity +// 5 : filterByConvexity + +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/features2d/features2d.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_blobAnalysis(char *fname, unsigned long fname_len){ + + // Error management variable + SciErr sciErr; + + //Variables + int i, j; + int intErr; + int iRows1 = 0; + int iCols1 = 0; + + int iRows = 0; + int iCols = 0; + int *piLen = NULL; + int *piAddr = NULL; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + + double *area = NULL; + double *threshold = NULL; + double *circularity = NULL; + double *convexity = NULL; + + double *key_value = NULL; + + // checking input argument + // 1 : image + // 2 : filterByArea + // 3 : filterByThreshold + // 4 : filterByCircularity + // 5 : filterByConvexity + CheckInputArgument(pvApiCtx, 1, 9); + CheckOutputArgument(pvApiCtx, 1, 1); + + // retrieve image + Mat image; + retrieveImage(image, 1); + + // For the optional arguments + int nbInputArguments = *getNbInputArgument(pvApiCtx); + //sciprint("%d\n",nbInputArguments); + + if((nbInputArguments%2) == 0) + { + Scierror(999, "%d: The number of arguments must be odd\n"); + return 0; + } + + providedArgs = (bool*) malloc(sizeof(bool) * 4); + memset(providedArgs, 0, 4); + + for(int iter = 2; iter <= nbInputArguments; ++iter) + { + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + /// Three calls to getMatrixOfString + //First Call + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Second call to get length of string + piLen = (int*) malloc(sizeof(int) * iRows1 * iCols1); + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows1, &iCols1, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // third call + pstData = (char**) malloc(sizeof(char*) * iRows1 * iCols1); + for(int k = 0; k < iRows1 * iCols1; ++k) + { + pstData[k] = (char*) malloc(sizeof(char) * piLen[k] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows1, &iCols1, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + currentArg = pstData[0]; + + // getting filterbyArea + if(strcmp(currentArg, "filterByArea") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &area); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// filterbyThreshold + else if(strcmp(currentArg, "filterByThreshold") == 0) + { + if(iter+1 <= nbInputArguments && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &threshold); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[1] = 1; + } + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// filterbycircularity + else if(strcmp(currentArg, "filterByCircularity") == 0) + { + if(iter+1 <= nbInputArguments && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &circularity); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[2] = 1; + } + else if(providedArgs[2]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// filterbyConvexity + else if(strcmp(currentArg, "filterByConvexity") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &convexity); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + + providedArgs[3] = 1; + } + else if(providedArgs[3]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + else + { + Scierror(9,"Invalid argument passed"); + return 0; + } + } + /// End of error check and input get; + + /// params for holding the parameters values; + SimpleBlobDetector::Params params; + + /// if area is provided + if(providedArgs[0] == 1){ + params.filterByArea = true; + params.minArea = area[0]; + params.maxArea = area[1]; + } + + /// if threshold is provided + if(providedArgs[1] == 1){ + params.minThreshold = threshold[0]; + params.maxThreshold = threshold[1]; + } + + /// if Circularity is provided + if(providedArgs[2] == 1){ + params.filterByCircularity = true; + params.minCircularity = circularity[0]; + params.maxCircularity = circularity[1]; + } + + /// if convexity is provided + if(providedArgs[3] == 1){ + params.filterByConvexity = true; + params.minConvexity = convexity[0]; + params.maxConvexity = convexity[1]; + } + + + SimpleBlobDetector detector( params ); + + /// to store the keypoints of blobs detected + vector keyPoints; + + /// fuction to detect blob in the image + detector.detect(image, keyPoints); + + //sciprint("%d", keyPoints.size()); + + key_value = (double*)malloc(sizeof(double) * (int)keyPoints.size() * 2); + + double *size_value = NULL; + + size_value = (double*)malloc(sizeof(double) * (int)keyPoints.size() * 1); + + int total_keypoints = (int)keyPoints.size(); + + vector< KeyPoint >::iterator it; + + for(it = keyPoints.begin(); it != keyPoints.end(); ++it) + { + KeyPoint temp = keyPoints[i]; + + // x coordinate + key_value[i + 0 * total_keypoints] = it->pt.x; + + // y coordinate + key_value[i + 1 * total_keypoints] = it->pt.y; + + // size + size_value[i + 0 * total_keypoints] = it->size; + + //area + //key_value[i + 3 * total_keypoints] = it->Area; + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, total_keypoints, 2, key_value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, total_keypoints, 1, size_value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_blur.cpp b/sci_gateway1/cpp/opencv_blur.cpp new file mode 100644 index 0000000..3db34ca --- /dev/null +++ b/sci_gateway1/cpp/opencv_blur.cpp @@ -0,0 +1,134 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + + + int opencv_blur(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + double ksize_width,ksize_height,anchorX,anchorY; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of ksize_height + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &ksize_height); + if(intErr) + return intErr; + + //for value of ksize_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&ksize_width); + if(intErr) + return intErr; + + //for value of anchorX + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &anchorX); + if(intErr) + return intErr; + + //for value of anchorY + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &anchorY); + if(intErr) + return intErr; + + //applying checks on input parameters + Mat new_image(image.rows,image.cols,image.type()); + + //error checks + if(ksize_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead"); + ksize_height=1; + } + if(ksize_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead"); + ksize_width=1; + } + if(ksize_width != ksize_height) + { + sciprint("Blurring Kernel Size not equal. Value of ksize_height used for ksize_width as well"); + ksize_width = ksize_height; + } + if(anchorX >= ksize_height || anchorY >= ksize_width) + { + sciprint("Invalid anchor points. Default point (-1,-1) used instead"); + anchorY=-1; + anchorX=-1; + } + + //defining temporary size and point type variables to use in the function + Size sz(ksize_height,ksize_width); + Point pt(anchorX,anchorY); + + //void blur(InputArray src, OutputArray dst, Size ksize, Point anchor=Point(-1,-1), int borderType=BORDER_DEFAULT ) + blur(image,new_image,sz,pt); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_boundingRect.cpp b/sci_gateway1/cpp/opencv_boundingRect.cpp new file mode 100644 index 0000000..5bf8707 --- /dev/null +++ b/sci_gateway1/cpp/opencv_boundingRect.cpp @@ -0,0 +1,82 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + rectangle_dimensions_arry(height, width, x, y) = boundingRect ( vector ) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_boundingRect(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddrNew = NULL; + int i,j,k ; + double *points; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + +//retreive the points + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols,&points); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + vector actualPoints; + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_bundleAdjustment(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j; + + int iRows = 0; + int iCols = 0; + int iRows1 = 0; + int iCols1 = 0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piChild = NULL; + int *piAddrNew = NULL; + int iItem = 0; + double *pdblReal = NULL; + + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + + double *cameraMatrix = NULL; + double *rotationMatrix = NULL; + double *translationMatrix = NULL; + double *newCameraMatrix = NULL; + double *distCoeffs = NULL; + + /// checking input argument + + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1); + + // cv::LevMarqSparse::bundleAdjust ( vector< Point3d > & points, + // const vector< vector< Point2d > > & imagePoints, + // const vector< vector< input > > & visibility, + // vector< Mat > & cameraMatrix, + // vector< Mat > & R, + // vector< Mat > & T, + // vector< Mat > & distCoeffs, + // const TermCriteria & criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON), + // BundleAdjustCallback cb = 0, + // void * user_data = 0 + // ) + + /////////// getting the 3d points + vector< Point3d > points; + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(i=0; i > imagePoints; + + imagePoints.resize(iRows); + + for(i=0; i > visibility(iRows, vector< int >(iCols, 0)); + + for(i=0; i camera; + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = getListItemNumber(pvApiCtx, piAddr, &iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(int item = 0; item(i, j) = cameraMatrix[i + j * 3]; + + camera.push_back(temp); + + } + + /// M * 3 * 3 rotation matrix + vector< Mat > rotation; + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = getListItemNumber(pvApiCtx, piAddr, &iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(int item = 0; item(i, j) = rotationMatrix[i + j * iRows]; + + rotation.push_back(temp); + + } + + + /// M * 3 * 1 translation matrix + vector< Mat > translation; + sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = getListItemNumber(pvApiCtx, piAddr, &iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(int item = 0; item(i, j) = translationMatrix[i + j * 3]; + + translation.push_back(temp); + + } + + + /// M * (4, 5, 8) * 1 distCoefficients matrix + vector< Mat > distCoefficients; + sciErr = getVarAddressFromPosition(pvApiCtx, 7, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = getListItemNumber(pvApiCtx, piAddr, &iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(int item = 0; item(i, j) = distCoeffs[i + j * iRows]; + + distCoefficients.push_back(temp); + + } + + // cv::LevMarqSparse::bundleAdjust ( vector< Point3d > & points, + // const vector< vector< Point2d > > & imagePoints, + // const vector< vector< input > > & visibility, + // vector< Mat > & cameraMatrix, + // vector< Mat > & R, + // vector< Mat > & T, + // vector< Mat > & distCoeffs, + // const TermCriteria & criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON), + // BundleAdjustCallback cb = 0, + // void * user_data = 0 + // ) + + /// TermCriteria + + TermCriteria criteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 30, DBL_EPSILON); + + LevMarqSparse lms; + lms.bundleAdjust(points, imagePoints, visibility, camera, rotation, translation, distCoefficients, criteria); + + + double *ans = NULL; + + ans = (double*)malloc(sizeof(double) * points.size() * 3); + + for(i = 0; i < points.size(); ++i){ + ans[i + 0 * points.size()] = points[i].x; + ans[i + 1 * points.size()] = points[i].y; + ans[i + 2 * points.size()] = points[i].z; + } + + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, points.size(), 3 , ans); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_cameraMatrix.cpp b/sci_gateway1/cpp/opencv_cameraMatrix.cpp new file mode 100644 index 0000000..3110017 --- /dev/null +++ b/sci_gateway1/cpp/opencv_cameraMatrix.cpp @@ -0,0 +1,186 @@ +/*************************************************** +Author : Tanmay Chaudhari + **************************************************/ + +#include +#include +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" + + int opencv_cameraMatrix(char *fname, unsigned long fname_len) + { + //Error management variable + SciErr sciErr; + + //Variable declaration + int iComplex = 0; + int iType = 0; + int iRows = 0; + int iCols = 0; + int *piAddr = NULL; + double *instrinsicMatrix = NULL; + double *rotationMatrix = NULL; + double *translationVector = NULL; + double *camMatrix = NULL; + double *tempMatrix = NULL; + + //Check input output arguments + checkInputArgument(pvApiCtx, 3, 3); + checkOutputArgument(pvApiCtx, 1, 1); + + //Get variable address of the first input argument + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &instrinsicMatrix); + if(sciErr.iErr || iCols != 3 || iRows != 3) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the second input argument + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &rotationMatrix); + if(sciErr.iErr || iCols != 3 || iRows != 3) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the first third argument + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &translationVector); + if(sciErr.iErr || iCols != 3 || iRows != 1) + { + printError(&sciErr, 0); + return 0; + } + + tempMatrix = (double*)malloc(sizeof(double) * 12); + + for(int i = 0; i < 4; i++) + { + for(int j = 0; j < 3; j++) + { + if(i == 3) + tempMatrix[j * 4 + i] = translationVector[j]; + else + tempMatrix[j * 4 + i] = rotationMatrix[j * 3 + i]; + } + } + + camMatrix = (double*)malloc(sizeof(double) * 12); + for(int i = 0; i < 12; i++) + camMatrix[i] = 0; + + for(int i = 0; i < 4; i++) + { + for(int j = 0; j < 3; j++) + { + for(int k = 0; k < 3; k++) + { + camMatrix[j * 4 + i] += (tempMatrix[k * 4 + i] * instrinsicMatrix[j * 3 + k]); + } + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 4, 3, camMatrix); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Return output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} + + + diff --git a/sci_gateway1/cpp/opencv_canny.cpp b/sci_gateway1/cpp/opencv_canny.cpp new file mode 100644 index 0000000..010d634 --- /dev/null +++ b/sci_gateway1/cpp/opencv_canny.cpp @@ -0,0 +1,121 @@ +/******************************************************** +Authors: Sukul Bagai, Abhilasha Sancheti, Shubheksha Jalan +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int opencv_canny(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double aperture, threshold1, threshold2, gradient; + bool L2gradient = false; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + cvtColor( image, image, CV_BGR2GRAY ); + + //for value of threshold1 + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &threshold1); + if(intErr) + { + return intErr; + } + + + //for value of threshold2 + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &threshold2); + if(intErr) + { + return intErr; + } + + + //for value of aperture + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &aperture); + if(intErr) + { + return intErr; + } + + + //for value of gradient as an integer and convert to bool accordingly + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &gradient); + if(intErr) + { + return intErr; + } + + if ( gradient == 1){ + L2gradient = true; + } + Mat new_image; + Canny(image, new_image, threshold1, threshold2, aperture, L2gradient); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_circle.cpp b/sci_gateway1/cpp/opencv_circle.cpp new file mode 100644 index 0000000..928c251 --- /dev/null +++ b/sci_gateway1/cpp/opencv_circle.cpp @@ -0,0 +1,203 @@ +/******************************************************** + Author: Abhilasha Sancheti +********************************************************* + return_image = circle(input_image , x_coordinate_of_centre , y_coordinate_centre , radius, r_value,g_value,b_value,thickness,linetype,shift); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_circle(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int i,j,k; + double thickness=1 , linetype=8,shift =0 ,centre_x ,centre_y,radius,r_value,g_value,b_value; + + + //checking input argument + CheckInputArgument(pvApiCtx, 10, 10); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of centre + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,¢re_x); + if(intErr) + return intErr; + + + //for value of y coordinate of centre + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,¢re_y); + if(intErr) + return intErr; + + //for value of radius + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&radius); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&r_value); + if(intErr) + return intErr; + + //for value of G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7,&b_value); + if(intErr) + return intErr; + + // for thickness of circle default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&thickness); + if(intErr) + return intErr; + + //for line type of circle default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr9,&linetype); + if(intErr) + return intErr; + + // for shift in circle defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Syntax: sciErr = getMatrixOfInteger8(pvApiCtx, piAddr8, &iRows8, &iCols8, &pcData); + intErr = getScalarDouble(pvApiCtx, piAddr10,&shift); + if(intErr) + return intErr; + + // checking radius parameter + if (radius <0) + { + radius=3; + sciprint("Radius should be positive , 3 value was used instead\n"); + } + // checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead\n"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead\n"); + } + //temporary pt variable, to use in function as centre + Point pt(centre_x,centre_y); + /// Draw the circles detected + + circle( src, pt, radius, Scalar(r_value,g_value,b_value), thickness, linetype, shift); + +//// sending the image + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway1/cpp/opencv_clipline.cpp b/sci_gateway1/cpp/opencv_clipline.cpp new file mode 100644 index 0000000..2e13412 --- /dev/null +++ b/sci_gateway1/cpp/opencv_clipline.cpp @@ -0,0 +1,149 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + return_image = clipline( width, height, x1 , y1 , x2,y2 ) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_clipline(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int i,j,k; + double x1,y1,x2,y2,width,height; + + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + //for width of the rectangle + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr1 ,&width); + if(intErr) + return intErr; + + // for height of the rectangle + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,&height); + if(intErr) + return intErr; + + //for value of x coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &x1); + if(intErr) + return intErr; + + //for value of y coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&y1); + if(intErr) + return intErr; + + //for value of x coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&x2); + if(intErr) + return intErr; + + ///for value of y coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&y2); + if(intErr) + return intErr; + + + if(height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead"); + height=1; + } + if(width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead"); + width=1; + } + //temporary pt1 variable, to use in function as first line point + Point pt1(x1,y1); + //temporary pt2 variable, to use in function as second line point + Point pt2(x2,y2); + // image size + Size sz(width,height); + Rect r(0,0,width,height); + //// calling open cv clipLine function + + bool ans = clipLine( sz , pt1, pt2); + int actualans=(int)ans; + + // returning the result to the scilab environment + // Syntax: SciErr createMatrixOfBoolean(void* _pvCtx, int _iVar, int _iRows, int _iCols, const int* _piBool) + intErr= createScalarBoolean(pvApiCtx, nbInputArgument(pvApiCtx) + 1, actualans); + if(intErr) + return intErr; + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway1/cpp/opencv_convexhull.cpp b/sci_gateway1/cpp/opencv_convexhull.cpp new file mode 100644 index 0000000..934fa20 --- /dev/null +++ b/sci_gateway1/cpp/opencv_convexhull.cpp @@ -0,0 +1,140 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + hull = convexhull ( contour(vector), clockwise(1/0) , returnpoints(1/0) ) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_convexhull(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + double *pstData = NULL; + int i,j,k ; + double clkwise; + double returnpoints; + + + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + +//retreive the contour parameter + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols,&pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //cout<<*(pstData + 0*2 + 0)<<" "<<*(pstData + 0*2 + 1)< cont1; + for ( i = 0 ; i < iRows ; i++) + { + Point pt(*(pstData + 0*iRows + i),*(pstData + 1*iRows + i)); + cont1.push_back(pt); + } + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&clkwise); + if(intErr) + return intErr; + + + +//// to retreive the value of returnpoint + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&returnpoints); + if(intErr) + return intErr; + + + bool clkWise, returnPoint; +/// setting clkwise parameter of function + if ( clkwise ==1) + clkWise = true; + else if (clkwise == 0) + clkWise = false; + else + { + clkWise = false; + sciprint("only 0/1 acceptable , taking 0 instead"); + } +///// setting returnpoint parameter of function + if ( returnpoints ==1) + returnPoint = true; + else if (returnpoints == 0) + returnPoint = false; + else + { + returnPoint = true; + sciprint("only 0/1 acceptable , taking 1 instead"); + } + + vector hull; + convexHull(Mat(cont1), hull , clkWise , returnPoint); + int row = hull.size(); + double *cor = (double *)malloc(row*2*sizeof(double)); + for (int i=0;i To execute, Image = convolver(I1, size, values,scalar) +* where 'I1' is image to be convoluted, +* where 'size' is size of kernel i.e size x size gives total no. of values in kernel, +* where 'values' contains the values of the kernel +* where 'scalar' is a float value +* +*******************************************************************************************/ +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + // # include "../common.cpp" + + int opencv_convolver(char *fname, unsigned long fname_len) + { + //-> Error Management variables + SciErr sciErr; + int intErr=0; + + //-> Mat containers for images + Mat image_1; + Mat image_2; + + //-> Address of Various Arguments + int *piAddr = NULL; + + //-> Local Variables + double *values1 = NULL; + float *values2 =NULL; + double size; + double scalar; + int iRows = 0; + int iCols = 0; + int *outList = NULL; + unsigned char *red = NULL; + unsigned char *green = NULL; + unsigned char *blue = NULL; + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + CheckInputArgument(pvApiCtx, 1, 5); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 5); //Check on Number of Output Arguments + + //-> Read Image + retrieveImage( image_1, 1); + //retrieveImage(image_2, 2); + + //-> Count number of input arguments + //num_InputArgs = *getNbInputArgument(pvApiCtx); + + //-> Based on number of input arguments + +//***************************************************** Actual Processing ************************************************************* + + //-> Taking in size of kernel + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //intErr = getScalarInteger32(pvApiCtx, piAddr, &size); + intErr = getScalarDouble(pvApiCtx, piAddr, &size); + if(intErr) + { + return intErr; + } + + if( size > 5) + { + Scierror(999," Invalid Value size. Please enter a non negative Double value\\n"); + return 0; + } + + //-> Taking the kernel values + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &values1); + //sciErr = getMatrixOfInteger32(pvApiCtx, piAddr, &iRows, &iCols, &values); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=9 && iRows*iCols!=16 && iRows*iCols!=25 ) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + values2 = (float*)malloc(sizeof(float)*size*size); + for(int i=0; i Taking scalar values + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr, &scalar); + if(intErr) + { + return intErr; + } + + if( scalar == 0) + { + Scierror(999," Invalid scalar value. Please enter a non negative Double value\\n"); + return 0; + } + + //float kdata[] = {1, 4, 6, -1, 3, 5, -1, -2, 2}; + Mat kernel(size,size,CV_32F, values2); + + Mat kernel2 = kernel/ (float)(scalar); + + Point anchor; + double delta; + int ddepth; + + anchor = Point( -1, -1 ); + delta = 0; + ddepth = -1; + + //filter2D(image_1, image_2, CV_32F, kernel); + filter2D(image_1, image_2, ddepth,kernel2,anchor,delta, BORDER_DEFAULT); + + //imshow("Convoluted Image",image_2); + //waitKey(0); + + if( image_2.channels() == 1) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + red = (unsigned char *)malloc(sizeof(unsigned char)*image_2.rows*image_2.cols); + + for(int k=0;k(k, p); + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, image_2.rows, image_2.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + } + + else + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + red = (unsigned char *)malloc(sizeof(unsigned char)*image_2.rows*image_2.cols); + green = (unsigned char *)malloc(sizeof(unsigned char)*image_2.rows*image_2.cols); + blue = (unsigned char *)malloc(sizeof(unsigned char)*image_2.rows*image_2.cols); + + for(int k=0;k(k, p); + red[k+image_2.rows*p]=intensity.val[2]; + green[k+image_2.rows*p]=intensity.val[1]; + blue[k+image_2.rows*p]=intensity.val[0]; + } + } + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, image_2.rows, image_2.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 2, image_2.rows, image_2.cols, green); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 3, image_2.rows, image_2.cols, blue); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + free(green); + free(blue); + + } + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } + +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_copymakeborder.cpp b/sci_gateway1/cpp/opencv_copymakeborder.cpp new file mode 100644 index 0000000..51175fb --- /dev/null +++ b/sci_gateway1/cpp/opencv_copymakeborder.cpp @@ -0,0 +1,195 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* + void copyMakeBorder(InputArray image, int top, int bottom, int left, int right, int borderType, const Scalar& value=Scalar() ) +********************************************************/ +// for BORDER_CONSTANT, enter a 4 element array as Scalar. For any other border type, pass 0. + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_copymakeborder(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int i,j,k; + char **borderType; + double top, bottom, right, left; + double *value = NULL; + //checking input argument + CheckInputArgument(pvApiCtx, 6, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for top + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &top); + if(intErr) + { + return intErr; + } + + //for bottom + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &bottom); + if(intErr) + { + return intErr; + } + + //for left + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &left); + if(intErr) + { + return intErr; + } + + //for bottom + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &right); + if(intErr) + { + return intErr; + } + + + sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr6, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr6, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr6, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for array of Scalar object + sciErr = getVarAddressFromPosition(pvApiCtx, 7, &piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr7, &iRows, &iCols ,&value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Mat new_image(Size(image.cols+left+right, image.rows+top+bottom), image.type()); + + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0 && iCols == 4 ) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_CONSTANT, Scalar(value[0], value[1], value[2], value[3])); + else if(strcmp(borderType[0], "BORDER_CONSTANT") == 0 && iCols == 3) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_CONSTANT, Scalar(value[0], value[1], value[2])); + else if(strcmp(borderType[0], "BORDER_DEFAULT") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_DEFAULT); + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_REPLICATE); + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_REFLECT); + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_REFLECT_101); + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + copyMakeBorder(image, new_image, top, bottom, left, right, BORDER_WRAP); + + // namedWindow ("window", 1); + // imshow("window", new_image); + // waitKey(0); + // destroyWindow("window"); + +free(borderType); + +string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_corner.cpp b/sci_gateway1/cpp/opencv_corner.cpp new file mode 100644 index 0000000..96c1f4c --- /dev/null +++ b/sci_gateway1/cpp/opencv_corner.cpp @@ -0,0 +1,469 @@ +/*************************************************** +Author : Rohit Suri +***************************************************/ +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + /*Calling syntax: corner(I) or corner(I, Name, Value) + which uses additional options specified by one or more Name, Value pair arguments. + Arguments Allowed: MinQuality, FilterSize, ROI, SensitivityFactor + MinQuality : The minimum accepted quality of corners represents a fraction of the + maximum corner metric value in the image. Larger values can be used + to remove erroneous corners. + FilterSize : Gaussian filter dimension + ROI : Rectangular region for corner detection + SensitivityFactor : A scalar value, K, where 0 < K < 0.25, specifying the sensitivity + factor used in the Harris detection algorithm. + Method : Whether to use 'Harris' or 'MinimumEigenValue' technique */ + + int opencv_corner(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + int intErr; + + //------Local variables------// + int *location = NULL; + double *metric = NULL; + int *piAddr = NULL; + int *piLen = NULL; + int nbInputArguments; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + double *matrixOfRoi; // ROI[xStart, yStart, width, height] + int iRows, iCols; + Mat sourceImage, dst, dstThresholded, ucharDstThresholded, extended; + vector > contours; + char *method; + double filterSize = 5, minQuality = 0.01, sensitivityFactor = 0.04, blockSize = 2, maxDst, localMax; + bool *included = NULL; + int pointCount = 0, localMaxPos; + double tempForSwapping; + int coordinateMin, coordinatePos; + + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 1, 11); + CheckOutputArgument(pvApiCtx, 1, 3); + + //------Get input arguments------// + retrieveImage(sourceImage, 1); + if(sourceImage.channels() != 1) + { + Scierror(999, "The input image is not a grayscale image."); + return 0; + } + matrixOfRoi = (double*) malloc(sizeof(double) * 4); + providedArgs = (bool*) malloc(sizeof(bool) * 5); + memset(providedArgs, 0, 5); + method = "Harris"; + matrixOfRoi[0] = 0; + matrixOfRoi[1] = 0; + matrixOfRoi[2] = sourceImage.cols; + matrixOfRoi[3] = sourceImage.rows; + nbInputArguments = *getNbInputArgument(pvApiCtx); + + // The following loop is for checking if optional arguments have been provided + for(int iter=2; iter <= nbInputArguments; iter++) + { + // Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + // providedArgs[] makes sure that no optional argument is provided more than once + if(strcmp(currentArg, "MinQuality")==0) + { + if(iter+1<=nbInputArguments && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &minQuality); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(minQuality < 0 || minQuality > 1) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible range is [0, 1].\n", currentArg); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "FilterSize")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &filterSize); + if(intErr) + { + return intErr; + } + providedArgs[1] = 1; + if(filterSize!=1 && filterSize!=3 && filterSize!=5 && filterSize!=7) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are {1, 3, 5, 7}.\n", currentArg); + return 0; + } + } + else if(providedArgs[1]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "ROI")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, iter); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &matrixOfRoi); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows!=1 || iCols!=4) + { + Scierror(999, "Incorrect dimension of matrix for argument ROI.\n"); + return 0; + } + providedArgs[2] = 1; + if(matrixOfRoi[0] < 0 || matrixOfRoi[0] > sourceImage.cols || matrixOfRoi[1] < 0 || + matrixOfRoi[1] > sourceImage.rows || matrixOfRoi[2] < 0 || matrixOfRoi[0] + matrixOfRoi[2] > sourceImage.cols + || matrixOfRoi[3] < 0 || matrixOfRoi[1] + matrixOfRoi[3] > sourceImage.rows) + { + Scierror(999, "Error: Please provide proper values for \"%s\".\n", currentArg); + return 0; + } + } + else if(providedArgs[2]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "SensitivityFactor")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &sensitivityFactor); + if(intErr) + { + return intErr; + } + providedArgs[3] = 1; + if(sensitivityFactor < 0 || sensitivityFactor > 0.25) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are [0, 0.25].\n", currentArg); + return 0; + } + } + else if(providedArgs[3]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "Method")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[4]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + method = pstData[0]; + providedArgs[4] = 1; + if(strcmp(method, "Harris") !=0 && strcmp(method, "MinimumEigenValue") !=0) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are [\"Harris\" , \"MinimumEigenValue\"].\n", currentArg); + return 0; + } + } + else if(providedArgs[4]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: \"%s\" name for input argument is not valid.\n", currentArg); + return 0; + } + } + + //------Actual processing------// + if(strcmp(method, "Harris") == 0) + { + cornerHarris(sourceImage(Rect(matrixOfRoi[0], matrixOfRoi[1], matrixOfRoi[2], matrixOfRoi[3])), dst, blockSize, filterSize, sensitivityFactor, BORDER_DEFAULT); + } + else + { + cornerMinEigenVal(sourceImage(Rect(matrixOfRoi[0], matrixOfRoi[1], matrixOfRoi[2], matrixOfRoi[3])), dst, blockSize, filterSize, BORDER_DEFAULT); + } + // Finding the maximum value at a pixel in dst + maxDst = 0; + for(int rowIter=0; rowIter < dst.rows; rowIter++) + { + for(int colIter=0; colIter < dst.cols; colIter++) + { + if(dst.at(rowIter, colIter) > maxDst) + { + maxDst = dst.at(rowIter, colIter); + } + } + } + + /* First, we threshold dst according to the minQuality expected by the user. + We then find contours in the thresholded image and check each pixel's value + to compute the local maxima. A extended mat is created because contours + having edges on the boundaries of the images are not considered by + findContours */ + threshold(dst, dstThresholded, maxDst*minQuality, 255, THRESH_BINARY); + dstThresholded.convertTo(ucharDstThresholded, CV_8UC1); + extended = Mat(dst.rows+2, dst.cols+2, CV_8UC1); + for(int rowIter = 0; rowIter < extended.rows; rowIter++) + { + for(int colIter = 0; colIter < extended.cols; colIter++) + { + if(rowIter == 0 || rowIter == extended.rows - 1 + || colIter == 0 || colIter == extended.cols - 1) + { + extended.at(rowIter, colIter) = 0; + } + else + { + extended.at(rowIter, colIter) = ucharDstThresholded.at(rowIter-1, colIter-1); + } + } + } + + findContours(extended, contours, CV_RETR_TREE, CV_CHAIN_APPROX_NONE); + pointCount = contours.size(); + location = (int*) malloc(sizeof(int) * pointCount * 2); + metric = (double*) malloc(sizeof(double)*pointCount); + for(int rowIter = 0; rowIter < contours.size(); rowIter++) + { + localMax = 0; + localMaxPos = 0; + for(int colIter=0; colIter(contours[rowIter][colIter].y-1, contours[rowIter][colIter].x-1)>localMax) + { + localMax = dst.at(contours[rowIter][colIter].y-1, contours[rowIter][colIter].x-1); + localMaxPos = colIter; + } + } + if(localMax > maxDst*minQuality) + { + location[rowIter] = contours[rowIter][localMaxPos].x-1 + matrixOfRoi[0]; + location[pointCount + rowIter] = contours[rowIter][localMaxPos].y-1 + matrixOfRoi[1]; + metric[rowIter] = dst.at(location[pointCount + rowIter], location[rowIter]); + } + } + + // To return coordinates in order + for(int iter1 = 0; iter1 < pointCount - 1; iter1++) + { + coordinateMin = location[iter1]; + coordinatePos = iter1; + for(int iter2 = iter1 + 1; iter2 < pointCount; iter2++) + { + if(location[iter2] < coordinateMin) + { + coordinateMin = location[iter2]; + coordinatePos = iter2; + } + else if(location[iter2] == coordinateMin) + { + if(location[pointCount + iter2] < location[pointCount + coordinatePos]) + { + coordinateMin = location[iter2]; + coordinatePos = iter2; + } + } + } + // Swapping x coordinate + tempForSwapping = location[coordinatePos]; + location[coordinatePos] = location[iter1]; + location[iter1] = tempForSwapping; + // Swapping y coordinate + tempForSwapping = location[pointCount + coordinatePos]; + location[pointCount + coordinatePos] = location[pointCount + iter1]; + location[pointCount + iter1] = tempForSwapping; + // Swapping metric + tempForSwapping = metric[coordinatePos]; + metric[coordinatePos] = metric[iter1]; + metric[iter1] = tempForSwapping; + } + + //------Create output arguments------// + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx)+1, pointCount, 2, location); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+2, pointCount, 1, metric); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx)+3, 1, 1, &pointCount); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx)+2; + AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx)+3; + ReturnArguments(pvApiCtx); + return 0; + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_cornerEigenValsAndVecs.cpp b/sci_gateway1/cpp/opencv_cornerEigenValsAndVecs.cpp new file mode 100644 index 0000000..205759d --- /dev/null +++ b/sci_gateway1/cpp/opencv_cornerEigenValsAndVecs.cpp @@ -0,0 +1,127 @@ +/******************************************************** +Author: Shubheksha Jalan +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + + int opencv_cornerEigenValsAndVecs(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + double blockSize, ksize; + char **borderType; + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + cvtColor(image,image,CV_BGR2GRAY); + + //for block size + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &blockSize); + if(intErr) + { + return intErr; + } + + //for ksize + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &ksize); + if(intErr) + { + return intErr; + } + + //for border type + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Mat new_image; + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0){ + cornerEigenValsAndVecs(image, new_image, blockSize, ksize, BORDER_CONSTANT); + } + else if(strcmp(borderType[0], "BORDER_DEFAULT") == 0) + cornerEigenValsAndVecs(image, new_image, blockSize, ksize, BORDER_DEFAULT); + free(borderType); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_cornerHarris.cpp b/sci_gateway1/cpp/opencv_cornerHarris.cpp new file mode 100644 index 0000000..d517eec --- /dev/null +++ b/sci_gateway1/cpp/opencv_cornerHarris.cpp @@ -0,0 +1,144 @@ +/******************************************************** +Author: Shubheksha Jalan +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + + int opencv_cornerHarris(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j; + double blockSize, ksize, k; + char **borderType; + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + cvtColor(image,image,CV_BGR2GRAY); + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &blockSize); + if(intErr) + { + return intErr; + } + + //for ksize + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &ksize); + if(intErr) + { + return intErr; + } +//for value of k + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &k); + if(intErr) + { + return intErr; + } + + //for border type + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr5, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr5, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr5, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + cout<<"String\n"; + + Mat new_image(image.rows, image.cols, CV_32FC1); + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0){ + cornerHarris(image, new_image, blockSize, ksize, k, BORDER_CONSTANT); + } + else if(strcmp(borderType[0], "BORDER_DEFAULT") == 0) + cornerHarris(image, new_image, blockSize, ksize, k, BORDER_DEFAULT); + + namedWindow ("window", 1); + imshow("window", new_image); + waitKey(0); + destroyWindow("window"); + + new_image.convertTo(new_image, CV_32FC1, 1.0/255.0); + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_cornerMinEigenVal.cpp b/sci_gateway1/cpp/opencv_cornerMinEigenVal.cpp new file mode 100644 index 0000000..19123e5 --- /dev/null +++ b/sci_gateway1/cpp/opencv_cornerMinEigenVal.cpp @@ -0,0 +1,125 @@ +/******************************************************** +Author: Shubheksha Jalan +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + + int opencv_cornerMinEigenVal(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + double blockSize, ksize; + char **borderType; + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + cvtColor(image,image,CV_BGR2GRAY); + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &blockSize); + if(intErr) + { + return intErr; + } + + //for ksize + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &ksize); + if(intErr) + { + return intErr; + } + + //for border type + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + cout<<"String\n"; + + Mat new_image; + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0){ + cornerMinEigenVal(image, new_image, blockSize, ksize, BORDER_CONSTANT); + } + else if(strcmp(borderType[0], "BORDER_DEFAULT") == 0) + cornerMinEigenVal(image, new_image, blockSize, ksize, BORDER_DEFAULT); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_cvtColor.cpp b/sci_gateway1/cpp/opencv_cvtColor.cpp new file mode 100644 index 0000000..2959dda --- /dev/null +++ b/sci_gateway1/cpp/opencv_cvtColor.cpp @@ -0,0 +1,183 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* + void cvtColor(InputArray src, int code, int dstCn=0 ) +********************************************************/ +// number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "sciprint.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_cvtColor(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + double dstCn; + int code; + char **codeString = NULL; + int i, j, k; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 3); + CheckOutputArgument(pvApiCtx, 1,1) ; + + + Mat src; + retrieveImage(src,1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + codeString = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + codeString[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, codeString); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &dstCn); + if(intErr){ + return intErr; + } + + Mat dst; + + if(strcmp(codeString[0], "CV_BGR2GRAY") == 0) + code = CV_BGR2GRAY; + else if(strcmp(codeString[0], "CV_RGB2GRAY") == 0) + code = CV_RGB2GRAY; + else if(strcmp(codeString[0], "CV_GRAY2BGR") == 0) + code = CV_GRAY2BGR; + else if(strcmp(codeString[0], "CV_GRAY2RGB") == 0) + code = CV_GRAY2RGB; + else if(strcmp(codeString[0], "CV_BGR2XYZ") == 0) + code = CV_BGR2XYZ; + else if(strcmp(codeString[0], "CV_RGB2XYZ") == 0) + code = CV_RGB2XYZ; + else if(strcmp(codeString[0], "CV_XYZ2BGR") == 0) + code = CV_XYZ2BGR; + else if(strcmp(codeString[0], "CV_XYZ2RGB") == 0) + code = CV_XYZ2RGB; + else if(strcmp(codeString[0], "CV_BGR2YCrCb") == 0) + code = CV_BGR2YCrCb; + else if(strcmp(codeString[0], "CV_RGB2YCrCb") == 0) + code = CV_RGB2YCrCb; + else if(strcmp(codeString[0], "CV_YCrCb2BGR") == 0) + code = CV_YCrCb2BGR; + else if(strcmp(codeString[0], "CV_YCrCb2RGB") == 0) + code = CV_YCrCb2RGB; + else if(strcmp(codeString[0], "CV_BGR2HSV") == 0) + code = CV_BGR2HSV; + else if(strcmp(codeString[0], "CV_RGB2HSV") == 0) + code = CV_RGB2HSV; + else if(strcmp(codeString[0], "CV_HSV2BGR") == 0) + code = CV_HSV2BGR; + else if(strcmp(codeString[0], "CV_HSV2RGB") == 0) + code = CV_HSV2RGB; + else if(strcmp(codeString[0], "CV_BGR2HLS") == 0) + code = CV_BGR2HLS; + else if(strcmp(codeString[0], "CV_RGB2HLS") == 0) + code = CV_RGB2HLS; + else if(strcmp(codeString[0], "CV_HLS2BGR") == 0) + code = CV_HLS2BGR; + else if(strcmp(codeString[0], "CV_HLS2RGB") == 0) + code = CV_HLS2RGB; + else if(strcmp(codeString[0], "CV_BGR2Lab") == 0) + code = CV_BGR2Lab; + else if(strcmp(codeString[0], "CV_RGB2Lab") == 0) + code = CV_RGB2Lab; + else if(strcmp(codeString[0], "CV_Lab2BGR") == 0) + code = CV_Lab2BGR; + else if(strcmp(codeString[0], "CV_Lab2RGB") == 0) + code = CV_Lab2RGB; + else if(strcmp(codeString[0], "CV_BGR2Luv") == 0) + code = CV_BGR2Luv; + else if(strcmp(codeString[0], "CV_RGB2Luv") == 0) + code = CV_RGB2Luv; + else if(strcmp(codeString[0], "CV_Luv2BGR") == 0) + code = CV_Luv2BGR; + else if(strcmp(codeString[0], "CV_Luv2RGB") == 0) + code = CV_Luv2RGB; + else{ + + sciprint("Invalid code"); + return 0; + } + + cvtColor(src, dst, code, dstCn); + + // namedWindow ("window", 1); + // imshow("window", dst); + // waitKey(0); + // destroyWindow("window"); + + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + free(checker); + + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_demosaic.cpp b/sci_gateway1/cpp/opencv_demosaic.cpp new file mode 100644 index 0000000..4c2ee4b --- /dev/null +++ b/sci_gateway1/cpp/opencv_demosaic.cpp @@ -0,0 +1,127 @@ +/******************************************************** + Author: V Srinivas +********************************************************* + void cvtColor(InputArray src, int code, int dstCn=0 ) +********************************************************/ +//Demosaicing using cvt color filter + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "sciprint.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_demosaic(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + double dstCn; + int code; + char **codeString = NULL; + int i, j, k; + + //checking input argument + CheckInputArgument(pvApiCtx, 2,2); + CheckOutputArgument(pvApiCtx, 1,1) ; + + + Mat src; + retrieveImage(src,1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return -1; + } + + codeString = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + codeString[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, codeString); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + + Mat dst; + + if(strcmp(codeString[0], "gbrg") == 0) + code = CV_BayerGR2RGB; + else if(strcmp(codeString[0], "bggr") == 0) + code = CV_BayerRG2RGB; + else if(strcmp(codeString[0], "rggb") == 0) + code = CV_BayerBG2RGB; + else if(strcmp(codeString[0], "grbg") == 0) + code = CV_BayerGB2RGB; + else{ + + sciprint("Invalid Bayer filter code"); + return 0; + } + + cvtColor(src, dst, code, 0); + + // namedWindow ("window", 1); + // imshow("window", dst); + // waitKey(0); + // destroyWindow("window"); + + + string tempstring = type2str(dst.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dst,1); + free(checker); + + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_detectBRISKFeatures.cpp b/sci_gateway1/cpp/opencv_detectBRISKFeatures.cpp new file mode 100644 index 0000000..e6ded8e --- /dev/null +++ b/sci_gateway1/cpp/opencv_detectBRISKFeatures.cpp @@ -0,0 +1,421 @@ +/*************************************************** +Author : Shashank Shekhar +***************************************************/ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_detectBRISKFeatures(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int *piAddr = NULL; + int *piAddrVal = NULL; + int argPresence[4]; + for(int i=0;i<4;i++) + argPresence[i]=0; + int val_position; + int iRows = 0; + int iCols = 0; + int *piLen = NULL; + char ** pstData = NULL; + char *currentArg = NULL; + + +// DECLARING INPUT ARGUMENTS + + double *ROImat = NULL; + double NumOctaves = 0; + double MinContrast = 0; + double MinQuality = 0; + + +// DECLARING OUTPUT ARGUMENTS + + vector myPoints; + int *LocationList = NULL; + double *LocationData = NULL; + double *OrientationData = NULL; + double *ScaleData = NULL; + double *MetricData = NULL; + int size; + +// ARGUMENT CHECK + + CheckInputArgument(pvApiCtx, 1,9); + CheckOutputArgument(pvApiCtx,1,5); + +// RETRIEVING IMAGE + + Mat image; + retrieveImage(image,1); + +// GRAYSCALE VALIDATION + + if(image.channels()!=1) + { + Scierror(999," Feature is not meant for RGB images\n"); + return 0; + } + +// OBTAINING NUMBER OF ARGUMENTS + + int noOfarguments = *getNbInputArgument(pvApiCtx); + +// ARGUMENT COUNT CHECK + + if(noOfarguments%2==0) + { + Scierror(999," Invalid Number Of Arguments\n"); + return 0; + } + +// MULTIPLE ARGUMENT HANDLING + + for(int i=2;i1) + { + Scierror(999,"Invalid Value for MinContrast. Please enter a value between 0 and 1\n"); + return 0; + } + + argPresence[0]=1; + } + else if(strcmp(currentArg,"NumOctaves")==0) + { + val_position=i+1; + + if(argPresence[1]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for NumOctaves. Recommended values are between 1 and 4\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &NumOctaves); + if(NumOctaves<1) + { + Scierror(999," Invalid Value for NumOctaves. Recommended values are between 1 and 4\n"); + return 0; + } + argPresence[1]=1; + } + else if(strcmp(currentArg,"MinQuality")==0) + { + val_position=i+1; + + + if(argPresence[2]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for MinQuality. Recommended values are between 0 and 1\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &MinQuality); + + if(MinQuality<0 || MinQuality>1) + { + Scierror(999," Invalid Value for MinQuality. Please enter a value between 0 and 1 \n"); + return 0; + } + + argPresence[2]=1; + } + else if(strcmp(currentArg,"ROI")==0) + { + val_position=i+1; + + if(argPresence[3]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx, piAddrVal)) + { + Scierror(999,"Enter a List of 4 arguments\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddrVal, &iRows, &iCols, &ROImat); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=4) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + if(ROImat[0]<0 || ROImat[1]<0 || ROImat[2]<0 || ROImat[3]<0) + { + Scierror(999,"Arguments cannot be negative\n"); + return 0; + } + + if(ROImat[0]>image.cols || ROImat[1]>image.rows || ROImat[0]+ROImat[2]>image.cols + || ROImat[1]+ROImat[3]>image.rows) + { + Scierror(999,"Please make sure the arguments are within the image range\n"); + return 0; + } + + argPresence[3]=1; + } + else + { + Scierror(999, "Error: \"%s\" Invalid input argument.\n", currentArg); + return 0; + } + + } + + +// DEFAULT ARGUMENTS + + if(argPresence[0]==0) + MinContrast=0.2; + if(argPresence[1]==0) + NumOctaves=4; + if(argPresence[2]==0) + MinQuality=0.1; + if(argPresence[3]==0) + { + ROImat=(double *)malloc(sizeof(double)*1*4); + ROImat[0]=0; + ROImat[1]=0; + ROImat[2]=image.cols; + ROImat[3]=image.rows; + } + +// CREATING REGION OF INTEREST + + Rect masker(ROImat[0], ROImat[1], ROImat[2], ROImat[3]); + + Mat mask(image.size(), CV_8UC1, Scalar::all(0)); + mask(masker).setTo(Scalar::all(255)); + +// PROCESSING BRISK + + + // Setting Threshold -------------------------------------------------> + + double Threshold = MinContrast * 255; + + // Limiting NumOctaves -----------------------------------------------> + + int g = image.rows>image.cols?image.rows:image.cols; + int maxNumOctaves = int(floor(log2(g))); + if(NumOctaves>maxNumOctaves) + NumOctaves=maxNumOctaves; + + //----------------------------------------------------------------------- + + BRISK myobject(Threshold, NumOctaves); + myobject.operator()(image, mask, myPoints, noArray()); + + // Selecting Points according to MinQuality ---------------------------> + + size = myPoints.size(); + + double maxMetric = 0; + double minMetric = 0; + + for(int i=0;imaxMetric) + maxMetric=myPoints[i].response; + } + minMetric = maxMetric*MinQuality; + + int finalSize = 0; + for(int i=0;i=minMetric) + finalSize++; + } + + //------------------------------------------------------------------------ + +// LOCATION ----> + + LocationData = (double *)malloc(sizeof(double)*finalSize*2); + MetricData = (double *)malloc(sizeof(double)*finalSize); + OrientationData = (double *)malloc(sizeof(double)*finalSize); + ScaleData = (double *)malloc(sizeof(double)*finalSize); + int k=0; + // Appending 1x2 Matrices containing the coordinates of the keypoints, as well as assiging other attributes + for(int i=0;i=minMetric) + { + LocationData[k++] = myPoints[i].pt.x; + LocationData[k++] = myPoints[i].pt.y; + OrientationData[i] = 0; + MetricData[i] = myPoints[i].response; + ScaleData[i] = myPoints[i].size; + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, finalSize, 2, LocationData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2, finalSize, 1, OrientationData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 3, finalSize,1, MetricData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 4,finalSize,1, ScaleData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + createScalarInteger32(pvApiCtx,nbInputArgument(pvApiCtx) + 5, finalSize); + +// ASSIGNING ----> + + for(int i=1;i<=5;i++) + { + AssignOutputVariable(pvApiCtx, i) = nbInputArgument(pvApiCtx) + i; + } + + ReturnArguments(pvApiCtx); + } + +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_detectCheckerboardPoints.cpp b/sci_gateway1/cpp/opencv_detectCheckerboardPoints.cpp new file mode 100644 index 0000000..33c951c --- /dev/null +++ b/sci_gateway1/cpp/opencv_detectCheckerboardPoints.cpp @@ -0,0 +1,731 @@ +/*************************************************** +Author : Rohit Suri +***************************************************/ +#include +#include +#include +#include +#include + +using namespace std; +using namespace cv; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + //function used to retrieve image matrix from Scilab environment + int retrieveImageFromAddress(Mat &image,int *piAddr) + { + SciErr sciErr; + int iRows=0,iCols=0,i,j,k=0; + int *piChild = NULL; + int iPrec = 0,iItem = 0; + sciErr = getListItemAddress(pvApiCtx,piAddr,1,&piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isIntegerType(pvApiCtx, piChild)) + { + //getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + if(iItem==3) + { + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC3); + + /*Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this*/ + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + unsigned char *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_8UC1); + + //Assigning matrix value to pixel + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + if(iItem==3) + { + short unsigned int *pstDataR = NULL; + short unsigned int *pstDataG = NULL; + short unsigned int *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + //i+iRows*j + } + } + } + else + { + short unsigned int *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16UC1); + + //Assigning matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + case SCI_INT16: //for signed integer 16 + { + if(iItem==3) + { + short int *pstDataR = NULL; + short int *pstDataG = NULL; + short int *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + short int *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfInteger16InList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_16SC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + break; + } + } + } + else //for floating point/ double precision values + { + if(iItem==3) + { + double *pstDataR = NULL; + double *pstDataG = NULL; + double *pstDataB = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the G values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 2, &iRows, &iCols, &pstDataG); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //retrive the matrix of the B values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 3, &iRows, &iCols, &pstDataB); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC3); + + //Now that we have the 3 matrices(R,G,B), we need to assign those values to the pixels. The following code does this + k=0; + for(i=0;i(i,j)[2]=pstDataR[i+iRows*j]; + image.at(i,j)[1]=pstDataG[i+iRows*j]; + image.at(i,j)[0]=pstDataB[i+iRows*j]; + } + } + } + else + { + double *pstDataR = NULL; + //retrive the matrix of the R values + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, 1, &iRows, &iCols, &pstDataR); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating an image matrix with the no. of rows and columns we retrieved, and assigning it to be of the form 8-bit unsinged integers + image = Mat(iRows,iCols,CV_64FC1); + + //Assigning image matrix values to pixels + k=0; + for(i=0;i(i,j)=pstDataR[i+iRows*j]; + } + } + return 1; + } + int opencv_detectCheckerboardPoints(char *fname, unsigned long fname_len) + { + // Error management variables + + SciErr sciErr; + + //------Local variables------// + + int *piAddr; + int *piChild; + int *piGrandChild; + int iItem; + int iItemInner; + int argCount; + int iRows,iCols; + int *piLen; + char **pstData; + + int imgCount[2]={0}; + int returnSize[2]; + vector inputs; + Mat inp; + vector > chessboardPoints; + vector chessboardSize; + vector v; + bool detected; + int *usedImages; + int ***points; + int count = 0,pointsPos; + //------Check number of parameters------// + + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 2, 3); + argCount = *getNbInputArgument(pvApiCtx); + + //------Get input arguments------// + + for(int iter=0;iter=3;i--) + { + for(int j=14;j>=i;j--) + { + Size boardsize(i,j); + detected = findChessboardCorners(inputs[iter],boardsize,v); + if(detected) + { + chessboardPoints.push_back(v); + chessboardSize.push_back(Size(i+1,j+1)); + break; + } + } + if(detected) + break; + } + if(!detected) + { + chessboardPoints.push_back(v); + } + } + if(chessboardSize.size()==0) + { + Scierror(999, "No chessboards detected. Aborting."); + return 0; + } + Size compare = chessboardSize[0]; + for(int i=1;i +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + //#include "../common.h" + + // Function using OpenCV to detect FAST Features + int opencv_detectFASTFeatures(char *fname, unsigned long fname_len) + { + int *piAddr = NULL; + + //Various Declarations + SciErr sciErr; //Error Variable + int sca_read; //Error Variable + + int *imgAddr = NULL; //Address of the Image List Argument + int *piAddrChild = NULL; //Address of the Child of the Image Matrix List + int no_item_list; //Number of items in the list + + int iPrec = 0; //Precesision of the image in the lsit + int iRows=0, iCols=0; //Rows & Columns in the image + + int inp_params; //Number of Input Arguments + int *piAddr1 = NULL; //Address of Argument Head + int *piAddr2 = NULL; //Address of Value_1 of Argument + //int *piAddr3 = NULL; //Address of Value_2 of Argument + //int *piAddr4 = NULL; //Address of Value_3 of Argument + //int *piAddr5 = NULL; //Address of Value_4 of Argument + int Rows,Cols; //Rows & Columns of String Matrix + int *len = NULL; //Length of each String Element + char **arg; //String Argument + double *roi_arg = NULL; //ROI Argument Matrix Address + int rows,cols; //Rows and Columns of ROI value matrix + + //int *point = NULL; //Points returned by the FAST Algorithm + + double arg_val; //Variables for Contrast/Quality Argument + double x = 0, y = 0, width, height; //Variables for ROI Argument + + double thresh; //Effective Threshold + double thresh_c = 0; //Threshold due to arguments + double thresh_q = 0; //Threshold due to arguments + + int count_q = 0; //Count of Argument Quality Call + int count_c = 0; //Count of Argument Contrast Call + int count_r = 0; //Count of Argument ROI call + + vector keypoints; //Keypoint Vector to store the points returned by FAST Algorithm + + int i,j; //Iterators + + double count = 0; //Count of number of Features Detected + double *metric = NULL; + + Mat image; //Image Container + Mat cropped; //Image Container + + //Checks on Input and Output Arguments + CheckInputArgument(pvApiCtx, 1, 7); + CheckOutputArgument(pvApiCtx, 1, 3); + + + //Image Retrieval + //Get Address of the Image List + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &imgAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Get Number of items in the list + sciErr = getListItemNumber(pvApiCtx, imgAddr, &no_item_list); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check if it is a Grayscal image by checking whether 1 item is present in the list + if( no_item_list == 1) + { + //Address of the Grayscale Matrix + sciErr = getListItemAddress(pvApiCtx, imgAddr, 1, &piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check whether it is an integer Matrix or not + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //If integer matrix getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Based on precision Read the matrix + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + unsigned char *pstDatagray = NULL; + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, imgAddr, 1, &iRows, &iCols, &pstDatagray); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + image = Mat(iRows,iCols,CV_8UC1); + for(i=0;i(i,j)=pstDatagray[i+iRows*j]; + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + short unsigned int *pstDatagray = NULL; + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, imgAddr, 1, &iRows, &iCols, &pstDatagray); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + image = Mat(iRows,iCols,CV_16UC1); + for(i=0;i(i,j)=pstDatagray[i+iRows*j]; + break; + } + case SCI_INT32: //for unsigned integer 32 + { + int *pstDatagray = NULL; + sciErr = getMatrixOfInteger32InList(pvApiCtx, imgAddr, 1, &iRows, &iCols, &pstDatagray); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + image = Mat(iRows,iCols,CV_32SC1); + for(i=0;i(i,j)=pstDatagray[i+iRows*j]; + break; + } + } + } + else + { + //If not Integer then must be double + double *pstDatagray = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &imgAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, imgAddr, 1, &iRows, &iCols, &pstDatagray); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + image = Mat(iRows,iCols,CV_64FC1); + for(i=0;i(i,j)=pstDatagray[i+iRows*j]; + } + } + //If not single element in list than it is not a grayscale image and hence error + else + { + Scierror(999,"Expecting a single Matrix in grayscale format\n"); + return 0; + } + + //Get the Number of Arguments + inp_params = *getNbInputArgument(pvApiCtx); + + //Based on Number of Arguments Evaluate each Argument + for(i=2; i=1) + { + Scierror(999,"Not a valid range.Should be between 0 and 1.\n"); + return 0; + } + //Threshold Calculation + else thresh_c = 256*arg_val; + i++; //Incrementing iterator to count for the value argument read + count_c += 1; //Indicating MinContrast has been called once + } + else + { + Scierror(999,"Specified MinContrast twice. Check Arguments\n"); + return 0; + } + } + //MinQuality Argument + else if(strcmp(arg[0],"MinQuality") == 0) + { + if(count_q == 0) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Checking the type of Value + if( !isDoubleType(pvApiCtx, piAddr2)) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Reading the Value of argument + sca_read = getScalarDouble(pvApiCtx, piAddr2, &arg_val); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Cheack for valid range + if(arg_val<0 && arg_val>=1) + { + Scierror(999,"Not a valid range.Should be between 0 and 1.\n"); + return 0; + } + //Threshold Calculation + else thresh_q = 240*arg_val; + i++; //Incrementing iterator to count for the value argument read + count_q += 1; //Indicating MinQuality has been called once + } + else + { + Scierror(999,"Specified MinQuality twice. Check Arguments\n"); + return 0; + } + } + //ROI Argument + else if(strcmp(arg[0],"ROI") == 0) + { + if(count_r == 0) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Checking the type of argument + if( !isDoubleType(pvApiCtx, piAddr2)) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Reading the Value of the argument + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &rows, &cols, &roi_arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Assigning the elements of the list to their proper function + x = roi_arg[0]; + y = roi_arg[1]; + width = roi_arg[2]; + height = roi_arg[3]; + + + //Code for reading the value associated with ROI individually rather than in a list form + + /*sca_read = getScalarDouble(pvApiCtx, piAddr2, &x); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, i+2, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sca_read = getScalarDouble(pvApiCtx, piAddr3, &y); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, i+3, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sca_read = getScalarDouble(pvApiCtx, piAddr4, &width); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, i+4, &piAddr5); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sca_read = getScalarDouble(pvApiCtx, piAddr5, &height); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + }*/ + + //Checking for valid crop area + if(x>=image.cols || y>=image.rows || x<0 || y<0) + { + sciprint("Invalid x or y value\n"); + return 0; + } + if(width<=0 || height<=0 || x+width > image.cols || y+height > image.rows) + { + sciprint("Invalid width or height value\n"); + return 0; + } + //Croppint the image + Rect myROI(x, y, width, height); + Mat croppedRef(image, myROI); + croppedRef.copyTo(cropped); + i++; //Incrementing iterator to count for the value argument read + count_r += 1; //Indicating ROI has been called once + } + else + { + Scierror(999,"Specified ROC twice. Check Arguments\n"); + return 0; + } + } + else + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + } + + //Calculating the effective threshold by selecting the maximum threshold + if(thresh_c > 0 && thresh_c >= thresh_q) + thresh = thresh_c; + else if(thresh_q > 0 && thresh_q > thresh_c) + thresh = thresh_q; + else thresh = 256*0.2; + + //Implementing FAST Algorithm + if(count_r != 0) + FAST(cropped, keypoints, thresh, 1); + else + FAST(image, keypoints, thresh, 1); + + count = keypoints.size(); + + metric = (double*)malloc(sizeof(double)*keypoints.size()*1); + for( i=0; i +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + /* Calling syntax: detectHarrisFeatures(I) or detectHarrisFeatures(I, Name, Value) + which uses additional options specified by one or more Name, Value pair arguments. + Arguments Allowed: MinQuality, FilterSize, ROI, SensitivityFactor + MinQuality : The minimum accepted quality of corners represents a fraction of the + maximum corner metric value in the image. Larger values can be used + to remove erroneous corners. + FilterSize : Gaussian filter dimension + ROI : Rectangular region for corner detection + SensitivityFactor : A scalar value, K, where 0 < K < 0.25, specifying the sensitivity + factor used in the Harris detection algorithm. */ + + int opencv_detectHarrisFeatures(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + int intErr; + + //------Local variables------// + int *location = NULL; + double *metric = NULL; + int *piAddr = NULL; + int *piLen = NULL; + int nbInputArguments; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + double *matrixOfRoi; // ROI[xStart, yStart, width, height] + int iRows, iCols; + Mat sourceImage, dst, dstThresholded, ucharDstThresholded, extended; + vector > contours; + double filterSize = 5, minQuality = 0.01, sensitivityFactor = 0.04, blockSize = 2, maxDst, localMax; + bool *included = NULL; + int pointCount = 0, localMaxPos; + double tempForSwapping; + int coordinateMin, coordinatePos; + + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 1, 9); + CheckOutputArgument(pvApiCtx, 1, 3); + + //------Get input arguments------// + retrieveImage(sourceImage, 1); + if(sourceImage.channels() != 1) + { + Scierror(999, "The input image is not a grayscale image."); + return 0; + } + matrixOfRoi = (double*) malloc(sizeof(double) * 4); + providedArgs = (bool*) malloc(sizeof(bool) * 4); + memset(providedArgs, 0, 4); + matrixOfRoi[0] = 0; + matrixOfRoi[1] = 0; + matrixOfRoi[2] = sourceImage.cols; + matrixOfRoi[3] = sourceImage.rows; + nbInputArguments = *getNbInputArgument(pvApiCtx); + + // The following loop is for checking if optional arguments have been provided + for(int iter=2; iter <= nbInputArguments; iter++) + { + // Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + // providedArgs[] makes sure that no optional argument is provided more than once + if(strcmp(currentArg, "MinQuality")==0) + { + if(iter+1<=nbInputArguments && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &minQuality); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(minQuality < 0 || minQuality > 1) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible range is [0, 1].\n", currentArg); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "FilterSize")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &filterSize); + if(intErr) + { + return intErr; + } + providedArgs[1] = 1; + if(filterSize!=1 && filterSize!=3 && filterSize!=5 && filterSize!=7) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are {1, 3, 5, 7}.\n", currentArg); + return 0; + } + } + else if(providedArgs[1]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "ROI")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, iter); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &matrixOfRoi); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows!=1 || iCols!=4) + { + Scierror(999, "Incorrect dimension of matrix for argument ROI.\n"); + return 0; + } + providedArgs[2] = 1; + if(matrixOfRoi[0] < 0 || matrixOfRoi[0] > sourceImage.cols || matrixOfRoi[1] < 0 || + matrixOfRoi[1] > sourceImage.rows || matrixOfRoi[2] < 0 || matrixOfRoi[0] + matrixOfRoi[2] > sourceImage.cols + || matrixOfRoi[3] < 0 || matrixOfRoi[1] + matrixOfRoi[3] > sourceImage.rows) + { + Scierror(999, "Error: Please provide proper values for \"%s\".\n", currentArg); + return 0; + } + } + else if(providedArgs[2]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "SensitivityFactor")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &sensitivityFactor); + if(intErr) + { + return intErr; + } + providedArgs[3] = 1; + if(sensitivityFactor < 0 || sensitivityFactor > 0.25) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are [0, 0.25].\n", currentArg); + return 0; + } + } + else if(providedArgs[3]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: \"%s\" name for input argument is not valid.\n", currentArg); + return 0; + } + } + + //------Actual processing------// + cornerHarris(sourceImage(Rect(matrixOfRoi[0], matrixOfRoi[1], matrixOfRoi[2], matrixOfRoi[3])), dst, blockSize, filterSize, sensitivityFactor, BORDER_DEFAULT); + + // Finding the maximum value at a pixel in dst + maxDst = 0; + for(int rowIter=0; rowIter < dst.rows; rowIter++) + { + for(int colIter=0; colIter < dst.cols; colIter++) + { + if(dst.at(rowIter, colIter) > maxDst) + { + maxDst = dst.at(rowIter, colIter); + } + } + } + + /* First, we threshold dst according to the minQuality expected by the user. + We then find contours in the thresholded image and check each pixel's value + to compute the local maxima. A extended mat is created because contours + having edges on the boundaries of the images are not considered by + findContours */ + threshold(dst, dstThresholded, maxDst*minQuality, 255, THRESH_BINARY); + dstThresholded.convertTo(ucharDstThresholded, CV_8UC1); + extended = Mat(dst.rows+2, dst.cols+2, CV_8UC1); + for(int rowIter = 0; rowIter < extended.rows; rowIter++) + { + for(int colIter = 0; colIter < extended.cols; colIter++) + { + if(rowIter == 0 || rowIter == extended.rows - 1 + || colIter == 0 || colIter == extended.cols - 1) + { + extended.at(rowIter, colIter) = 0; + } + else + { + extended.at(rowIter, colIter) = ucharDstThresholded.at(rowIter-1, colIter-1); + } + } + } + + findContours(extended, contours, CV_RETR_TREE, CV_CHAIN_APPROX_NONE); + pointCount = contours.size(); + location = (int*) malloc(sizeof(int) * pointCount * 2); + metric = (double*) malloc(sizeof(double)*pointCount); + for(int rowIter = 0; rowIter < contours.size(); rowIter++) + { + localMax = 0; + localMaxPos = 0; + for(int colIter=0; colIter(contours[rowIter][colIter].y-1, contours[rowIter][colIter].x-1)>localMax) + { + localMax = dst.at(contours[rowIter][colIter].y-1, contours[rowIter][colIter].x-1); + localMaxPos = colIter; + } + } + if(localMax > maxDst*minQuality) + { + location[rowIter] = contours[rowIter][localMaxPos].x-1 + matrixOfRoi[0]; + location[pointCount + rowIter] = contours[rowIter][localMaxPos].y-1 + matrixOfRoi[1]; + metric[rowIter] = dst.at(location[pointCount + rowIter], location[rowIter]); + } + } + + // To return coordinates in order + for(int iter1 = 0; iter1 < pointCount - 1; iter1++) + { + coordinateMin = location[iter1]; + coordinatePos = iter1; + for(int iter2 = iter1 + 1; iter2 < pointCount; iter2++) + { + if(location[iter2] < coordinateMin) + { + coordinateMin = location[iter2]; + coordinatePos = iter2; + } + else if(location[iter2] == coordinateMin) + { + if(location[pointCount + iter2] < location[pointCount + coordinatePos]) + { + coordinateMin = location[iter2]; + coordinatePos = iter2; + } + } + } + // Swapping x coordinate + tempForSwapping = location[coordinatePos]; + location[coordinatePos] = location[iter1]; + location[iter1] = tempForSwapping; + // Swapping y coordinate + tempForSwapping = location[pointCount + coordinatePos]; + location[pointCount + coordinatePos] = location[pointCount + iter1]; + location[pointCount + iter1] = tempForSwapping; + // Swapping metric + tempForSwapping = metric[coordinatePos]; + metric[coordinatePos] = metric[iter1]; + metric[iter1] = tempForSwapping; + } + + //------Create output arguments------// + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx)+1, pointCount, 2, location); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+2, pointCount, 1, metric); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx)+3, 1, 1, &pointCount); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx)+2; + AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx)+3; + ReturnArguments(pvApiCtx); + return 0; + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_detectMSERFeatures.cpp b/sci_gateway1/cpp/opencv_detectMSERFeatures.cpp new file mode 100644 index 0000000..a437c16 --- /dev/null +++ b/sci_gateway1/cpp/opencv_detectMSERFeatures.cpp @@ -0,0 +1,451 @@ +/******************************************************************************************************************************************************************************************************** +* Author: Umang Agrawal * +* Code: detectMSERFeatures.cpp * +* Function Format: [ list_pixels, ......] = detectMSERFeatures( image, Optional Arguments........) * +* Input Arguments: 1. Image (Grayscale or RGB) * +* 2. ThresholdDelta Value: (0:100] Expressed in Percentage * +* 3. RegionAreaRange Value: [ Min_Area , Max_Area ] * +* 4. MaxAreaVariation Value: Scalar Double typical range from 0.25 - 4 * +* 5. ROI Value: [ x_cord , y_cord , width , height ] * +* * +* Output Argument: 1. List Of Pixels stating the Locus of all features detected * +* 2. Count * +* 3. Location: Centroid of each Locus * +* 4. Axes: Major and Minor axis length of ellipse * +* 5. Orientation: Tilt of the elliptical fit * +* * +********************************************************************************************************************************************************************************************************/ + + +#include +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/imgproc/imgproc.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_detectMSERFeatures(char *fname, unsigned long fname_len) + { + //Various Declarations + //Error Variables + SciErr sciErr; + int sca_read; + + //Mat containers for image + Mat image; + Mat cropped; + + //Address of Various Arguments + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + + int inp_params, out_params; //Count of Parameters + double arg_val; //Memory for Scalar Argument Value + int Rows, Cols; //Rows and Columns for Matrix Argument Value + double *roi_arg = NULL; //Memory for Matrix Argument Value + int *pilen = NULL; //For String Matrix Reading: Length of String + char **arg = NULL; //For String Matrix Reading: String + int count_area = 0, count_tres = 0, count_var = 0, count_roi = 0; //Count of all the optional Arguments + double x = 0, y = 0, width = 0, height = 0; //Initials of all Cropped Varaibles + + int i,j; //Iterator + + int delta = 5, min_area = 60, max_area = 14400, max_evolution = 200, edge_blur_size = 5; //Arguments of MSER function with default values + double max_variation = 0.25, min_diversity = 0.2, area_threshold = 1.01, min_margin = 0.003; //Arguments of MSER function with default values + + double *pixellist = NULL; //Output Argument Pixellist + int count[1] ; //Output Argument Count + double *location = NULL; //Output Argument Location + double *axes = NULL; //Output Argument Axes + double *orientation = NULL; //Output Argument Orientation + + vector< vector > mser_points; //3D vector for storing the locus of each detected feature + vector< RotatedRect > regions; //RotatedRect vector for storing each region associated with a feature + + CheckInputArgument(pvApiCtx, 1, 9); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 5); //Check on Number of Output Arguments + + //Read Image + retrieveImage(image, 1); + + //Getting Number of Input Arguments + inp_params = *getNbInputArgument(pvApiCtx); + + //Based on the number of Input Arguments, alter the arguments of the MSER funtion. + for(i=2; i<=inp_params; i++) + { + if( inp_params == 2 || inp_params == 4 || inp_params == 6 || inp_params == 8) + { + Scierror(999,"Either Argument Name or its Value missing\n"); + return 0; + } + //Reading the Name of the Argument + sciErr = getVarAddressFromPosition(pvApiCtx, i, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr2)) + { + Scierror(999, "%s: Wrong type of argument #%d. A string is expected.\n", fname, 1); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &Rows, &Cols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * Rows * Cols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &Rows, &Cols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + arg = (char**)malloc(sizeof(char*) * Rows * Cols); + for(int j=0;j< Rows * Cols; j++) + { + arg[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &Rows, &Cols, pilen, arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(arg); + return 0; + } + + if(strcmp(arg[0],"ThresholdDelta") == 0) + { + if(count_tres == 0) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Checking the type of value + if( !isDoubleType(pvApiCtx, piAddr3)) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Reading the Value of the argument + sca_read = getScalarDouble(pvApiCtx, piAddr3, &arg_val); + if(sca_read) + { + Scierror(999,"Not a valid value.\n"); + return 0; + } + //Check for valid range + if(arg_val <= 0 && arg_val > 100) + { + Scierror(999,"Not a valid range.Should be between 0 and 100.\n"); + return 0; + } + delta = arg_val; + i++; + count_tres += 1; + } + else + { + Scierror(999,"ThresholdDelta has been specified twice.Check the Arguments\n"); + return 0; + } + } + else if(strcmp(arg[0],"RegionAreaRange") == 0) + { + if(count_area == 0) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Checking the type of argument + if( !(isDoubleType(pvApiCtx, piAddr3) || isIntegerType(pvApiCtx, piAddr3)) ) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Reading the Value of the argument + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &Rows, &Cols, &roi_arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( Rows*Cols != 2 ) + { + Scierror(999,"Only 2 values are expected.Neither less nor more\n"); + return 0; + } + if( roi_arg[0] >= roi_arg[1] ) + { + Scierror(999,"Max Area should be greater than Min Area\n"); + return 0; + } + //Assigning the elements of the list to their proper function + min_area = roi_arg[0]; + max_area = roi_arg[1]; + i++; + count_area += 1; + } + else + { + Scierror(999,"RegionAreaRange has been specified twice.Check the Arguments\n"); + return 0; + } + } + else if(strcmp(arg[0],"MaxAreaVariation") == 0) + { + if(count_var == 0) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Checking the type of value + if( !isDoubleType(pvApiCtx, piAddr3)) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Reading the Value of the argument + sca_read = getScalarDouble(pvApiCtx, piAddr3, &arg_val); + if(sca_read) + { + Scierror(999,"Not a valid value.\n"); + return 0; + } + max_variation = arg_val; + i++; + count_var += 1; + } + else + { + Scierror(999,"MaxAreaVariation has been specified twice.Check the Arguments\n"); + return 0; + } + } + else if(strcmp(arg[0],"ROI") == 0) + { + if(count_roi == 0) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Checking the type of argument + if( !(isDoubleType(pvApiCtx, piAddr3) || isIntegerType(pvApiCtx, piAddr3)) ) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Reading the Value of the argument + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &Rows, &Cols, &roi_arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( Rows*Cols != 4 ) + { + Scierror(999,"Expected 4 arguments in format [x,y,width,height]\n"); + return 0; + } + //Assigning the elements of the list to their proper function + x = roi_arg[0]; + y = roi_arg[1]; + width = roi_arg[2]; + height = roi_arg[3]; + if(x>=image.cols || y>=image.rows || x<0 || y<0) + { + sciprint("Invalid x or y value\n"); + return 0; + } + if(width<=0 || height<=0 || x+width > image.cols || y+height > image.rows) + { + sciprint("Invalid width or height value\n"); + return 0; + } + Rect myROI(x, y, width, height); + Mat croppedRef(image, myROI); + croppedRef.copyTo(cropped); + i++; //Incrementing iterator to count for the value argument read + count_roi += 1; //Indicating ROI has been called once + } + else + { + Scierror(999,"ROI has been specified twice.Check the Arguments\n"); + return 0; + } + } + else + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + } + + //Creating an MSER class Object with specified conditions + MSER img_ms = MSER(delta, min_area, max_area, max_variation, min_diversity, max_evolution, area_threshold, min_margin, edge_blur_size); + + //Checking if have to operate on original image or cropped one + // 1st Argument: Image on which MSER features has to be detected + // 2nd Argument: Locus of all the points detected + // 3rd Argument: Mask of empty Mat container + if(count_roi == 0) + img_ms( image, mser_points, Mat()); + else + img_ms( cropped, mser_points, Mat()); + + // Extracting Regions from the mser_points locus and approximating them to an elliptic fit + for(i=0; i= regions[i].size.height ) + { + axes[i] = regions[i].size.width; //Axis: Major Axis of Ellipse + axes[mser_points.size() + i] = regions[i].size.height; //Axis: Minor Axis of Ellipse + } + else + { + axes[i] = regions[i].size.height; //Axis: Major Axis of Ellipse + axes[mser_points.size() + i] = regions[i].size.width; //Axis: Minor Axis of Ellipse + } + orientation[i] = regions[i].angle; //Orientation + } + + //Reading the number of output Arguments user demands + + //Assigning Pixel_List + out_params = *getNbOutputArgument(pvApiCtx); + if( out_params >= 1 ) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, mser_points.size(), &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(i = 0; i= 2 ) + { + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx) + 2, 1, 1, count); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + } + + //Assigning Location + if( out_params >= 3 ) + { + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 3, mser_points.size(), 2, location); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx) + 3; + } + + //Assigning Axes + if( out_params >= 4 ) + { + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 4, mser_points.size(), 2, axes); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 4) = nbInputArgument(pvApiCtx) + 4; + } + + //Assigning Orientation + if( out_params == 5 ) + { + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 5, mser_points.size(), 1, orientation); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 5) = nbInputArgument(pvApiCtx) + 5; + } + + //Releasing all the memory Captured + free(location); + free(axes); + free(orientation); + + //Returning the Output Arguments + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway1/cpp/opencv_detectMinEigenFeatures.cpp b/sci_gateway1/cpp/opencv_detectMinEigenFeatures.cpp new file mode 100644 index 0000000..5138185 --- /dev/null +++ b/sci_gateway1/cpp/opencv_detectMinEigenFeatures.cpp @@ -0,0 +1,366 @@ +/*************************************************** +Author : Rohit Suri +***************************************************/ +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + /* Calling syntax: detectMinEigenFeatures(I) or detectMinEigenFeatures(I, Name, Value) + which uses additional options specified by one or more Name, Value pair arguments. + Arguments Allowed: MinQuality, FilterSize, ROI + MinQuality : The minimum accepted quality of corners represents a fraction of the + maximum corner metric value in the image. Larger values can be used + to remove erroneous corners. + FilterSize : Gaussian filter dimension + ROI : Rectangular region for corner detection */ + + int opencv_detectMinEigenFeatures(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + int intErr; + + //------Local variables------// + int *location = NULL; + double *metric = NULL; + int *piAddr = NULL; + int *piLen = NULL; + int nbInputArguments; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + double *matrixOfRoi; // ROI[xStart, yStart, width, height] + int iRows, iCols; + Mat sourceImage, dst, dstThresholded, ucharDstThresholded, extended; + vector > contours; + double filterSize = 5, minQuality = 0.01, blockSize = 2, maxDst, localMax; + bool *included = NULL; + int pointCount = 0, localMaxPos; + double tempForSwapping; + int coordinateMin, coordinatePos; + + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 1, 7); + CheckOutputArgument(pvApiCtx, 1, 3); + + //------Get input arguments------// + retrieveImage(sourceImage, 1); + if(sourceImage.channels() != 1) + { + Scierror(999, "The input image is not a grayscale image."); + return 0; + } + matrixOfRoi = (double*) malloc(sizeof(double) * 4); + providedArgs = (bool*) malloc(sizeof(bool) * 3); + memset(providedArgs, 0, 3); + matrixOfRoi[0] = 0; + matrixOfRoi[1] = 0; + matrixOfRoi[2] = sourceImage.cols; + matrixOfRoi[3] = sourceImage.rows; + nbInputArguments = *getNbInputArgument(pvApiCtx); + + // The following loop is for checking if optional arguments have been provided + for(int iter=2; iter <= nbInputArguments; iter++) + { + // Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + // providedArgs[] makes sure that no optional argument is provided more than once + if(strcmp(currentArg, "MinQuality")==0) + { + if(iter+1<=nbInputArguments && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &minQuality); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(minQuality < 0 || minQuality > 1) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible range is [0, 1].\n", currentArg); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "FilterSize")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &filterSize); + if(intErr) + { + return intErr; + } + providedArgs[1] = 1; + if(filterSize!=1 && filterSize!=3 && filterSize!=5 && filterSize!=7) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are {1, 3, 5, 7}.\n", currentArg); + return 0; + } + } + else if(providedArgs[1]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "ROI")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, iter); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &matrixOfRoi); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows!=1 || iCols!=4) + { + Scierror(999, "Incorrect dimension of matrix for argument ROI.\n"); + return 0; + } + providedArgs[2] = 1; + if(matrixOfRoi[0] < 0 || matrixOfRoi[0] > sourceImage.cols || matrixOfRoi[1] < 0 || + matrixOfRoi[1] > sourceImage.cols || matrixOfRoi[2] < 0 || matrixOfRoi[0] + matrixOfRoi[2] > sourceImage.cols + || matrixOfRoi[3] < 0 || matrixOfRoi[1] + matrixOfRoi[3] > sourceImage.rows) + { + Scierror(999, "Error: Please provide proper values for \"%s\".\n", currentArg); + return 0; + } + } + else if(providedArgs[2]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: \"%s\" name for input argument is not valid.\n", currentArg); + return 0; + } + } + + //------Actual processing------// + cornerMinEigenVal(sourceImage(Rect(matrixOfRoi[0], matrixOfRoi[1], matrixOfRoi[2], matrixOfRoi[3])), dst, blockSize, filterSize, BORDER_DEFAULT); + + // Finding the maximum value at a pixel in dst + maxDst = 0; + for(int rowIter=0; rowIter < dst.rows; rowIter++) + { + for(int colIter=0; colIter < dst.cols; colIter++) + { + if(dst.at(rowIter, colIter) > maxDst) + { + maxDst = dst.at(rowIter, colIter); + } + } + } + + /* First, we threshold dst according to the minQuality expected by the user. + We then find contours in the thresholded image and check each pixel's value + to compute the local maxima. A extended mat is created because contours + having edges on the boundaries of the images are not considered by + findContours */ + threshold(dst, dstThresholded, maxDst*minQuality, 255, THRESH_BINARY); + dstThresholded.convertTo(ucharDstThresholded, CV_8UC1); + extended = Mat(dst.rows+2, dst.cols+2, CV_8UC1); + for(int rowIter = 0; rowIter < extended.rows; rowIter++) + { + for(int colIter = 0; colIter < extended.cols; colIter++) + { + if(rowIter == 0 || rowIter == extended.rows - 1 + || colIter == 0 || colIter == extended.cols - 1) + { + extended.at(rowIter, colIter) = 0; + } + else + { + extended.at(rowIter, colIter) = ucharDstThresholded.at(rowIter-1, colIter-1); + } + } + } + + findContours(extended, contours, CV_RETR_TREE, CV_CHAIN_APPROX_NONE); + pointCount = contours.size(); + location = (int*) malloc(sizeof(int) * pointCount * 2); + metric = (double*) malloc(sizeof(double)*pointCount); + for(int rowIter = 0; rowIter < contours.size(); rowIter++) + { + localMax = 0; + localMaxPos = 0; + for(int colIter=0; colIter(contours[rowIter][colIter].y-1, contours[rowIter][colIter].x-1)>localMax) + { + localMax = dst.at(contours[rowIter][colIter].y-1, contours[rowIter][colIter].x-1); + localMaxPos = colIter; + } + } + if(localMax > maxDst*minQuality) + { + location[rowIter] = contours[rowIter][localMaxPos].x-1 + matrixOfRoi[0]; + location[pointCount + rowIter] = contours[rowIter][localMaxPos].y-1 + matrixOfRoi[1]; + metric[rowIter] = dst.at(location[pointCount + rowIter], location[rowIter]); + } + } + + // To return coordinates in order + for(int iter1 = 0; iter1 < pointCount - 1; iter1++) + { + coordinateMin = location[iter1]; + coordinatePos = iter1; + for(int iter2 = iter1 + 1; iter2 < pointCount; iter2++) + { + if(location[iter2] < coordinateMin) + { + coordinateMin = location[iter2]; + coordinatePos = iter2; + } + else if(location[iter2] == coordinateMin) + { + if(location[pointCount + iter2] < location[pointCount + coordinatePos]) + { + coordinateMin = location[iter2]; + coordinatePos = iter2; + } + } + } + // Swapping x coordinate + tempForSwapping = location[coordinatePos]; + location[coordinatePos] = location[iter1]; + location[iter1] = tempForSwapping; + // Swapping y coordinate + tempForSwapping = location[pointCount + coordinatePos]; + location[pointCount + coordinatePos] = location[pointCount + iter1]; + location[pointCount + iter1] = tempForSwapping; + // Swapping metric + tempForSwapping = metric[coordinatePos]; + metric[coordinatePos] = metric[iter1]; + metric[iter1] = tempForSwapping; + } + //------Create output arguments------// + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx)+1, pointCount, 2, location); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+2, pointCount, 1, metric); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx)+3, 1, 1, &pointCount); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx)+2; + AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx)+3; + ReturnArguments(pvApiCtx); + return 0; + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_detectSURFFeatures.cpp b/sci_gateway1/cpp/opencv_detectSURFFeatures.cpp new file mode 100644 index 0000000..8a62361 --- /dev/null +++ b/sci_gateway1/cpp/opencv_detectSURFFeatures.cpp @@ -0,0 +1,432 @@ +/*************************************************** +Author : Shashank Shekhar +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + //#include "../common.cpp" + +int opencv_detectSURFFeatures(char *fname, unsigned long fname_len) +{ + + SciErr sciErr; + + + int *piAddr = NULL; + int *piAddrVal = NULL; + int val_position; + int *piLen = NULL; + int iRows, iCols = 0; + char ** pstData = NULL; + char *currentArg = NULL; + int valOfCA; + int argPresence[4]; + for(int i=0;i<4;i++) + argPresence[i]=0; + + vector myPoints; + + int *LocationList = NULL; + int *OrientationList = NULL; + int *MetricList = NULL; + int *LaplacianList = NULL; + int *ScaleList = NULL; + + double *LocationData = NULL; + double *OrientationData = NULL; + double *MetricData = NULL; + int *LaplacianData = NULL; + double *ScaleData = NULL; + + int noOfarguments = *getNbInputArgument(pvApiCtx); + + CheckInputArgument(pvApiCtx, 1,9); + CheckOutputArgument(pvApiCtx,1,6); + + Mat image; + retrieveImage(image,1); + + + if(image.channels()!=1) + { + Scierror(999," Feature is not meant for RGB images\n"); + return 0; + } + +//********************************************* Valid Argument Count Check ***************************************** + + if(noOfarguments%2==0) + { + Scierror(999," Invalid No of Arguments \n"); + return 0; + } + +//********************************************* Optional Input Arguments ***************************************** + + double MetricThreshold = 0; + double NumOctaves = 0; + double NumScaleLevels = 0; + double *ROImat = NULL; + +//********************************************* Retrieval of Name, Value Argument Pair **************************** + + + for(int i=2;i<=noOfarguments;i=i+2) + { + sciErr = getVarAddressFromPosition(pvApiCtx,i,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isStringType(pvApiCtx, piAddr)) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + //first call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + +//second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j = 0 ; j < iRows * iCols ; j++) + { + pstData[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1));//+ 1 for null termination + } +//third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + + if(strcmp(currentArg,"MetricThreshold")==0) + { + + val_position=i+1; + + if(argPresence[0]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for MetricThreshold. Please enter a non negative Double value\\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &MetricThreshold); + + if(MetricThreshold<0) + { + Scierror(999," Invalid Value for MetricThreshold. Please enter a non negative Double value\\n"); + return 0; + } + argPresence[0]=1; + } + else if(strcmp(currentArg,"NumOctaves")==0) + { + val_position=i+1; + + if(argPresence[1]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + + Scierror(999," Invalid Value for NumOctaves. Recommended Values are between 1 and 4\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &NumOctaves); + + if(NumOctaves<1) + { + Scierror(999," Invalid Value for NumOctaves. Recommended Values are between 1 and 4\n"); + return 0; + } + + argPresence[1]=1; + } + else if(strcmp(currentArg,"NumScaleLevels")==0) + { + val_position=i+1; + + if(argPresence[2]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for NumScaleLevels. Recommended values are between 3 and 6\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &NumScaleLevels); + + if(NumScaleLevels<3) + { + Scierror(999," Invalid Value for NumScaleLevels. Please enter an integer value greater than or equal to 3\n"); + return 0; + } + + argPresence[2]=1; + } + else if(strcmp(currentArg,"ROI")==0) + { + val_position=i+1; + + if(argPresence[3]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx, piAddrVal)) + { + Scierror(999,"Enter a List of 4 double arguments\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddrVal, &iRows, &iCols, &ROImat); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=4) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + + if(ROImat[0]<0 || ROImat[1]<0 || ROImat[2]<0 || ROImat[3]<0) + { + sciprint("Arguments cannot be negative\n"); + return 0; + } + + if(ROImat[0]>image.cols || ROImat[1]>image.rows || ROImat[0]+ROImat[2]>image.cols + || ROImat[1]+ROImat[3]>image.rows) + { + Scierror(999,"Please make sure the arguments are within the image range\n"); + return 0; + } + + argPresence[3]=1; + } + else + { + Scierror(999, "Error: \"%s\" name for input argument is not valid.\n", currentArg); + return 0; + } + + } +//********************************************* End of OIA ******************************************************** + +int nOctaveLayers; + + if(argPresence[2]==1) + nOctaveLayers = NumScaleLevels-2; + + + if(argPresence[0]==0) + MetricThreshold=1000; + if(argPresence[1]==0) + NumOctaves=3; + if(argPresence[2]==0) + nOctaveLayers = 2; + if(argPresence[3]==0) + { + ROImat=(double *)malloc(sizeof(double)*4); + ROImat[0]=0; + ROImat[1]=0; + ROImat[2]=image.cols; + ROImat[3]=image.rows; + } + + + Rect masker(ROImat[0], ROImat[1], ROImat[2], ROImat[3]); + + Mat mask(image.size(), CV_8UC1, Scalar::all(0)); + mask(masker).setTo(Scalar::all(255)); + + + + SURF my_object(MetricThreshold, NumOctaves, nOctaveLayers); + + my_object.operator()(image, mask, myPoints, noArray()); + + int size= myPoints.size(); + +//*************************************************************** Location *************************************************************** + + ScaleData = (double *)malloc(sizeof(double)*size); + OrientationData = (double *)malloc(sizeof(double)*size); + MetricData = (double *)malloc(sizeof(double)*size); + LaplacianData = (int *)malloc(sizeof(int)*size); + + iRows=size; + iCols=2; + int k=0; + + LocationData = (double *)malloc(sizeof(double)*iRows*iCols); + + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_dilate(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + char **type = NULL; + int i,j,k; + double anchorX,anchorY,iterations; + double *kernel = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for kernel + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &kernel); + if(sciErr.iErr) + { + printError(&sciErr,0); + return 0; + } + + double actualkernel[iRows][iCols]; + for(i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_ellipse(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11 = NULL; + int *piAddr12 = NULL; + int *piAddr13 = NULL; + int *piAddr14 = NULL; + int i,j,k; + double thickness=1 , linetype=8, shift=0 ,centre_x ,centre_y,first_axis,second_axis,angle,startAngle,endAngle,r_value,g_value,b_value; + + //checking input argument + CheckInputArgument(pvApiCtx, 14, 14); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of centre + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,¢re_x); + if(intErr) + return intErr; + + + //for value of y coordinate of centre + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,¢re_y); + if(intErr) + return intErr; + + //for value of first_axis + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&first_axis); + if(intErr) + return intErr; + + //for value of second_axis + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&first_axis); + if(intErr) + return intErr; + + //for value of angle + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6,&angle); + if(intErr) + return intErr; + + //for value of startAngle + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7,&startAngle); + if(intErr) + return intErr; + + //for value of endAngle + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&endAngle); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr9 ,&r_value); + if(intErr) + return intErr; + + //for value of G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr10 ,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr11,&b_value); + if(intErr) + return intErr; + + // for thickness of circle default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,12,&piAddr12); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr12,&thickness); + if(intErr) + return intErr; + + //for line type of circle default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,13,&piAddr13); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr13,&linetype); + if(intErr) + return intErr; + + // for shift in circle default : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,14,&piAddr14); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr14,&shift); + if(intErr) + return intErr; + + // checking radius parameter + if (first_axis < 0) + { + first_axis=3; + sciprint("first_axis should be positive , 3 value was used instead\n"); + } + if (second_axis < 0) + { + second_axis=3; + sciprint("second_axis should be positive , 3 value was used instead\n"); + } + if (first_axis < second_axis) + { + double temp; + temp=first_axis; + first_axis=second_axis; + second_axis=temp; + sciprint("first_axis should be greater than second_axis. Values swapped.\n"); + } + if(angle < 0 || angle > 360) + { + angle=45; + sciprint("Imvalid angle value used. Using 45 instead"); + } + if(startAngle < 0 || startAngle > 360) + { + startAngle=45; + sciprint("Imvalid startAngle value used. Using 120 instead"); + } + if(endAngle < 0 || endAngle > 360) + { + endAngle=45; + sciprint("Imvalid endAngle value used. Using 120 instead"); + } + // checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead\n"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead\n"); + } + //temporary pt variable, to use in function as centre + Point pt(centre_x,centre_y); + Size sz(first_axis,second_axis); + /// Draw the circles detected + + ellipse( src, pt, sz, angle, startAngle, endAngle, Scalar(r_value,g_value,b_value), thickness, linetype, shift); + +//// sending the image + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway1/cpp/opencv_ellipse2poly.cpp b/sci_gateway1/cpp/opencv_ellipse2poly.cpp new file mode 100644 index 0000000..e7edef4 --- /dev/null +++ b/sci_gateway1/cpp/opencv_ellipse2poly.cpp @@ -0,0 +1,171 @@ +/******************************************************** + Author: Abhilasha Sancheti +********************************************************* + pts = ellipse2Poly(double center_x,double center_y,double width,double height,int angle, int arcStart, int arcEnd, int delta) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_ellipse2poly(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + double *pstData = NULL; + int i,j,k ; + double center_x,center_y,width,height,arcstart,arcend,delta,angle; + + + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 8); + CheckOutputArgument(pvApiCtx, 1, 1) ; + +//// to retreive the value of x coordinate of center + + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr1 ,¢er_x); + if(intErr) + return intErr; + + + +//// to retreive the value of y coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,¢er_y); + if(intErr) + return intErr; + +//// to retreive the value of width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&width); + if(intErr) + return intErr; + +//// to retreive the value of height + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&height); + if(intErr) + return intErr; + +//// to retreive the value of angle parameter + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&angle); + if(intErr) + return intErr; + +//// to retreive the value of arcstart parameter + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6,&arcstart); + if(intErr) + return intErr; + +//// to retreive the value of arcend parameter + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7,&arcend); + if(intErr) + return intErr; + +//// to retreive the value of delta parameter + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&delta); + if(intErr) + return intErr; + + + vector pts; + Point center(center_x,center_y); + Size axes(width,height); + ellipse2Poly(center, axes, angle, arcstart, arcend,delta, pts); + + int row = pts.size(); + double *cor = (double *)malloc(row*2*sizeof(double)); + for (int i=0;i To execute, epipolarlines(I1,I2) +* where I1.1 & I2 are images +* +* This will display the matched points and the epipolar lines. +*********************************************************************************/ +#include +#include +#include +#include +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + //# include "../common.cpp" + + int opencv_epipolarlines(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr=0; + + //-> Mat containers for images + Mat img_1; + Mat img_2; + + //-> Address of Various Arguments + int *piAddr = NULL; + + //-> Local variables + int *outList = NULL; + unsigned char *red = NULL; + unsigned char *green = NULL; + unsigned char *blue = NULL; + + int *outList2 = NULL; + unsigned char *red2 = NULL; + unsigned char *green2 = NULL; + unsigned char *blue2 = NULL; + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + CheckInputArgument(pvApiCtx, 1, 5); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 5); //Check on Number of Output Arguments + + //-> Read Image + retrieveImage( img_1, 1); + retrieveImage(img_2, 2); + + //-> Count number of input arguments + //num_InputArgs = *getNbInputArgument(pvApiCtx); + + //-> Based on number of input arguments + +//***************************************************** Actual Processing ************************************************************* + + //Mat img_1 = imread( "left.jpg", CV_LOAD_IMAGE_GRAYSCALE ); + //Mat img_2 = imread( "right.jpg", CV_LOAD_IMAGE_GRAYSCALE ); + + cvtColor(img_1, img_1, CV_BGR2GRAY); + cvtColor(img_2, img_2,CV_BGR2GRAY); + + //-- Step 1: Detect the keypoints using SURF Detector + int minHessian = 400; + + SurfFeatureDetector detector( minHessian ); + + std::vector keypoints_1, keypoints_2; + + detector.detect( img_1, keypoints_1 ); + detector.detect( img_2, keypoints_2 ); + + //-- Step 2: Calculate descriptors (feature vectors) + SurfDescriptorExtractor extractor; + + Mat descriptors_1, descriptors_2; + + extractor.compute( img_1, keypoints_1, descriptors_1 ); + extractor.compute( img_2, keypoints_2, descriptors_2 ); + + //-- Step 3: Matching descriptor vectors using FLANN matcher + FlannBasedMatcher matcher; + std::vector< DMatch > matches; + matcher.match( descriptors_1, descriptors_2, matches ); + + double max_dist = 0; double min_dist = 100; + + //-- Quick calculation of max and min distances between keypoints + for( int i = 0; i < descriptors_1.rows; i++ ) + { double dist = matches[i].distance; + if( dist < min_dist ) min_dist = dist; + if( dist > max_dist ) max_dist = dist; + } + + std::vector< DMatch > good_matches; + vector points1,points2; + + for( int i = 0; i < descriptors_1.rows; i++ ) + { //if( matches[i].distance <= max(2*min_dist, 0.02) ) + if( matches[i].distance <= max(3*min_dist, 0.02) ) + { + good_matches.push_back( matches[i]); + points2.push_back( keypoints_2[matches[i].trainIdx].pt ); + points1.push_back( keypoints_1[matches[i].queryIdx].pt ); + } + + } + + //***************** Finding Fundamental Matrix ************************** + + vector states; + Mat f = findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99, states); + //Mat F = findFundamentalMat(points1, points2, FM_LMEDS, 3, 0.99, states); + string window = "epipolarlines"; + //drawEpipolarLines(window, fundamental_matrix, img_1, img_2, points1, points2); + + + vector > epilines1, epilines2; + computeCorrespondEpilines(points1, 1, f, epilines1); //Index starts with 1 + computeCorrespondEpilines(points2, 2, f, epilines2); + + cvtColor(img_1,img_1,COLOR_GRAY2BGR); + cvtColor(img_2,img_2,COLOR_GRAY2BGR); + int inlierDistance = -1; + // cout< 0) + { + if(distancePointLine(points1[i], epilines2[i]) > inlierDistance || + distancePointLine(points2[i], epilines1[i]) > inlierDistance) + { + //The point match is no inlier + continue; + } + } */ + + //Mat outImg(img1.rows, img1.cols*2, CV_8UC3); + //Rect rect1(0,0, img1.cols, img1.rows); + +//************************************************************* Draw epipolar lines function ******************************************************** + cv::Scalar color(rng(256),rng(256),rng(256)); + line(img_1, Point(0,-epilines1[i][2]/epilines1[i][1]), Point(img_1.cols,-(epilines1[i][2]+epilines1[i][0]*img_1.cols)/epilines1[i][1]),color); + circle(img_1, points1[i], 3, color, -1, CV_AA); + + line(img_2, Point(0,-epilines1[i][2]/epilines1[i][1]), Point(img_2.cols,-(epilines1[i][2]+epilines1[i][0]*img_2.cols)/epilines1[i][1]),color); + circle(img_2, points2[i], 3, color, -1, CV_AA); + } + +//************************************************ Returning first image ************************************************** + + if( img_1.channels() == 1) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + red = (unsigned char *)malloc(sizeof(unsigned char)*img_1.rows*img_1.cols); + + for(int k=0;k(k, p); + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, img_1.rows, img_1.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + } + + else + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + red = (unsigned char *)malloc(sizeof(unsigned char)*img_1.rows*img_1.cols); + green = (unsigned char *)malloc(sizeof(unsigned char)*img_1.rows*img_1.cols); + blue = (unsigned char *)malloc(sizeof(unsigned char)*img_1.rows*img_1.cols); + + for(int k=0;k(k, p); + red[k+img_1.rows*p]=intensity.val[2]; + green[k+img_1.rows*p]=intensity.val[1]; + blue[k+img_1.rows*p]=intensity.val[0]; + } + } + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, img_1.rows, img_1.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 2, img_1.rows, img_1.cols, green); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 3, img_1.rows, img_1.cols, blue); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + free(green); + free(blue); + + } + + + /*imshow("title", img_1); + waitKey(0); + imshow("title2", img_2); + waitKey(0);*/ + + if( img_2.channels() == 1) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, 1, &outList2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + red2 = (unsigned char *)malloc(sizeof(unsigned char)*img_2.rows*img_2.cols); + + for(int k=0;k(k, p); + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, outList2, 1, img_2.rows, img_2.cols, red2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red2); + } + + else + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, 3, &outList2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + red2 = (unsigned char *)malloc(sizeof(unsigned char)*img_2.rows*img_2.cols); + green2 = (unsigned char *)malloc(sizeof(unsigned char)*img_2.rows*img_2.cols); + blue2 = (unsigned char *)malloc(sizeof(unsigned char)*img_2.rows*img_2.cols); + + for(int k=0;k(k, p); + red2[k+img_2.rows*p]=intensity2.val[2]; + green2[k+img_2.rows*p]=intensity2.val[1]; + blue2[k+img_2.rows*p]=intensity2.val[0]; + } + } + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, outList2, 1, img_2.rows, img_2.cols, red2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, outList2, 2, img_2.rows, img_2.cols, green2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, outList2, 3, img_2.rows, img_2.cols, blue2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red2); + free(green2); + free(blue2); + + } + + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + ReturnArguments(pvApiCtx); + return 0; + } + +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_erode.cpp b/sci_gateway1/cpp/opencv_erode.cpp new file mode 100644 index 0000000..fd12082 --- /dev/null +++ b/sci_gateway1/cpp/opencv_erode.cpp @@ -0,0 +1,117 @@ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_erode(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + char **type = NULL; + int i,j,k; + double anchorX,anchorY,iterations; + double *kernel; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for kernel + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &kernel); + if(sciErr.iErr) + { + printError(&sciErr,0); + return 0; + } + + double actualkernel[iRows][iCols]; + for(i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/calib3d/calib3d.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_estimateFundamentalMat(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iComplex = 0; + int iType = 0; + int *piAddr = NULL; + int *piLen = NULL; + int iRows,iCols; + int iA1Rows=0,iA1Cols=0,iA2Rows=0,iA2Cols=0; + int method=FM_RANSAC; + int nbInputArguments,iter,i,j; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + double* pdblReal = NULL; + double *iArray1=NULL,*iArray2=NULL; + double param1=3,param2=0.99; + + vector point1,point2; + + CheckInputArgument(pvApiCtx, 2, 8); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + nbInputArguments = *getNbInputArgument(pvApiCtx); + providedArgs = (bool*) malloc(sizeof(bool) * 3); + + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iA1Rows, &iA1Cols, &iArray1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iA2Rows, &iA2Cols, &iArray2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iA1Rows != iA2Rows) + { + Scierror(999, "There must be the same number of points in both input arrays\n"); + return 0; + } + + for(i=0;i(i,j); + } + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, fm.rows , 3, pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway1/cpp/opencv_estimateGeometricTransform.cpp b/sci_gateway1/cpp/opencv_estimateGeometricTransform.cpp new file mode 100644 index 0000000..2a4ec4b --- /dev/null +++ b/sci_gateway1/cpp/opencv_estimateGeometricTransform.cpp @@ -0,0 +1,164 @@ +/********************************************************************************* +*Author : Kevin George +* +*-> To execute, estimateGeometricTransform(I1,I2) +* where I1 & I2 are images +* +*This program only gives an affine transformation matrix +*********************************************************************************/ +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + // # include "../common.cpp" + + int opencv_estimateGeometricTransform(char *fname, unsigned long fname_len) + { + + //-> Error Management variables + SciErr sciErr; + int intErr=0; + + //-> Mat containers for images + Mat image_1; + Mat image_2; + + //-> Address of Various Arguments + int *piAddr = NULL; + + //-> Local variables + int minHessian = 400; //-> For SURF algorithm + int num_InputArgs; + //-> KeyPoint is data structure to store a point feature + // detected by a keypoint detector(OpenCV) + vector keypoints_1; + vector keypoints_2; + Mat descriptors_1, descriptors_2; + Mat img_matches; + vector< DMatch > good_matches; + vector< DMatch > matches; + vector a,b; + double max_dist = 0; double min_dist = 100; + /*double *indexPairs = NULL; + double *matchMetric = NULL; + double size=1; + int iRows, iCols; */ + + //KeyPoint *X = new KeyPoint; //-> Remember to free this Kevin + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + CheckInputArgument(pvApiCtx, 1, 5); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 5); //Check on Number of Output Arguments + + //-> Read Image + retrieveImage( image_1, 1); + retrieveImage(image_2, 2); + + //-> Count number of input arguments + //num_InputArgs = *getNbInputArgument(pvApiCtx); + + //-> Based on number of input arguments + +//***************************************************** Actual Processing ************************************************************* + + //-- Step 1: Calculate keypoints + SurfFeatureDetector detector( minHessian ); + detector.detect( image_1, keypoints_1 ); + detector.detect( image_2, keypoints_2 ); + + //-- Step 2: Calculate descriptors (feature vectors) + SurfDescriptorExtractor extractor; + extractor.compute( image_1, keypoints_1, descriptors_1 ); + extractor.compute( image_2, keypoints_2, descriptors_2 ); + + //-- Step 3: Matching descriptor vectors using FLANN matcher + FlannBasedMatcher matcher; + matcher.match( descriptors_1, descriptors_2, matches ); + + //-- Quick calculation of max and min distances between keypoints + for( int i = 0; i < descriptors_1.rows; i++ ) + { + double dist = matches[i].distance; + if( dist < min_dist ) min_dist = dist; + if( dist > max_dist ) max_dist = dist; + } + + for( int i = 0; i < descriptors_1.rows; i++ ) + { + if( matches[i].distance <= max(2*min_dist, 0.02) ) + { good_matches.push_back( matches[i]); } + } + + //queryidx- keypoints1 + //traindidx- keypoints2 + + + for(int i=0;i<3;i++) + { + a.push_back( keypoints_1[good_matches[i].queryIdx].pt ); + b.push_back( keypoints_2[good_matches[i].trainIdx].pt ); + } + + + Mat M = getAffineTransform( Mat(a), Mat(b) ); + + /* double output[M.rows][M.cols]; + + for(int i=0; i(i); + for (int j = 0; j < M.cols; j++) + { + output[i][j] = Mi[j]; + } + } */ + double *output = NULL; + output = (double *)malloc(sizeof(double)*M.rows*M.cols); + + //-> Accessing elements of Mat object M + for(int i=0; i(i); + for (int j = 0; j < M.cols; j++) + { + output[i+j] = Mi[j]; + } + } + + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1, M.rows, M.cols, output); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Returning Output + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + + return 0; + } + +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_estimateUncalibratedRectification.cpp b/sci_gateway1/cpp/opencv_estimateUncalibratedRectification.cpp new file mode 100644 index 0000000..c0078bd --- /dev/null +++ b/sci_gateway1/cpp/opencv_estimateUncalibratedRectification.cpp @@ -0,0 +1,201 @@ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/calib3d/calib3d.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_estimateUncalibratedRectification(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr=0; + int iComplex = 0; + int iType = 0; + int *piAddr = NULL; + int i,j; + int iA1Rows=0,iA1Cols=0,iA2Rows=0,iA2Cols=0; + int fmRows=0,fmCols=0,imageRows=0,imageCols=0; + double *fm=NULL,*iArray1=NULL,*iArray2=NULL,*imageSize=NULL; + double *oArray1=NULL,*oArray2=NULL; + Mat fundamentalMatrix(3,3,CV_64F); + int imagesize[2]; + vector points1,points2; + + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 2, 2) ; + + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + iComplex = isVarComplex(pvApiCtx, piAddr); + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &fmRows, &fmCols, &fm); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + iComplex = isVarComplex(pvApiCtx, piAddr); + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iA1Rows, &iA1Cols, &iArray1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + iComplex = isVarComplex(pvApiCtx, piAddr); + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iA2Rows, &iA2Cols, &iArray2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + iComplex = isVarComplex(pvApiCtx, piAddr); + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &imageRows, &imageCols, &imageSize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(fmRows!=3 || fmCols!=3) + { + Scierror(999, "Fudamental matrix must be of size 3*3\n"); + return 0; + } + if(iA1Rows != iA2Rows) + { + Scierror(999, "There must be the same number of points in both input arrays\n"); + return 0; + } + if(imageRows!=1 || (imageCols!=2 && imageCols!=3)) + { + Scierror(999, "Image size must be of vector of with 2 or 3 elements\n"); + return 0; + } + for(i = 0 ; i < 3 ; i++) + { + for(j = 0 ; j < 3 ; j++) + { + fundamentalMatrix.at(i,j) = fm[i+3*j]; + } + } + for(i=0;i(i,j); + oArray2[i + 3 * j] = H2.at(i,j); + } + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3 , 3, oArray1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2, 3 , 3, oArray2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway1/cpp/opencv_evaluate.cpp b/sci_gateway1/cpp/opencv_evaluate.cpp new file mode 100644 index 0000000..2794b90 --- /dev/null +++ b/sci_gateway1/cpp/opencv_evaluate.cpp @@ -0,0 +1,417 @@ +/*************************************************** +Author : Rohit Suri +***************************************************/ +#include +#include +#include +#include + +using namespace std; +using namespace cv; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + int opencv_evaluate(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr extractor = new SurfDescriptorExtractor(1, 4, 2, 1, int(upright)); + BOWImgDescriptorExtractor bowDE(extractor, matcher); + SurfFeatureDetector detector(1, 4, 2, 1, int(upright)); + char *classifierLocation = NULL; + Mat dictionary,inp,features; + double response; + vector keyPoints; + CvSVM svm; + int dictionarySize; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + int *count = NULL; + char **classifierDescription = NULL; + int classifierDescriptionCount; + char **description = NULL; + char ***location = NULL; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + double *confMatrix = NULL; + char *fileName = NULL; + double avgAccuracy = 0; + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + // Extracting object type and checking if type is classifier + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"classifier")==0)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for classifier argument."); + return 0; + } + classifierLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + bagOfFeaturesLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + classifierDescription = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + classifierDescription[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, classifierDescription); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + classifierDescriptionCount = iRows; + // Second Argument + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #2 is not of type imageSet.\n"); + return 0; + } + + // Extracting object type and checking if type is imageSet + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"imageSet")==0)) + { + Scierror(999, "Error: The input argument #2 is not of type imageSet.\n"); + return 0; + } + + // Extracting Description attribute of input argument + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + description = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + description[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + descriptionCount = iRows; + + // Extracting Count attribute of input argument + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &count); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location = (char***) malloc(sizeof(char**) * descriptionCount); + sciErr = getListItemAddress(pvApiCtx, piAddr, 4, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int iter = 1; iter<=descriptionCount; iter++) + { + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location[iter-1] = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int colIter = 0 ; colIter < iRows * iCols ; colIter++) + { + location[iter-1][colIter] = (char*)malloc(sizeof(char) * (piLen[colIter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, location[iter-1]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + //------Actual processing------// + if(descriptionCount != classifierDescriptionCount) + { + Scierror(999, "Error: The input imageSet does not match the classifier's training set.\n"); + return 0; + } + for(int i=0; i> dictionary; + fs.release(); + dictionarySize = dictionary.rows; + bowDE.setVocabulary(dictionary); + svm.load(classifierLocation); + for(int i=0; i +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/ml/ml.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + bool count_based(const vector& a, const vector& b);// { + // return a[1] > b[1]; + //} + + int opencv_evaluateImageRetrieval(char *fname, unsigned long fname_len) + { + SciErr sciErr; + + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piChild = NULL; + int *piGrandChild = NULL; + int *piLen = NULL; + char **pstData = NULL; + int iRows, iCols; + int no_item_list = 0; + int no_item_list_2 = 0; + int inp_params = 0; + char **arg = NULL; + + double *expectedID = NULL; + char ***location_img = NULL; + double **WordIndex = NULL; + double **Location = NULL; + double **Vocab_size = NULL; + double **Count = NULL; + double *wordFrequency = NULL; + char *bagOfFeaturesLocation = NULL; + int count_res = 0, count_roi = 0; + double numRes = 0; + double *roi_arg = NULL; + double x, y, width, height; + int no_expectation = 0; + int count; + + Mat image; + Mat feature_des; + Mat dictionary; + Mat des_matched; + Mat hist; + vector keypoints; + vector valid_key; + vector< vector > clusterID; + vector WordIndex_size; + vector avg_ID; + + int indx; + int search_ind; + double *Index_Out = NULL; + double *Score = NULL; + double **ImageWords = NULL; + double avg_prec = 0; + + CheckInputArgument(pvApiCtx, 3, 7); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 3); //Check on Number of Output Arguments + + retrieveImage(image, 1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr2)) + { + Scierror(999, "Error: Invalid Second argument. List Expected.\n"); + return 0; + } + sciErr = getListItemNumber(pvApiCtx, piAddr2, &no_item_list); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!(strcmp(pstData[0],"invertedImageIndex")==0)) + { + Scierror(999, "Error: The input argument 2 is not of type invertedImageIndex.\n"); + return 0; + } + + sciErr = getListItemAddress(pvApiCtx, piAddr2, 2, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx, piChild, &no_item_list_2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + WordIndex = (double**)malloc(sizeof(double*)*no_item_list_2); + Location = (double**)malloc(sizeof(double*)*no_item_list_2); + Vocab_size = (double**)malloc(sizeof(double*)*no_item_list_2); + Count = (double**)malloc(sizeof(double*)*no_item_list_2); + for( int i=0; i> dictionary; + fs.release(); + + SurfFeatureDetector detector(100, 4, 2, 1, 1); + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr extractor = new SurfDescriptorExtractor(100, 4, 2, 1, 1); + BOWImgDescriptorExtractor bowDE(extractor, matcher); + bowDE.setVocabulary(dictionary); + detector.detect(image, keypoints); + bowDE.compute(image, keypoints, hist, &clusterID, &des_matched); + + ImageWords = (double**)malloc(sizeof(double*)*4*1); + ImageWords[0] = (double*)malloc(sizeof(double)*keypoints.size()*1); + ImageWords[1] = (double*)malloc(sizeof(double)*keypoints.size()*2); + ImageWords[2] = (double*)malloc(sizeof(double)*1); + ImageWords[3] = (double*)malloc(sizeof(double)*1); + + ImageWords[2][0] = Vocab_size[0][0]; + ImageWords[3][0] = keypoints.size(); + + vector > count_index(no_item_list_2 , vector(3)); + + for( int i=0; ino_item_list_2 ) + { + Scierror(999,"Not a valid expected Index ID. \n"); + return 0; + } + int k = int(expectedID[i]); + if( i==0 ) + { + for( int j=0; j +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/imgproc/imgproc.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + /*string type2str(int type) + { + string r; + uchar depth = type & CV_MAT_DEPTH_MASK; + uchar chans = 1 + (type >> CV_CN_SHIFT); + switch ( depth ) + { + case CV_8U: r = "8U"; break; + case CV_8S: r = "8S"; break; + case CV_16U: r = "16U"; break; + case CV_16S: r = "16S"; break; + case CV_32S: r = "32S"; break; + case CV_32F: r = "32F"; break; + case CV_64F: r = "64F"; break; + default: r = "User"; break; + } + r += "C"; + r += (chans+'0'); + return r; + }*/ + + int opencv_extractFeatures(char *fname, unsigned long fname_len) + { + //...................................................................................................................................................................................... + //Declarations + // Error Handling + SciErr sciErr; + + // Address of Various Scilab API + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + + // Number of input and output Arguments + int inputArguments = 0; + int out_params; + + // Matrix Reading Argument + int iPrec = 0; + int noItemInList = 0; + int iRows,iCols; + int Rows, Cols; + int *pilen = NULL; + char **arg = NULL; + char **method = NULL; + char **clas = NULL; + int length_M = 0; + + // Necessary Output Arguments + double *valid_points = NULL; + double numBits = 0; + double numFeatures = 0; + double *featureVector = NULL; + + // Count of various Arguments + int count_method=0, count_block=0, count_surf=0, count_upright=0 ,count_metric=0; + int count_orient=0, count_scale=0, count_axes=0, count_sign=0, count_class=0; + + // Iterator + int i; + + //Input Arguments Parameters + double *metric = NULL; + double *orient = NULL; + double *axes = NULL; + double *scale = NULL; + double *sign = NULL; + double block_size = 0; + double upright = 0; + double surf_size = 64; + + // Keypoint Vector + vector keypoints; + vector copy_k; + + //Image Container + Mat image; + + //Output Argument + int feature_rows, feature_cols; + int valid_rows; + double count; + double *metric_o = NULL; + double *scale_o = NULL; + double *orientation_o = NULL; + double *axes_o = NULL; + double *sign_o = NULL; + + // Checks on Number of Input and Output Arguments + CheckInputArgument(pvApiCtx, 4, 22); + CheckOutputArgument(pvApiCtx, 2, 9); + + inputArguments = *getNbInputArgument(pvApiCtx); + + //....................................................................................................................................................................................... + //Checking for the Presence of Valid Number of Arguments + if( inputArguments%2 == 0) + { + + //Argument 1 - Image + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isListType(pvApiCtx, piAddr1)) + { + sciErr = getListItemNumber(pvApiCtx, piAddr1, &noItemInList); + if( noItemInList != 1) + { + Scierror(999,"Expecting a grayscale image as first Argument\n"); + return 0; + } + retrieveImage(image, 1); + } + else + { + if( !isDoubleType(pvApiCtx, piAddr1)) + { + Scierror(999,"Expecting a grayscale Image as first Argument\n"); + return 0; + } + retrieveImage(image, 1); + } + + + //.............................................................................................................................................................................. + //Argument 2 - Location Co-ordinates + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !isVarMatrixType(pvApiCtx, piAddr2)) + { + Scierror(999,"Expecting a Matrix of Mx2 order depicting the Location Coordinates as 2nd Argument.\n"); + return 0; + } + //Checking the type of location coordinates + if(isDoubleType(pvApiCtx, piAddr2)) + { + //for double location co-ordinates + double *points = NULL; + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &points); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + length_M = iRows; + KeyPoint *X = new KeyPoint; + for(int i=0; ipt = Point2f(points[i],points[iRows+i]); + X->size = 7; + keypoints.push_back(*X); + copy_k.push_back(*X); + } + } + else if(isIntegerType(pvApiCtx, piAddr2)) + { + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddr2, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 location co-ordinates + { + unsigned char *points = NULL; + sciErr = getMatrixOfUnsignedInteger8(pvApiCtx, piAddr2, &iRows, &iCols, &points); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + length_M = iRows; + KeyPoint *X = new KeyPoint; + for(int i=0; ipt = Point2f(points[i],points[iRows+i]); + X->size = 7; + keypoints.push_back(*X); + copy_k.push_back(*X); + } + break; + } + case SCI_UINT16: //for unsigned integer 16 location co-ordinates + { + unsigned short *points = NULL; + sciErr = getMatrixOfUnsignedInteger16(pvApiCtx, piAddr2, &iRows, &iCols, &points); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + length_M = iRows; + KeyPoint *X = new KeyPoint; + for(int i=0; ipt = Point2f(points[i],points[iRows+i]); + X->size = 7; + keypoints.push_back(*X); + copy_k.push_back(*X); + } + break; + } + case SCI_INT32: + { + int *points = NULL; + sciErr = getMatrixOfInteger32(pvApiCtx, piAddr2, &iRows, &iCols, &points); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + length_M = iRows; + KeyPoint *X = new KeyPoint; + for(int i=0; ipt = Point2f(points[i],points[iRows+i]); + X->size = 7; + keypoints.push_back(*X); + copy_k.push_back(*X); + } + break; + } + } + } + else + { + Scierror(999,"Expecting a Double or an Integer Matrix as Location Coordinates in 2nd Argument.\n"); + return 0; + } + + + //............................................................................................................................................................................. + //Argument 3 - Class + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr3)) + { + Scierror(999,"Expecting String Argument \"Class\" as Argument 3.\n"); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &Rows, &Cols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * Rows * Cols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &Rows, &Cols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + arg = (char**)malloc(sizeof(char*) * Rows * Cols); + for(int j=0;j< Rows * Cols; j++) + { + arg[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &Rows, &Cols, pilen, arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(arg); + return 0; + } + + //Checking Whether Argument 3 is Method or not + if(strcmp(arg[0],"Class") == 0) + { + //If method extracting the Value of the Argument + if(count_class != 0) + { + Scierror(999,"Class Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr4)) + { + Scierror(999, "%s: Wrong type of 2nd argument #%d. A string is expected.\n", fname, 1); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &Rows, &Cols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * Rows * Cols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &Rows, &Cols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + clas = (char**)malloc(sizeof(char*) * Rows * Cols); + for(int j=0;j< Rows*Cols; j++) + { + clas[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &Rows, &Cols, pilen, clas); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(clas); + return 0; + } + count_class += 1; + } + else + { + Scierror(999,"Expecting \"Class\" as the 3rd Argument\n"); + return 0; + } + } + else + { + Scierror(999,"Required Arguments are Missing\n"); + return 0; + } + + + + + //.......................................................................................................................................................................................... + //Getting Optional Input Arguments + for( i=5; i<= inputArguments; i++) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr3)) + { + Scierror(999, "%s: Wrong type of argument #%d. A string is expected.\n", fname, 1); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &Rows, &Cols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * Rows * Cols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &Rows, &Cols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + arg = (char**)malloc(sizeof(char*) * Rows * Cols); + for(int j=0;j< Rows * Cols; j++) + { + arg[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &Rows, &Cols, pilen, arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(arg); + return 0; + } + + + + //................................................................................................................................................................................. + //Reading the Value of Optional Arguments + //If Optional Argument is Method + if(strcmp(arg[0],"Method") == 0) + { + if(count_method!= 0) + { + Scierror(999,"Method Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr4)) + { + Scierror(999, "%s: Wrong type of 2nd argument #%d. A string is expected.\n", fname, 1); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &Rows, &Cols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * Rows * Cols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &Rows, &Cols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + method = (char**)malloc(sizeof(char*) * Rows * Cols); + for(int j=0;j< Rows*Cols; j++) + { + method[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &Rows, &Cols, pilen, method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(method); + return 0; + } + i++; + count_method += 1; + } + //................................................................................................................................................................................. + //If Optional Argument is Metric + else if(strcmp(arg[0],"Metric") == 0) + { + if(count_metric != 0) + { + Scierror(999,"Metric Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isVarMatrixType(pvApiCtx, piAddr4)) + { + Scierror(999,"Expecting a Matrix of Mx1 order depicting the Metric Values as Value of Metric Argument.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols, &metric); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( iCols != 1) + { + Scierror(999,"Expecting a single Column Metric Matrix\n"); + return 0; + } + if( iRows != length_M) + { + Scierror(999,"Expecting a M Row matrix having M number of elements corresponding to M rows of location coordinates\n"); + return 0; + } + i++; + count_metric += 1; + } + //................................................................................................................................................................................. + //If Optional Argument is BlockSize + else if(strcmp(arg[0],"BlockSize") == 0) + { + if(count_block != 0) + { + Scierror(999,"BlockSize has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !(isDoubleType(pvApiCtx, piAddr4)) ) + { + Scierror(999,"BlockSize Value should be an odd Integer\n"); + return 0; + } + if(getScalarDouble(pvApiCtx, piAddr4, &block_size)) + { + Scierror(999,"Cannot Read Block Size Value"); + return 0; + } + if(int(block_size)%2 == 0) + { + Scierror(999,"Expecting a odd value for Block Size"); + return 0; + } + i++; + count_block += 1; + } + //................................................................................................................................................................................. + //If Optional Argument is Upright + else if(strcmp(arg[0],"Upright") == 0) + { + if(count_upright != 0) + { + Scierror(999,"Upright Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !(isDoubleType(pvApiCtx, piAddr4)) ) + { + Scierror(999,"Upright Value must be a logic scalar\n"); + return 0; + } + if(getScalarDouble(pvApiCtx, piAddr4, &upright)) + { + Scierror(999,"Cannot Read Upright Value\n"); + return 0; + } + i++; + count_upright += 1; + } + //................................................................................................................................................................................. + //If Optional Argument is SURFSize + else if(strcmp(arg[0],"SURFSize") == 0) + { + if(count_surf != 0) + { + Scierror(999,"SURFSize Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !(isDoubleType(pvApiCtx, piAddr4)) ) + { + Scierror(999,"Surf Size Value should be an Integer\n"); + return 0; + } + if(getScalarDouble(pvApiCtx, piAddr4, &surf_size)) + { + Scierror(999,"Cannot Read Surf Size Value"); + return 0; + } + if( int(surf_size) != 64 && int(surf_size) !=128 ) + { + Scierror(999,"Expecting value 64 or 128 for Surf Size\n"); + return 0; + } + i++; + count_surf += 1; + } + //................................................................................................................................................................................. + //If Optional Argument is Orientation + else if(strcmp(arg[0],"Orientation") == 0) + { + if(count_orient != 0) + { + Scierror(999,"Orientation Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isVarMatrixType(pvApiCtx, piAddr4)) + { + Scierror(999,"Expecting a Matrix of Mx1 order depicting the Orientation Values as Value of Orientation Argument.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols, &orient); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( iCols != 1) + { + Scierror(999,"Expecting a single Column Orientation Matrix\n"); + return 0; + } + if( iRows != length_M) + { + Scierror(999,"Expecting a M Row matrix having M number of elements corresponding to M rows of location coordinates\n"); + return 0; + } + i++; + count_orient += 1; + } + //................................................................................................................................................................................. + //If Optional Argument is Axes + else if(strcmp(arg[0],"Axes") == 0) + { + if(count_axes != 0) + { + Scierror(999,"Axes Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isVarMatrixType(pvApiCtx, piAddr4)) + { + Scierror(999,"Expecting a Matrix of Mx2 order depicting the Axes as Value of Axes Argument.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols, &axes); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( iCols != 2) + { + Scierror(999,"Expecting a 2 Column Axes Matrix\n"); + return 0; + } + if( iRows != length_M) + { + Scierror(999,"Expecting a M Row matrix having M number of elements corresponding to M rows of location coordinates\n"); + return 0; + } + i++; + count_axes += 1; + } + //................................................................................................................................................................................ + //If Optional Argument is Scale + else if(strcmp(arg[0],"Scale") == 0) + { + if(count_scale != 0) + { + Scierror(999,"Scale Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isVarMatrixType(pvApiCtx, piAddr4)) + { + Scierror(999,"Expecting a Matrix of Mx1 order depicting the Scale as Value of Scale Argument.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols, &scale); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( iCols != 1) + { + Scierror(999,"Expecting a single Column Scale Matrix\n"); + return 0; + } + if( iRows != length_M) + { + Scierror(999,"Expecting a M Row matrix having M number of elements corresponding to M rows of location coordinates\n"); + return 0; + } + i++; + count_scale += 1; + } + //................................................................................................................................................................................ + //If Optional Argument is SignOfLaplacian + else if(strcmp(arg[0],"SignOfLaplacian") == 0) + { + if(count_sign != 0) + { + Scierror(999,"SignOfLaplacian Argument has been called twice\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isVarMatrixType(pvApiCtx, piAddr4)) + { + Scierror(999,"Expecting a Matrix of Mx1 order depicting the Sign Of Laplacian as Value of SignOfLaplacian Argument.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols, &sign); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( iCols != 1) + { + Scierror(999,"Expecting a single Column Axes Matrix\n"); + return 0; + } + if( iRows != length_M) + { + Scierror(999,"Expecting a M Row matrix having M number of elements corresponding to M rows of location coordinates\n"); + return 0; + } + i++; + count_sign += 1; + //Check for Validity of Laplacian Matrix + for(int i=0; i(i,j)); + } + feature_rows = descriptor.size[0]; + feature_cols = descriptor.size[1]; + valid_rows = descriptor.size[0]; + count = descriptor.size[0]; + + if( strcmp(clas[0],"cornerPoints") == 0) + { + metric_o = (double*)malloc(sizeof(double) * valid_rows); + int iterator = 0; + for(int i=0; i(i,j)); + } + feature_rows = descriptor.size[0]; + feature_cols = descriptor.size[1]; + valid_rows = descriptor.size[0]; + count = descriptor.size[0]; + + if( strcmp(clas[0],"cornerPoints") == 0) + { + metric_o = (double*)malloc(sizeof(double) * valid_rows); + int iterator = 0; + for(int i=0; i(i,j)); + } + feature_rows = descriptor.size[0]; + feature_cols = descriptor.size[1]; + valid_rows = descriptor.size[0]; + count = descriptor.size[0]; + + if( strcmp(clas[0],"cornerPoints") == 0) + { + metric_o = (double*)malloc(sizeof(double) * valid_rows); + int iterator = 0; + for(int i=0; i valid; + for( i=0; i image.cols || keypoints[i].pt.y + (block_size-1)/2 > image.rows) + continue; + valid.push_back(keypoints[i]); + } + valid_points = (double *)malloc(sizeof(double)*valid.size()*2); + featureVector = (double *)malloc(sizeof(double)*valid.size()*int(block_size)*int(block_size)); + for( int i=0; i(j,k); + iterator = iterator+1; + } + } + feature_rows = valid.size(); + feature_cols = int(block_size)*int(block_size); + valid_rows = valid.size(); + numFeatures = valid.size(); + numBits = int(block_size)*int(block_size); + count = valid.size(); + + if( strcmp(clas[0],"cornerPoints") == 0) + { + metric_o = (double*)malloc(sizeof(double) * valid_rows); + int iterator = 0; + for(int i=0; i Syntax- extractHOGFeatures("cellsize",..., "BlockSize",..., + "BlockOverlap",..., "NumBins",...) + +-> NumBins is strictly of integer type +********************************************************************************************************************/ +#include +#include +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_extractHOGFeatures(char *fname, unsigned long fname_len) + { + + //Error variables + SciErr sciErr; + int intErr = 0; + + //-> Mat containers for images + Mat img; + Mat img_gray; + + //-> Address of Various Arguments + int *piAddr = NULL; + //int *piAddr2 = NULL; + + //-> Local variables + int num_InputArgs; //-> gives total number of arguments + int iRows, iCols; + int featureVector_size; + int *piLen = NULL; + char **pstData = NULL; //-> why double pointer?? and what is it + char *currentArg = NULL; //-> Stores current string representing 'name' of name,value pair arguments + bool *providedArgs = NULL; //-> Used to check that optional argument is not entered more than once + double *featureVector; + //-> Name,Value pair variables + double *CellSize = NULL; //-> + double *BlockSize = NULL; //-> + double *BlockOverlap = NULL; //-> + /*Size CellSize ; //-> + Size BlockSize ; //-> + Size BlockOverlap ; //-> */ + + int NumBins = 9; //-> In docs, it must be int type + //vector< int> CellSize; CellSize.push_back(8); CellSize.push_back(8); + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + //-> Checks number of input and output arguments + CheckInputArgument(pvApiCtx, 1, 5); + CheckOutputArgument(pvApiCtx, 1, 5); + + //-> Read Image + retrieveImage( img, 1); + + //-> Count number of input arguments + num_InputArgs = *getNbInputArgument(pvApiCtx); + + providedArgs = (bool*) malloc(sizeof(bool) * 5); + + //-> Based on number of input arguments + +//************************************************************ Optional Input Arguments ************************************************* + vector Points; + +//************************************************ Retrieval of Name, Value Argument Pair ************************************************* + for(int iter = 2; iter<= num_InputArgs; iter++) + { + //-> Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Extracting name of next argument takes three calls to getMatrixOfString + //-> First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + //-> Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + //-> Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + +//****************************************************** Name,Value - Cellsize ***************************************************************** + if(strcmp(currentArg, "CellSize")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &CellSize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=2) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if(CellSize[0]<0 || CellSize[1]<0) + { + sciprint("Arguments cannot be negative\n"); + return 0; + } + + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } +//************************************** Name,Value - BlockSize ************************************ + else if(strcmp(currentArg, "BlockSize")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &BlockSize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=2) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if(BlockSize[0]<0 || BlockSize[1]<0) + { + sciprint("Arguments cannot be negative\n"); + return 0; + } + + providedArgs[1] = 1; + } + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + } + + +//********************************************* Name,Value - BlockOverlap ************************************************ + else if(strcmp(currentArg, "BlockOverlap")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &BlockOverlap); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=2) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if(BlockOverlap[0]<0 || BlockOverlap[1]<0) + { + sciprint("Arguments cannot be negative\n"); + return 0; + } + + providedArgs[2] = 1; + } + + else if(providedArgs[2]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + + } +//*********************************************** *********** Name,Value - NumBins ********************************************************************** + else if(strcmp(currentArg, "NumBins")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &NumBins); + if(intErr) + { + return intErr; + } + + if( NumBins<= 0) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values must be more than zero.\n", currentArg); + return 0; + } + providedArgs[3] = 1; + } + + else if(providedArgs[3]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + else + { + Scierror(999, "Incorrect Name type. Please re-enter the correct Name-Value pair arguments\n"); + return 0; + } + + }//-> Brace for closing for loop + +//******************************************************** Giving Default Arguments ******************************************************************* + + if(providedArgs[0] == 0) + { + CellSize = (double*) malloc(sizeof(double)*2); + CellSize[0] = 8; + CellSize[1] = 8; + } + + + if(providedArgs[1] == 0) + { + BlockSize = (double*) malloc(sizeof(double)*2); + BlockSize[0] = 16; + BlockSize[1] = 16; + } + + if(providedArgs[2] == 0) + { + BlockOverlap = (double*) malloc(sizeof(double)*2); + BlockOverlap[0] = 8; + BlockOverlap[1] = 8; + } + + if(providedArgs[3] == 0) + { + NumBins = 9; + } + +//**************************************************************Actual Processing******************************************************************************* + + //-> resizing + //resize(img, img, Size(64,48) ); + resize(img, img, Size(64,128) ); + + //-> Convert to grayscale(Necessary Step) + cvtColor(img, img_gray, CV_RGB2GRAY); + + //-> CellSize (8,8) is only supported size + // in opencv 2.4.9 + //HOGDescriptor d( Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9); + HOGDescriptor d(Size(64,128), Size(BlockSize[0],BlockSize[1]), Size(BlockOverlap[0],BlockOverlap[1]),Size(CellSize[0],CellSize[1]), NumBins); + //-> This is our descriptor vector + + vector descriptorsValues; + vector< Point> locations ; + d.compute( img_gray, descriptorsValues, Size(32,32), Size(0,0), locations); + + //-> Displaying feature vector size + //sciprint("%d\n",descriptorsValues.size()); + //for(int i =0; i Returning Output + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } + +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_extractLBPFeatures.cpp b/sci_gateway1/cpp/opencv_extractLBPFeatures.cpp new file mode 100644 index 0000000..291f6bb --- /dev/null +++ b/sci_gateway1/cpp/opencv_extractLBPFeatures.cpp @@ -0,0 +1,515 @@ +/*************************************************** +Author : Tanmay Chaudhari + +TO DO: +1) nearest neighbor interpolation +2) upright parameter implementation + ***************************************************/ + +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" +#include "../common.h" +//#include "../common.cpp" + + void ELBP(const Mat& src, Mat& dst, int radius, int neighbors) + { + neighbors = max(min(neighbors,31),1); // set bounds... + // Note: alternatively you can switch to the new OpenCV Mat_ + // type system to define an unsigned int matrix... I am probably + // mistaken here, but I didn't see an unsigned int representation + // in OpenCV's classic typesystem... + dst = Mat::zeros(src.rows-2*radius, src.cols-2*radius, CV_32SC1); + for(int n=0; n(radius) * cos(2.0*M_PI*n/static_cast(neighbors)); + float y = static_cast(radius) * -sin(2.0*M_PI*n/static_cast(neighbors)); + // relative indices + int fx = static_cast(floor(x)); + int fy = static_cast(floor(y)); + int cx = static_cast(ceil(x)); + int cy = static_cast(ceil(y)); + // fractional part + float ty = y - fy; + float tx = x - fx; + // set interpolation weights + float w1 = (1 - tx) * (1 - ty); + float w2 = tx * (1 - ty); + float w3 = (1 - tx) * ty; + float w4 = tx * ty; + // iterate through your data + for(int i=radius; i < src.rows-radius;i++) + { + for(int j=radius;j < src.cols-radius;j++) + { + float t = w1*src.at(i+fy,j+fx) + w2*src.at(i+fy,j+cx) + w3*src.at(i+cy,j+fx) + w4*src.at(i+cy,j+cx); + // we are dealing with floating point precision, so add some little tolerance + dst.at(i-radius,j-radius) += ((t > src.at(i,j)) && (abs(t-src.at(i,j)) > std::numeric_limits::epsilon())) << n; + } + } + } + } + + /*void ELBP(const Mat& src, Mat& dst, int radius, int neighbors) + { + switch(src.type()) { + case CV_8SC1: ELBP_(src, dst, radius, neighbors); break; + case CV_8UC1: ELBP_(src, dst, radius, neighbors); break; + case CV_16SC1: ELBP_(src, dst, radius, neighbors); break; + case CV_16UC1: ELBP_(src, dst, radius, neighbors); break; + case CV_32SC1: ELBP_(src, dst, radius, neighbors); break; + case CV_32FC1: ELBP_(src, dst, radius, neighbors); break; + case CV_64FC1: ELBP_(src, dst, radius, neighbors); break; + } + }*/ + + static Mat histc_(const Mat& src, int minVal=0, int maxVal=255, bool normed=false) + { + Mat result; + // Establish the number of bins. + int histSize = maxVal-minVal+1; + // Set the ranges. + float range[] = { static_cast(minVal), static_cast(maxVal+1) }; + const float* histRange = { range }; + // calc histogram + calcHist(&src, 1, 0, Mat(), result, 1, &histSize, &histRange, true, false); + // normalize + if(normed) { + result /= (int)src.total(); + } + return result.reshape(1,1); + } + + static Mat histc(InputArray _src, int minVal, int maxVal, bool normed) + { + Mat src = _src.getMat(); + switch (src.type()) { + case CV_8SC1: + return histc_(Mat_(src), minVal, maxVal, normed); + break; + case CV_8UC1: + return histc_(src, minVal, maxVal, normed); + break; + case CV_16SC1: + return histc_(Mat_(src), minVal, maxVal, normed); + break; + case CV_16UC1: + return histc_(src, minVal, maxVal, normed); + break; + case CV_32SC1: + return histc_(Mat_(src), minVal, maxVal, normed); + break; + case CV_32FC1: + return histc_(src, minVal, maxVal, normed); + break; + } + return Mat(); + } + + + static Mat spatial_histogram(InputArray _src, int numPatterns, int grid_x, int grid_y, bool normed) + { + Mat src = _src.getMat(); + // calculate LBP patch size + //int width = src.cols/grid_x; + //int height = src.rows/grid_y; + int width = grid_x; + int height = grid_y; + + // allocate memory for the spatial histogram + Mat result = Mat::zeros(grid_x * grid_y, numPatterns, CV_32FC1); + // return matrix with zeros if no data was given + if(src.empty()) + return result.reshape(1,1); + // initial result_row + int resultRowIdx = 0; + // iterate through grid + for(int i = 0; i < grid_y; i++) { + for(int j = 0; j < grid_x; j++) { + Mat src_cell = Mat(src, Range(i*height,(i+1)*height), Range(j*width,(j+1)*width)); + Mat cell_hist = histc(src_cell, 0, (numPatterns-1), normed); + // copy to the result matrix + Mat result_row = result.row(resultRowIdx); + cell_hist.reshape(1,1).convertTo(result_row, CV_32FC1); + // increase row count in result matrix + resultRowIdx++; + } + } + // return result as reshaped feature vector + return result.reshape(1,1); + } + + int opencv_extractLBPFeatures(char *fname, unsigned long fname_len) + { + //Error management variable + SciErr sciErr; + + //Variable declaration + int iRows = 0; + int iCols = 0; + int intErr = 0; + int nbInputArguments = 0; + int *piLen = NULL; + int *piAddr = NULL; + bool *providedArgs = NULL; + bool linearInterpolation = 1; + bool normalization = 1; + char *currentArg = NULL; + char **pstData = NULL; + double radius = 1; + double upright = 1; + double neighbors = 8; + double *cellSize = NULL; + double *outputHist = NULL; + Mat image; + Mat grayscaleImage; + Mat lbpImage; + Mat spatialHistogram; + + //Check input output arguments + CheckInputArgument(pvApiCtx, 1, 13); + CheckOutputArgument(pvApiCtx, 1, 1); + + nbInputArguments = *getNbInputArgument(pvApiCtx); + + if((nbInputArguments-1)%2!=0) + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + providedArgs = (bool*)malloc(sizeof(bool) * 6); + for(int i=0;i<6;i++) + providedArgs[i] = 0; + + //getting input arguments + retrieveImage(image, 1); + cellSize = (double*)malloc(sizeof(double) * 2); + + for(int iter=2;iter<=nbInputArguments;iter++) + { + // Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + // providedArgs[] makes sure that no optional argument is provided more than once + + if(strcmp(currentArg, "NumNeighbors")==0) + { + if(!providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr, &neighbors); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(neighbors < 4 || neighbors > 24) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible range is [4, 24].\n", currentArg); + return 0; + } + providedArgs[0] = 1; + + } + else if(providedArgs[0]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "Radius")==0) + { + if(!providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr, &radius); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(radius < 1 || radius > 5) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible range is [1, 5].\n", currentArg); + return 0; + } + providedArgs[1] = 1; + } + else if(providedArgs[1]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "Upright")==0) + { + if(!providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr, &upright); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(upright!=0.0 && upright!=1.0) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are 0 or 1.\n", currentArg); + return 0; + } + providedArgs[2] = 1; + } + else if(providedArgs[2]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "Interpolation")==0) + { + if(!providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp(pstData[0],"Mean square error")==0) + linearInterpolation=1; + else if(strcmp(pstData[0], "Nearest")==0) + linearInterpolation=0; + else + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are Linear or Nearest.\n", currentArg); + return 0; + } + providedArgs[3]=1; + } + else if(providedArgs[3]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "CellSize")==0) + { + if(!providedArgs[4]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, iter); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &cellSize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows!=1 || iCols!=2) + { + Scierror(999, "Incorrect dimension of matrix for argument CellSize.\n"); + return 0; + } + providedArgs[4]=1; + } + else if(providedArgs[4]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + else if(strcmp(currentArg, "Normalization")==0) + { + if(!providedArgs[5]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp(pstData[0],"L2")==0) + normalization=1; + else if(strcmp(pstData[0], "None")==0) + normalization=0; + else + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are L2 or None.\n", currentArg); + return 0; + } + providedArgs[5]=1; + } + else if(providedArgs[5]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + } + } + //onvert RGB image to gray + cvtColor(image, grayscaleImage, CV_BGR2GRAY); + ELBP(grayscaleImage, lbpImage, radius, neighbors); + + if(!providedArgs[4]) + { + cellSize[0] = lbpImage.cols; + cellSize[1] = lbpImage.rows; + } + + //calculate the spatial histogram + spatialHistogram = spatial_histogram(lbpImage, static_cast(pow(2.0, static_cast(neighbors))), int(cellSize[0]), int(cellSize[1]), normalization); + + outputHist = (double*)malloc(sizeof(double) * spatialHistogram.cols); + for(int i=0;i(0,i); + + //create output argument + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1, 1, spatialHistogram.cols, outputHist); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //return output Arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_fillconvexpoly.cpp b/sci_gateway1/cpp/opencv_fillconvexpoly.cpp new file mode 100644 index 0000000..1ce29c6 --- /dev/null +++ b/sci_gateway1/cpp/opencv_fillconvexpoly.cpp @@ -0,0 +1,173 @@ +/************************************************* +Authors : Abhilasha Sancheti & Sukul Bagai +************************************************** + sample inputs : b=fillconvexpoly(a,points,4,0,0,0,8,0); (points: [0 0;0 100;100 0; 100 100]) + fillConvexpoly( image , points , r_value , g_value , b_value , linrtype,shift); +*************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_fillconvexpoly(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int i,j,k ; + double *pstData = NULL; + double npts ,r_value,g_value ,b_value ,linetype=8 ,shift=0; + + + + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 8); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + +//retreive the value of clockwise parameter + Mat image; + retrieveImage(image,1); + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } +///// store values in a contour + + Point *pts = (Point *)malloc(iRows * 2 * sizeof(double)); + for ( i = 0 ; i < iRows ; i++) + { + pts[i].x = *(pstData + i); + pts[i].y = *(pstData + 1*iRows + i); + } + + /// to retrieve the number of points + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &npts); + if(intErr) + { + return intErr; + } + +//// to retreive the value of r_value + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &r_value); + if(intErr) + { + return intErr; + } + /// to retreive the value of g_value + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &g_value); + if(intErr) + { + return intErr; + } + + /// to retreive the value of b_value + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &b_value); + if(intErr) + { + return intErr; + } + /// to retreive the value of linetype + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7, &linetype); + if(intErr) + { + return intErr; + } +/// to retreive the value of shift + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8, &shift); + if(intErr) + { + return intErr; + } + + + + // call the fillconvexpoly function of opencv + fillConvexPoly(image,pts,npts,Scalar(r_value,g_value,b_value),linetype,shift); + + //temp variable was not needed, hence has been discarded + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,image,1); //here, remove the temp as a parameter as it is not needed, and instead add 1 as the third parameter. 1 denotes that the first output argument will be this variable + free(checker); //free memory taken up by checker + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} diff --git a/sci_gateway1/cpp/opencv_filter2D.cpp b/sci_gateway1/cpp/opencv_filter2D.cpp new file mode 100644 index 0000000..650771b --- /dev/null +++ b/sci_gateway1/cpp/opencv_filter2D.cpp @@ -0,0 +1,184 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = filter2D(input_image,"",kernel_matrix,anchor_x,anchor_y,delta); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_filter2D(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + char **ddepth = NULL; + int i,j,k; + double *kernel,anchorX,anchorY,delta; + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for kernel matrix + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&kernel); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n=iRows; + double kernelArray[n][n]; + //assigning values to actual kernelMatrix + for(i=0;i= n || anchorY >=n) + { + sciprint("Invalid anchor point given. Centre point (-1,-1) was used instead"); + anchorY = -1; + anchorX = -1; + } + Mat new_image(image.rows,image.cols,image.type()); + Point pt(anchorX,anchorY); + + int actualddepth; + + if(strcmp(ddepth[0],"CV_8U")==0) + actualddepth=CV_8U; + else if(strcmp(ddepth[0],"CV_16U")==0) + actualddepth=CV_16U; + else if(strcmp(ddepth[0],"CV_16S")==0) + actualddepth=CV_16S; + else if(strcmp(ddepth[0],"CV_32F")==0) + actualddepth=CV_32F; + else if(strcmp(ddepth[0],"CV_64F")==0) + actualddepth=CV_64F; + else + { + sciprint("Invalid type %s used. CV_8U was used instead by default",ddepth[0]); + actualddepth = CV_8U; + } + + //applying function + filter2D(image,new_image,actualddepth,kernelMatrix,pt,delta); + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_findContours.cpp b/sci_gateway1/cpp/opencv_findContours.cpp new file mode 100644 index 0000000..05da2b3 --- /dev/null +++ b/sci_gateway1/cpp/opencv_findContours.cpp @@ -0,0 +1,199 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + contours = findcontours(input_image(after canny is applied on it), mode, method, point_x, point_y) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_findContours(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen2=NULL; + int *piLen3 = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k, findmode , findmethod; + char **find_method=NULL , **mode = NULL; + double x,y; + + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat src; + retrieveImage(src,1); + + // for mode parameter of findContours + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + piLen2 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen2, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + mode= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + mode[i] = (char*)malloc(sizeof(char) * (piLen2[i] + 1));//+ 1 for null termination + } + //third call to retrieve data of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen2, mode); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for method parameter of findContours + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + piLen3 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen3, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + find_method= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + find_method[i] = (char*)malloc(sizeof(char) * (piLen3[i] + 1));//+ 1 for null termination + } + //third call to retrieve data of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen3, find_method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //toget the x coordinate of point parameter of findcontours + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&x); + if(intErr) + return intErr; + + //toget the y coordinate of point parameter of findcontours + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&y); + if(intErr) + return intErr; + + //to set hte mode for findcontour + if (strcmp(mode[0],"CV_RETR_EXTERNAL")==0) + findmode = CV_RETR_EXTERNAL; + else if (strcmp(mode[0],"CV_RETR_LIST")==0) + findmode = CV_RETR_LIST; + else if (strcmp(mode[0],"CV_RETR_CCOMP")==0) + findmode = CV_RETR_CCOMP; + else if (strcmp(mode[0],"CV_RETR_TREE")==0) + findmode = CV_RETR_TREE; + else + { + findmode = CV_RETR_TREE; + sciprint("wrong mode given , using CV_RETR_TREE instead"); + } + // to set the method for findContours + if (strcmp(find_method[0],"CV_CHAIN_APPROX_NONE")==0) + findmethod = CV_CHAIN_APPROX_NONE; + else if (strcmp( find_method[0],"CV_CHAIN_APPROX_SIMPLE")==0) + findmethod = CV_CHAIN_APPROX_SIMPLE; + else if (strcmp(find_method[0],"CV_CHAIN_APPROX_TC89_L1")==0) + findmethod = CV_CHAIN_APPROX_TC89_L1; + else if (strcmp(find_method[0],"CV_CHAIN_APPROX_TC89_KCOS")==0) + findmethod = CV_CHAIN_APPROX_TC89_KCOS; + else + { + findmethod = CV_CHAIN_APPROX_SIMPLE; + sciprint("wrong method given , using CV_CHAIN_APPROX_SIMPLE instead"); + } + Point pt(x,y); + /// Find contours + vector > contours; + vector hierarchy; + findContours( src, contours, hierarchy, findmode, findmethod, pt); + + //Assigning the list as the Output Variable + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, contours.size(), &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_gaussianblur(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double ksize_width,ksize_height,sigmaX,sigmaY; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of ksize_height + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &ksize_height); + if(intErr) + return intErr; + + //for value of ksize_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &ksize_width); + if(intErr) + return intErr; + + //for value of sigmaX + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &sigmaX); + if(intErr) + return intErr; + + //for value of sigmaY + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &sigmaY); + if(intErr) + return intErr; + + //applying checks to input parameters, and assigning default values in case of error + Mat new_image(image.rows,image.cols,CV_8UC3); + if(ksize_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead"); + ksize_height=1; + } + if(ksize_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead"); + ksize_width=1; + } + if((int)ksize_height%2==0) + { + ksize_height+=1; + sciprint("Odd Value Required for Height. %f value was used instead",&ksize_height); + } + if((int)ksize_width%2==0) + { + ksize_width+=1; + sciprint("Odd Value Required for Width. %f value was used instead",&ksize_width); + } + + //temporary size variable, to use in function + Size sz(ksize_height,ksize_width); + + //void GaussianBlur(InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT ) + GaussianBlur(image,new_image,sz,sigmaX,sigmaY); + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_generateCheckerboardPoints.cpp b/sci_gateway1/cpp/opencv_generateCheckerboardPoints.cpp new file mode 100644 index 0000000..998f6fe --- /dev/null +++ b/sci_gateway1/cpp/opencv_generateCheckerboardPoints.cpp @@ -0,0 +1,157 @@ +/*************************************************** +Author : Tanmay Chaudhari + **************************************************/ + +#include +#include +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" + + int opencv_genCheckerboardPoints(char *fname, unsigned long fname_len) + { + //Error management variable + SciErr sciErr; + + //Variable declaration + int iType = 0; + int iComplex = 0; + int rowsOfBoardSize = 0; + int rowsOfSquareSize = 0; + int colsOfBoardSize = 0; + int colsOfSquareSize = 0; + int iterator1 = 0; + int iterator2 = 0; + int *piAddr = NULL; + double *boardSize = NULL; + double *squareSize = NULL; + double *worldPoints = NULL; + + //Check input output arguments + checkInputArgument(pvApiCtx, 2, 2); + checkOutputArgument(pvApiCtx, 1, 1); + + //Get variable address of the first input argument + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfBoardSize, &colsOfBoardSize, &boardSize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(((rowsOfBoardSize != 1) || (colsOfBoardSize != 2)) || (boardSize[0] <= 0) || (boardSize[1] <= 0)) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the second input argument + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfSquareSize, &colsOfSquareSize, &squareSize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(((rowsOfSquareSize != 1) || (colsOfSquareSize != 1)) || (squareSize[0] <= 0)) + { + printError(&sciErr, 0); + return 0; + } + + boardSize[0] -= 1; + boardSize[1] -= 1; + worldPoints = (double*)malloc(sizeof(double) * 2 * (int(boardSize[0])) * (int(boardSize[1]))); + + iterator1 = 0; + iterator2 = (int(boardSize[0])) * (int(boardSize[1])); + + for(int j = 0; j <= int(boardSize[1]) - 1; j++) + { + for(int i = 0; i <= int(boardSize[0]) - 1; i++) + { + worldPoints[iterator1] = j * squareSize[0]; + worldPoints[iterator2] = i * squareSize[0]; + iterator1++; + iterator2++; + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, (int(boardSize[0])) * (int(boardSize[1])), 2, worldPoints); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Return Output Argument + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} + + + diff --git a/sci_gateway1/cpp/opencv_getStructuringElement.cpp b/sci_gateway1/cpp/opencv_getStructuringElement.cpp new file mode 100644 index 0000000..8226671 --- /dev/null +++ b/sci_gateway1/cpp/opencv_getStructuringElement.cpp @@ -0,0 +1,147 @@ +/************************************************* +Author: Sukul Bagai +**************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getStructuringElement(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr1 = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k,type; + char **gettype = NULL; + double cols,rows,anchorX,anchorY; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr1); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, NULL, NULL); + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + gettype= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + gettype[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, gettype); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp("MORPH_RECT",gettype[0])==0) + type=MORPH_RECT; + else if(strcmp("MORPH_ELLIPSE",gettype[0])==0) + type=MORPH_ELLIPSE; + else if(strcmp("MORPH_CROSS",gettype[0])==0) + type=MORPH_CROSS; + + //for cols + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&cols); + if(intErr) + return intErr; + + //for rows + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &rows); + if(intErr) + return intErr; + + //for anchorX + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4 ,&anchorX); + if(intErr) + return intErr; + + //for anchorY + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&anchorY); + if(intErr) + return intErr; + + Mat temp = getStructuringElement(type,Size(rows,cols),Point(anchorX,anchorY)); + + double *m = (double *)malloc(temp.rows*temp.cols*sizeof(double)); + for(i=0;i(i, j); + *(m + i*temp.cols + j) = intensity; + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, temp.rows, temp.cols, m); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} diff --git a/sci_gateway1/cpp/opencv_getTextSize.cpp b/sci_gateway1/cpp/opencv_getTextSize.cpp new file mode 100644 index 0000000..150b8ec --- /dev/null +++ b/sci_gateway1/cpp/opencv_getTextSize.cpp @@ -0,0 +1,188 @@ +/*************************************************** +Author : Sukul Bagai , Abhilasha Sancheti +**************************************************** +Usage : [size,baseline] = getTextSize("","",fontscale,thickness); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getTextSize(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + int *piLen2 = NULL; + char **pstData = NULL; + char **pstData2 = NULL; + double font_scale = 0,thickness = 0; + int i; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 2, 2) ; + + //for string + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + pstData[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for fontface + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen2 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen2, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData2 = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + pstData2[i] = (char*)malloc(sizeof(char) * (piLen2[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen2, pstData2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of font scale + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &font_scale); + if(intErr) + return intErr; + + //for value of thickness + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &thickness); + if(intErr) + return intErr; + + int fontFace; + + if(strcmp(pstData2[0],"FONT_HERSHEY_SIMPLEX")==0) + fontFace = FONT_HERSHEY_SIMPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_PLAIN")==0) + fontFace= FONT_HERSHEY_PLAIN; + else if (strcmp(pstData2[0],"FONT_HERSHEY_DUPLEX")==0) + fontFace= FONT_HERSHEY_DUPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_COMPLEX")==0) + fontFace= FONT_HERSHEY_COMPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_TRIPLEX")==0) + fontFace = FONT_HERSHEY_TRIPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_COMPLEX_SMALL")==0) + fontFace = FONT_HERSHEY_COMPLEX_SMALL; + else if (strcmp(pstData2[0],"FONT_HERSHEY_SCRIPT_SIMPLEX")==0) + fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + else if (strcmp(pstData2[0],"FONT_HERSHEY_SCRIPT_COMPLEX")==0) + fontFace = FONT_HERSHEY_SCRIPT_COMPLEX; + + int baseline; + Size sz = getTextSize(pstData[0],fontFace,font_scale,thickness,&baseline); + double tempsize[2]; + tempsize[0]=sz.width; + tempsize[1]=sz.height; + double returnbaseline = (double)baseline; + + sciErr = createMatrixOfDouble(pvApiCtx,nbInputArgument(pvApiCtx)+1,1,2,tempsize); + if(sciErr.iErr) + { + printError(&sciErr,0); + return 0; + } + sciErr = createMatrixOfDouble(pvApiCtx,nbInputArgument(pvApiCtx)+2,1,1,&returnbaseline); + if(sciErr.iErr) + { + printError(&sciErr,0); + return 0; + } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_getgaussiankernel.cpp b/sci_gateway1/cpp/opencv_getgaussiankernel.cpp new file mode 100644 index 0000000..99c6d7d --- /dev/null +++ b/sci_gateway1/cpp/opencv_getgaussiankernel.cpp @@ -0,0 +1,142 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* +Mat getGaussianKernel(int ksize, double sigma, int ktype=CV_64F ) +********************************************************/ + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getgaussiankernel(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + double ksize, sigma; + char **kType = NULL; + int i, j, k; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + + //for value of ksize + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &ksize); + if(intErr || ksize < 0 || (int) ksize % 2 == 0) + { + return intErr; + } + + + //for first value of size + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &sigma); + if(intErr) + { + return intErr; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + kType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + kType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, kType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + Mat temp; + + if(strcmp(kType[0], "CV_32F") == 0) + temp = getGaussianKernel(ksize, sigma, CV_32F + ); + else if(strcmp(kType[0], "CV_64F") == 0) + temp = getGaussianKernel(ksize, sigma, CV_64F); + + double *m = (double *)malloc(temp.rows*temp.cols*sizeof(double)); + for(i=0;i(i, j); + *(m + i*temp.cols + j) = intensity; + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, temp.rows, temp.cols, m); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + + + + + diff --git a/sci_gateway1/cpp/opencv_getrectsubpix.cpp b/sci_gateway1/cpp/opencv_getrectsubpix.cpp new file mode 100644 index 0000000..2f6e835 --- /dev/null +++ b/sci_gateway1/cpp/opencv_getrectsubpix.cpp @@ -0,0 +1,142 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* + void getRectSubPix(InputArray image, Size patchSize, Point2f center, OutputArray patch, int patchType=-1 )¶ +********************************************************/ + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getrectsubpix(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int i,j,k; + double centerX, centerY, alpha, beta, patchType; + + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for first value of size + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &alpha); + if(intErr) + { + return intErr; + } + + + //for second value of size + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &beta); + if(intErr) + { + return intErr; + } + + //for x coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, ¢erX); + if(intErr) + { + return intErr; + } + + //for y coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, ¢erY); + if(intErr) + { + return intErr; + } + + + //for patchType + sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &patchType); + if(intErr) + { + return intErr; + } + + + Size patchSize = Size(alpha, beta); + + Point2f center = Point2f(centerX, centerY); + + Mat patch; + + getRectSubPix(image, patchSize, center, patch, patchType); + + + String tempstring = type2str(patch.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, patch, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_getrotationmatrix2D.cpp b/sci_gateway1/cpp/opencv_getrotationmatrix2D.cpp new file mode 100644 index 0000000..5a54ed2 --- /dev/null +++ b/sci_gateway1/cpp/opencv_getrotationmatrix2D.cpp @@ -0,0 +1,121 @@ +/******************************************************** + Author: Shubheksha Jalan +********************************************************* +Mat getRotationMatrix2D(Point2f center, double angle, double scale) +********************************************************/ + + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_getrotationmatrix2D(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + double centerX, centerY, angle, scale; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1); + + + //for x coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, ¢erX); + if(intErr) + { + return intErr; + } + + //for y coordinate of center + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, ¢erY); + if(intErr) + { + return intErr; + } + + + //for value of angle + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &angle); + if(intErr) + { + return intErr; + } + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &scale); + if(intErr) + { + return intErr; + } + + Point2f center = Point2f(centerX, centerY); + + Mat temp = getRotationMatrix2D(center, angle, scale); + + double *m = (double *)malloc(temp.rows*temp.cols*sizeof(double)); + for(i=0;i(i, j); + *(m + i*temp.cols + j) = intensity; + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, temp.rows, temp.cols, m); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway1/cpp/opencv_goodfeaturestotrack.cpp b/sci_gateway1/cpp/opencv_goodfeaturestotrack.cpp new file mode 100644 index 0000000..79e614f --- /dev/null +++ b/sci_gateway1/cpp/opencv_goodfeaturestotrack.cpp @@ -0,0 +1,168 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + corner_points = goodFeaturesToTrack( input_image, maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector((1 for true)/(0 for false)),k ); +********************************************************/ + + +// Abhilasha Sancheti +// sample input : +//output: (image , corners) +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_goodfeaturestotrack(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + + int i,j, detector; + double maxCorners ,qualityLevel,minDistance,blocksize=3 ,k=0.04, usedetector; + bool useHarrisDetector = false; + + //checking input argument + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat image; + retrieveImage(image,1); + + //for maximum corners + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&maxCorners); + if(intErr) + return intErr; + + + //for qualityLevel + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&qualityLevel); + if(intErr) + return intErr; + + //for minDistance + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4 ,&minDistance); + if(intErr) + return intErr; + + + //for blocksize + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&blocksize); + if(intErr) + return intErr; + + //for Harrisusedetector taking integer 1 or 0 + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6,&usedetector); + if(intErr) + return intErr; + detector=(int)usedetector; + + //for k value + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7 ,&k); + if(intErr) + return intErr; + + + //checking the input parameter usedetector + if (detector == 1) + useHarrisDetector = true; + else if(detector == 0) + useHarrisDetector = false; + else + { + sciprint("Either 1 or 0 , 0 value was used instead"); + useHarrisDetector = false; + } + + /// processing of the input image and other parameters + if( maxCorners < 1 ) { maxCorners = 1; sciprint("maxcorners cannot be less than 1 , using 1 instead");} + + /// Parameters for Shi-Tomasi algorithm + + vector corners; + /// Copy the source image + Mat src_gray; + cvtColor( image, src_gray, CV_BGR2GRAY ); + /// Apply corner detection + goodFeaturesToTrack( src_gray,corners,maxCorners,qualityLevel,minDistance,Mat(),blocksize,useHarrisDetector,k ); + + int row = corners.size(); + double *cor = (double *)malloc(2*row*sizeof(double *)); + for (int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_houghcircles(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int i,j,k; + double *dp =NULL , *mindist = NULL ,*param1= NULL ,*param2=NULL; + double *minradius =NULL, *maxradius = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + // for dp parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols ,&dp); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for minDist parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&mindist); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for param1 parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols ,¶m1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for param2 parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr5, &iRows, &iCols ,¶m2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + // for minradius parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr6, &iRows, &iCols ,&minradius); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + // for maxradius parameter of houghCircles function + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr7, &iRows, &iCols ,&maxradius); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Mat src_gray; + cvtColor( src, src_gray, CV_BGR2GRAY ); + + /// Reduce the noise so we avoid false circle detection + + + vector circles; + + /// Apply the Hough Transform to find the circles + HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, dp[0], mindist[0], param1[0], param2[0], minradius[0], maxradius[0] ); + int rows = circles.size(); + cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int opencv_houghlines(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int i,j,k; + double rho, theta, srn, stn, threshold; + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + cvtColor(image, image, CV_BGR2GRAY); + //for value of rho + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &rho); + if(intErr) + { + return intErr; + } + + //for value of theta + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &theta); + if(intErr) + { + return intErr; + } + + //for value of threshold + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &threshold); + if(intErr) + { + return intErr; + } + //for value of srn + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &srn); + if(intErr) + { + return intErr; + } + //for value of stn + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &stn); + if(intErr) + { + return intErr; + } + vector < vector > output; + cout<<"here5"; + HoughLines(image, output, rho, theta, threshold, srn, stn); + cout<<"here5"; +//create matrix from 2-D vector + int row = output.size(); + cout< +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int opencv_houghlinesp(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int i,j,k; + double rho, theta, minLineLength = 0, maxLineGap = 0, threshold; + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + cvtColor(image, image, CV_BGR2GRAY); + //for value of rho + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &rho); + if(intErr) + { + return intErr; + } + + //for value of theta + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &theta); + if(intErr) + { + return intErr; + } + + //for value of threshold + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &threshold); + if(intErr) + { + return intErr; + } + //for value of minLineLength + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &minLineLength); + if(intErr) + { + return intErr; + } + + //for value of maxLineGap + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &maxLineGap); + if(intErr) + { + return intErr; + } + + vector < vector > output; + + HoughLinesP(image, output, rho, theta, threshold, minLineLength, maxLineGap); + +//create matrix from 2-D vector + int row = output.size(); + double *cor = (double *)malloc(row*4*sizeof(double)); + + + for (int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_im2double(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int i,j,k; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + Mat image; + retrieveImage(image, 1); + + Mat img_dbprec; + image.convertTo(img_dbprec, CV_64FC3,1.0/255.0); /// Create Image with Double Precision + + string tempstring = type2str(img_dbprec.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,img_dbprec,1); + free(checker); + + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_imageSet.cpp b/sci_gateway1/cpp/opencv_imageSet.cpp new file mode 100644 index 0000000..9b27840 --- /dev/null +++ b/sci_gateway1/cpp/opencv_imageSet.cpp @@ -0,0 +1,294 @@ + +/*************************************************** +Author : Rohit Suri +***************************************************/ + +#include +#include +#include +#include +#include +#include +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + + + string formats[] = {"jpeg","jpg","bmp","png","JPEG","JPG","BMP","PNG"}; + + vector > descriptionVector; + + const char pathSeparator = + #ifdef _WIN32 + '\\'; + #else + '/'; + #endif + + bool recursive = false; + bool listDirError = 0; + + bool isImage(string fname) + { + for(int i=0;i<8;i++) + { + if(fname.substr(fname.length()-formats[i].length()).compare(formats[i])==0) + return 1; + } + return 0; + } + + void listDir(string dirName, vector ¤tDir) + { + DIR *dir; + struct dirent *entry; + if(!(dir = opendir(dirName.c_str()))) + { + Scierror(999,"Cannot open directory for reading. Please check the directory name.\n"); + listDirError = 1; + return; + } + if(!(entry = readdir(dir))) + { + Scierror(999,"The directory is empty.\n"); + listDirError = 1; + return; + } + + do + { + if (entry -> d_type == DT_DIR) + { + if(strcmp(entry->d_name,".")==0 || strcmp(entry->d_name,"..")==0) continue; + if(recursive) + { + vector nextDir; + nextDir.push_back(entry->d_name); + listDir(dirName+pathSeparator+entry->d_name,nextDir); + } + } + else + { + if(isImage(entry->d_name)) + { + //cout<d_name<d_name); + } + } + }while(entry = readdir(dir)); + vector::iterator it = currentDir.begin(); + sort(it+1,currentDir.end()); + descriptionVector.push_back(currentDir); + } + + /*Calling syntax: imageSet(imageLocation) OR imageSet(imageFolder,'recursive') */ + + int opencv_imageSet(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int *piAddr = NULL; + int *piChild = NULL; + int *piLen = NULL; + char **pstData = NULL; + int iRows, iCols; + + string filePath,fileName,tempPath; + char **description = NULL; + char ***location = NULL; + int *count = NULL; + int descriptionCount = 0; + int pos = 0; + char *objectType = "imageSet"; + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + filePath = string(pstData[0]); + + if(*getNbInputArgument(pvApiCtx) == 2) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp("recursive",pstData[0])!=0) + { + Scierror(999, "Error: Argument %s is invalid. \n", pstData[0]); + return 0; + } + recursive = true; + } + + //------Actual processing------// + while(filePath[filePath.length()-1]==pathSeparator) + { + filePath = filePath.substr(0,filePath.length()-1); + } + vector nextDir; + nextDir.push_back(filePath.substr(filePath.find_last_of(pathSeparator)+1)); + + listDir(filePath,nextDir); + + if(listDirError) + { + listDirError = 0; //Doing this is very important as it is a global + return 0; + } + for(int i=0;i1) + descriptionCount++; + + } + description = (char**) malloc(sizeof(char*) * descriptionCount); + count = (int*) malloc(sizeof(int) * descriptionCount); + location = (char***) malloc(sizeof(char**) * descriptionCount); + + for(int i=0;i1) + { + description[pos] = (char*) malloc(sizeof(char) * descriptionVector[i][0].length() + 1); + descriptionVector[i][0].copy(description[pos],descriptionVector[i][0].length()); + description[pos][descriptionVector[i][0].length()] = 0; + + count[pos] = descriptionVector[i].size()-1; + + location[pos] = (char**) malloc(sizeof(char*) * (descriptionVector[i].size()-1)); + for(int j=1;j +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_imcontrast(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + double alpha, beta; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + + //for value of alpha + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &alpha); + if(intErr) + { + return intErr; + } + + //for value of beta + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &beta); + if(intErr) + { + return intErr; + } + + + //we copy the original image into the new_image, and change values of each pixel using value of alpha and beta given by the user + Mat new_image = Mat::zeros( image.size(), image.type() ); + + //changes the attributes of each individual pixel + for( int y = 0; y < image.rows; y++ ) + { + for( int x = 0; x < image.cols; x++ ) + { + for( int c = 0; c < 3; c++ ) + { + new_image.at(y,x)[c] = + saturate_cast( alpha*( image.at(y,x)[c] ) + beta ); + } + } + } + + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + +} +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_imcrop.cpp b/sci_gateway1/cpp/opencv_imcrop.cpp new file mode 100644 index 0000000..d180c0a --- /dev/null +++ b/sci_gateway1/cpp/opencv_imcrop.cpp @@ -0,0 +1,130 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imcrop(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double x, y, width, height; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + + //for value of top-left x-coordinate + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &x); + if(intErr) + { + return intErr; + } + + //for value top-left y-coordinate + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &y); + if(intErr) + { + return intErr; + } + + //for value of width + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &width); + if(intErr) + { + return intErr; + } + + //for value of height + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &height); + if(intErr) + { + return intErr; + } + + if(x>=image.cols || y>=image.rows || x<0 || y<0) + { + sciprint("Invalid x or y value\n"); + return 0; + } + if(width<=0 || height<=0 || x+width > image.cols || y+height > image.rows) + { + sciprint("Invalid width or height value\n"); + return 0; + } + //defining a temporary rectangle, that denotes the area that has to be cropped into the new image + Rect myROI(x, y, width, height); + + // Crop the full image to that image contained by the rectangle myROI + // Note that this doesn't copy the data + Mat croppedRef(image, myROI); + + Mat cropped; + // Copy the data into new matrix + croppedRef.copyTo(cropped); + + string tempstring = type2str(cropped.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,cropped,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_imfindcircles.cpp b/sci_gateway1/cpp/opencv_imfindcircles.cpp new file mode 100644 index 0000000..2569343 --- /dev/null +++ b/sci_gateway1/cpp/opencv_imfindcircles.cpp @@ -0,0 +1,128 @@ +/******************************************************** +Function :imfindcircles +Syntax :B=imfindcircles(A) +Author: Tess Zacharias + +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imfindcircles(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + Mat src_gray,image; + double Rmin,Rmax; + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 2) ; + retrieveImage(image, 1); + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &Rmin); + if(intErr) + { + return intErr; + } + if(Rmin!= round(Rmin) || Rmin<=0) + { + sciprint("The value of minium Radius must be an integer\n"); + return 0; + } + if(Rmin<5) + { + sciprint("The value of minium Radius too small\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &Rmax); + if(intErr) + { + return intErr; + } + if(Rmax!= round(Rmax) || Rmax<=0) + { + sciprint("The value of maximum Radius must be an integer\n"); + return 0; + } + cvtColor(image,src_gray, CV_BGR2GRAY); + GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 ); + vector circles; + HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 2, 10, 200, 100,Rmin,Rmax); + int k=circles.size(); + double *radius=NULL; + radius=( double *)malloc(sizeof(double)*k); + double *c1=NULL; + c1=( double *)malloc(sizeof(double)*k); + double *c2=NULL; + c2=( double *)malloc(sizeof(double)*k); + for( size_t i= 0; i < circles.size(); i++ ) + { + c1[i]=cvRound(circles[i][0]); + c2[i]=cvRound(circles[i][1]); + radius[i]=cvRound(circles[i][2]); + } + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 2, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddrNew, 1, 1, k, c1); + free(c1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddrNew, 2, 1, k, c2); + free(c2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + if(Lhs==2) + { + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) +2,1,k,radius); + free(radius); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + } + ReturnArguments(pvApiCtx); + return 0; + + } +} diff --git a/sci_gateway1/cpp/opencv_imread.cpp b/sci_gateway1/cpp/opencv_imread.cpp new file mode 100644 index 0000000..c02af10 --- /dev/null +++ b/sci_gateway1/cpp/opencv_imread.cpp @@ -0,0 +1,99 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + #include "../common.cpp" + +int opencv_imread(char *fname, unsigned long fname_len) +{ + SciErr sciErr; + int iLen = 0; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int *piAddr = NULL; + int *piLen = NULL; + char **pstData = NULL; + int **pstData1 = NULL; + int i,j,k=0; + + + //Checking number of input and output arguments (enviromnet variable, min arguments, max arguments) + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // get Address of inputs + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + pstData[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating image matrix, by getting the image at the specified path + Mat img = imread(pstData[0],CV_LOAD_IMAGE_COLOR); + + string tempstring = type2str(img.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,img,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_imread.cpp~ b/sci_gateway1/cpp/opencv_imread.cpp~ new file mode 100644 index 0000000..2f5ad90 --- /dev/null +++ b/sci_gateway1/cpp/opencv_imread.cpp~ @@ -0,0 +1,99 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + #include "../common.cpp" + +int opencv_imread(char *fname, unsigned long fname_len) +{ + SciErr sciErr; + int iLen = 0; + //variable info + int iRows = 0; + int iCols = 0; + int piRows = 0; + int piCols = 0; + int *piAddr = NULL; + int *piLen = NULL; + char **pstData = NULL; + int **pstData1 = NULL; + int i,j,k=0; + + + //Checking number of input and output arguments (enviromnet variable, min arguments, max arguments) + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // get Address of inputs + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + pstData[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //creating image matrix, by getting the image at the specified path + Mat img = imread(pstData[0],CV_LOAD_IMAGE_UNCHANGED); + + string tempstring = type2str(img.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,img,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_imresize.cpp b/sci_gateway1/cpp/opencv_imresize.cpp new file mode 100644 index 0000000..190ab5e --- /dev/null +++ b/sci_gateway1/cpp/opencv_imresize.cpp @@ -0,0 +1,94 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imresize(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + double alpha, beta; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + + //for new no. of rows + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &alpha); + if(sciErr.iErr) + { + return intErr; + } + + //for new no. of columns + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &beta); + if(sciErr.iErr) + { + return intErr; + } + + + Mat new_image; + Size size(alpha, beta); + + if(alpha<=0 || beta<=0) + { + sciprint("Invalid new size\n"); + return 0; + } + //resizes the image to new_image to the size given by the user + resize(image,new_image,size); + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_imsharpen.cpp b/sci_gateway1/cpp/opencv_imsharpen.cpp new file mode 100644 index 0000000..ad0a593 --- /dev/null +++ b/sci_gateway1/cpp/opencv_imsharpen.cpp @@ -0,0 +1,59 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_imsharpen(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + + Mat tmp; + cv::GaussianBlur(image, tmp, cv::Size(5,5), 5); + cv::addWeighted(image, 1.5, tmp, -0.5, 0, image); + + string tempstring = type2str(image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, image, 1); + free(checker); + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_imwrite.cpp b/sci_gateway1/cpp/opencv_imwrite.cpp new file mode 100644 index 0000000..2815ac9 --- /dev/null +++ b/sci_gateway1/cpp/opencv_imwrite.cpp @@ -0,0 +1,85 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_imwrite(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piLen = NULL; + char **pstData=NULL; + int i,j,k; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + //CheckOutputArgument(pvApiCtx, 0, 0) ; + Mat image; + retrieveImage(image, 1); + + //Now, retriving path + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i = 0 ; i < iRows * iCols ; i++) + { + pstData[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //image path will contain the path where the image has to be written + string image_path=pstData[0]; + + // image_path="\""+image_path+"\""; + + //writes to the path + imwrite(image_path,image); + + return 0; + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_indexImages.cpp b/sci_gateway1/cpp/opencv_indexImages.cpp new file mode 100644 index 0000000..6a37466 --- /dev/null +++ b/sci_gateway1/cpp/opencv_indexImages.cpp @@ -0,0 +1,663 @@ +/**************************************************************************************** +* Author: Umang Agrawal * +* Code: indexImages.cpp * +* Function Call: indexImage = indexImages( Image_Set, Bag, Optional Arguments) * +* Optional Argument: Name Value * +* Verbose Bool(1 or 0) * +* SaveFeatureLocations Bool(1 or 0) * +****************************************************************************************/ +#include +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/ml/ml.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + bool response(const KeyPoint& p1, const KeyPoint& p2) { + return p1.response > p2.response; + } + + int opencv_indexImages(char *fname, unsigned long fname_len) + { + SciErr sciErr; + + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piChild = NULL; + int *piGrandChild = NULL; + int iRows, iCols; + int *piLen = NULL; + char **pstData = NULL; + char *objectType = "invertedImageIndex"; + char **description = NULL; + char ***location = NULL; + int *count = NULL; + int descriptionCount; + char **arg = NULL; + char **filePath = NULL; + + int inp_params = 0; + char *bagOfFeaturesLocation = NULL; + double *upright_bag = NULL; + double *strength_bag = NULL; + double *vocab_size_bag = NULL; + int count_ver = 0, count_save = 0; + double save = 1; + double verbose = 1; + int upright = 1; + int vocab_size = 500; + double strength = 0.8; + + vector key_size_vector; + double *wordFrequency = NULL; + double ***ImageWords = NULL; + int indx; + + Mat image; + Mat dictionary; + Mat featuresUnclustered; + Mat feature_des; + Mat des_matched; + Mat hist; + vector keypoints; + vector valid_key; + vector< vector > clusterID; + int key_size; + int v_key_size; + + CheckInputArgument(pvApiCtx, 1, 6); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 1); //Check on Number of Output Arguments + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr1)) + { + Scierror(999, "Error: Invalid first argument. List Expected.\n"); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"imageSet")==0)) + { + Scierror(999, "Error: The input argument 1 is not of type imageSet.\n"); + return 0; + } + + // Extracting Description attribute of input argument + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( iRows!= 1 ) + { + Scierror(999,"Expecting an image Set with single type of Images.\n"); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + description = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + description[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr1, 2, &iRows, &iCols, piLen, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + descriptionCount = iRows; + + // Extracting Count attribute of input argument + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr1, 3, &iRows, &iCols, &count); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( iRows!= 1 ) + { + Scierror(999,"Expecting an image Set with single type of Images.\n"); + return 0; + } + location = (char***) malloc(sizeof(char**) * descriptionCount); + sciErr = getListItemAddress(pvApiCtx, piAddr1, 4, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int iter = 1; iter<=descriptionCount; iter++) + { + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + location[iter-1] = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int colIter = 0 ; colIter < iRows * iCols ; colIter++) + { + location[iter-1][colIter] = (char*)malloc(sizeof(char) * (piLen[colIter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, location[iter-1]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + +//.............................................................................................................. + inp_params = *getNbInputArgument(pvApiCtx); + if( inp_params>=2 ) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr2)) + { + Scierror(999, "Error: Invalid first argument. List Expected.\n"); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!(strcmp(pstData[0],"bagOfFeatures")==0)) + { + Scierror(999, "Error: The input argument 2 is not of type bagOfFeatures.\n"); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 2, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + bagOfFeaturesLocation = pstData[0]; + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr2, 3, &iRows, &iCols, &vocab_size_bag); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr2, 4, &iRows, &iCols, &strength_bag); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr2, 5, &iRows, &iCols, &upright_bag); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + upright = int(upright_bag[0]); + vocab_size = int(vocab_size_bag[0]); + strength = strength_bag[0]; + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + +//................................................................................................................ + for( int i=3; i<=inp_params; i++) + { + if( inp_params%2 != 0) + { + Scierror(999,"Either Argument Name or its Value missing\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, i, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr3)) + { + Scierror(999, "%s: Wrong type of argument for Name of Optional Argument. A string is expected.\n", fname); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(piLen); + return 0; + } + arg = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + arg[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1)); + } + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(piLen); + free(arg); + return 0; + } + + if(strcmp(arg[0],"Verbose") == 0) + { + if( count_ver != 0) + { + Scierror(999,"Verbose has been called twice.\n"); + return 0; + } + free(arg); + free(piLen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !(isDoubleType(pvApiCtx, piAddr4)||isIntegerType(pvApiCtx, piAddr4))) + { + Scierror(999,"Not a valid type of value for Verbose.\n"); + return 0; + } + //Reading the Value of the argument + if(getScalarDouble(pvApiCtx, piAddr4, &verbose)) + { + Scierror(999,"Not a valid type of value for Verbose.\n"); + return 0; + } + if( !(verbose == 1|| verbose == 0) ) + { + Scierror(999,"Enter a valid value for Verbose (Either 0 or 1)\n"); + return 0; + } + i++; + count_ver += 1; + } + else if(strcmp(arg[0],"SaveFeatureLocations") == 0) + { + if( count_save != 0) + { + Scierror(999,"SaveFeatureLoactions has been called twice.\n"); + return 0; + } + free(arg); + free(piLen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !(isDoubleType(pvApiCtx, piAddr4)||isIntegerType(pvApiCtx, piAddr4))) + { + Scierror(999,"Not a valid type of value for SaveFeatureLoactions.\n"); + return 0; + } + //Reading the Value of the argument + if(getScalarDouble(pvApiCtx, piAddr4, &save)) + { + Scierror(999,"Not a valid type of value for SaveFeatureLoactions.\n"); + return 0; + } + if( !(save == 1|| save == 0) ) + { + Scierror(999,"Enter a valid value for SaveFeatureLoactions (Either 0 or 1)\n"); + return 0; + } + i++; + count_save += 1; + } + else + { + Scierror(999,"Invalid Argument Name\n"); + return 0; + } + } + } + + if(int(verbose)) + { + sciprint("Creating an Inverted image Index Using Bag-Of-Features.\n"); + sciprint("--------------------------------------------------------\n"); + } + + if( inp_params == 1) + { + bagOfFeaturesLocation = "Bag-Of-Features.yml"; + SurfFeatureDetector detector(100, 4, 2, 1, upright); + SurfDescriptorExtractor extractor(100, 4, 2, 1, upright); + + if(int(verbose)) + { + sciprint("Creating Bag-Of-Features from %d image sets.\n\n",descriptionCount); + for(int i=0; i matcher = DescriptorMatcher::create("FlannBased"); + Ptr extractor = new SurfDescriptorExtractor(100, 4, 2, 1, 1); + SurfFeatureDetector detector(100, 4, 2, 1, 1); + BOWImgDescriptorExtractor bowDE(extractor, matcher); + bowDE.setVocabulary(dictionary); + for( int i=0; i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" + // #include "../common.h" + // #include "../common.cpp" + + int opencv_integralFilter(char *fname, unsigned long fname_len) + { + //Error management variable + SciErr sciErr; + + //Variable declaration + int i, j, k; + int iType = 0; + int iComplex = 0; + int rowsOfintImage = 0; + int colsOfintImage = 0; + int rowsOfBbox = 0; + int rowsOfWeights = 0; + int colsOfBbox = 0; + int colsOfWeights = 0; + int rowsOfFilter = 0; + int colsOfFilter = 0; + int sR = 0; + int sC = 0; + int eR = 0; + int eC = 0; + int *piAddr = NULL; + int *outSize = NULL; + double bboxSum = 0; + double *integralFilter = NULL; + double *intImage = NULL; + double *bbox = NULL; + double *weights = NULL; + double *filterSize = NULL; + + //Check input output argument + checkInputArgument(pvApiCtx, 4, 4); + checkOutputArgument(pvApiCtx, 1, 1); + + //Get variable address of the first input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfintImage, &colsOfintImage, &intImage); + if(sciErr.iErr || rowsOfintImage == 0 || colsOfintImage == 0) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the second input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfBbox, &colsOfBbox, &bbox); + if(sciErr.iErr || colsOfBbox != 4 || rowsOfBbox == 0) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the third input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfWeights, &colsOfWeights, &weights); + if(sciErr.iErr || colsOfWeights == 0 || rowsOfWeights != 1 || colsOfWeights != rowsOfBbox) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the fourth input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfFilter, &colsOfFilter, &filterSize); + if(sciErr.iErr || colsOfFilter != 2 || rowsOfFilter != 1) + { + printError(&sciErr, 0); + return 0; + } + + outSize = (int*)malloc(sizeof(int) * 2); + if(filterSize[0] != 0 && filterSize[1] != 0) + { + outSize[0] = rowsOfintImage - filterSize[0]; + outSize[1] = colsOfintImage - filterSize[1]; + } + else + { + outSize[0] = rowsOfintImage - 1; + outSize[1] = colsOfintImage - 1; + } + + if(outSize[0] <= 0 || outSize[1] <= 0) + outSize[0] = outSize[1] = 0; + + integralFilter = (double*)malloc(sizeof(double) * outSize[0] * outSize[1]); + + for( i = 0; i < outSize[0] * outSize[1]; i++) + integralFilter[i] = 0; + + for( i = 1; i <= outSize[1]; i++) + { + for( j = 1; j <= outSize[0]; j++) + { + for( k = 0; k < rowsOfBbox; k++) + { + sR = j + bbox[1 * rowsOfBbox + k] - 1; + sC = i + bbox[0 * rowsOfBbox + k] - 1; + eR = sR + bbox[3 * rowsOfBbox + k]; + eC = sC + bbox[2 * rowsOfBbox + k]; + + bboxSum = intImage[(eC - 1) * rowsOfintImage + (eR - 1)] - intImage[(sC - 1) * rowsOfintImage + (eR - 1)]; + bboxSum -= intImage[(eC - 1) * rowsOfintImage + (sR - 1)]; + bboxSum += intImage[(sC - 1) * rowsOfintImage + (sR - 1)]; + + integralFilter[(j - 1) * outSize[1] + (i - 1)] = integralFilter[(j - 1) * outSize[1] + (i - 1)] + weights[k] * bboxSum; + } + } + } + + //Creating list to store integral filter in it + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, outSize[0], outSize[1], integralFilter); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Return output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_integralImage.cpp b/sci_gateway1/cpp/opencv_integralImage.cpp new file mode 100644 index 0000000..b40e268 --- /dev/null +++ b/sci_gateway1/cpp/opencv_integralImage.cpp @@ -0,0 +1,153 @@ +/*************************************************** +Author : Tanmay Chaudhari +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + // #include "../common.cpp" + +int opencv_integralImage(char *fname, unsigned long fname_len) +{ + //Error management variable + SciErr sciErr; + + //Variable declaration + int nbInputArguments = 0; + int iRows = 0; + int iCols = 0; + int *piLen = NULL; + int *piAddr = NULL; + int tilted = 0; + char **pstData = NULL; + double *integralImage; + Mat image, sum, sqsum, tiltedsum; + + //Check input output arguments + checkInputArgument(pvApiCtx, 1, 2); + checkOutputArgument(pvApiCtx, 1, 1); + + nbInputArguments = *getNbInputArgument(pvApiCtx); + + //getting input arguments + retrieveImage(image, 1); + + if(nbInputArguments == 2) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(strcmp(pstData[0],"upright")==0) + tilted=0; + else if(strcmp(pstData[0],"rotated")==0) + tilted=1; + else + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are upright or rotated.\n", pstData[0]); + return 0; + } + } + + integral(image,sum,sqsum,tiltedsum); + + if(tilted==0) + { + integralImage = (double*)malloc(sizeof(double) * int(sum.rows) * int(sum.cols)); + for(int i=0;i(i,j); + } + } + } + else + { + integralImage = (double*)malloc(sizeof(double) * int(sum.rows) * (int(sum.cols)+1)); + for(int i=0;i(i,j); + } + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(tilted==0) + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, int(sum.rows), int(sum.cols), integralImage); + else + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, int(sum.rows), int(sum.cols)+1, integralImage); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Return output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_integralKernel.cpp b/sci_gateway1/cpp/opencv_integralKernel.cpp new file mode 100644 index 0000000..5dfabf3 --- /dev/null +++ b/sci_gateway1/cpp/opencv_integralKernel.cpp @@ -0,0 +1,251 @@ +/*************************************************** +Author : Tanmay Chaudhari + ***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" + // #include "../common.h" + // #include "../common.cpp" + + int opencv_integralKernel(char *fname, unsigned long fname_len) + { + //Error management variable + SciErr sciErr; + + //Variable declaration + int i, j, k; + int iType = 0; + int iComplex = 0; + int rowsOfBbox = 0; + int rowsOfWeights = 0; + int colsOfBbox = 0; + int colsOfWeights = 0; + int *piAddr = NULL; + double *filterSize = NULL; + double *hlSize = NULL; + double *ulCorner = NULL; + double *lrCorner = NULL; + double *centre = NULL; + double sR = 0; + double sC = 0; + double eR = 0; + double eC = 0; + double *coefficients = NULL; + double *bbox = NULL; + double *weights = NULL; + + //Check input output argument + checkInputArgument(pvApiCtx, 2, 2); + checkOutputArgument(pvApiCtx, 5, 5); + + //Get variable address of the first input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfBbox, &colsOfBbox, &bbox); + if(sciErr.iErr || colsOfBbox != 4 || rowsOfBbox == 0) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the second input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfWeights, &colsOfWeights, &weights); + if(sciErr.iErr || colsOfWeights == 0 || rowsOfWeights != 1 || colsOfWeights != rowsOfBbox) + { + printError(&sciErr, 0); + return 0; + } + + //Creating list to storing bouding boxes + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, rowsOfBbox, colsOfBbox, bbox); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Creating list to store weights + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 2, piAddr, 1, rowsOfWeights, colsOfWeights, weights); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Computing filter size + filterSize = (double*)malloc(sizeof(double) * 2); + hlSize = (double*)malloc(sizeof(double) * 2); + ulCorner = (double*)malloc(sizeof(double) * 2); + lrCorner = (double*)malloc(sizeof(double) * 2); + + filterSize[0] = filterSize[1] = 0; + for( i = 0 ; i < rowsOfBbox; i++) + { + hlSize[0] = bbox[3 * rowsOfBbox + i]; + hlSize[1] = bbox[2 * rowsOfBbox + i]; + + ulCorner[0] = bbox[1 * rowsOfBbox + i]; + ulCorner[1] = bbox[0 * rowsOfBbox + i]; + + lrCorner[0] = ulCorner[0] + hlSize[0] - 1; + lrCorner[1] = ulCorner[1] + hlSize[1] - 1; + + if(filterSize[0] < lrCorner[0]) + filterSize[0] = lrCorner[0]; + if(filterSize[1] < lrCorner[1]) + filterSize[1] = lrCorner[1]; + } + + //Computing coefficient matrix + coefficients = (double*)malloc(sizeof(double) * int(filterSize[0]) * int(filterSize[1])); + for( i = 0; i < int(filterSize[0]) * int(filterSize[1]); i++) + coefficients[i] = 0; + + for( i = 0; i < rowsOfBbox; i++) + { + sR = bbox[1 * rowsOfBbox + i]; + sC = bbox[0 * rowsOfBbox + i]; + eR = sR + bbox[3 * rowsOfBbox + i] - 1; + eC = sC + bbox[2 * rowsOfBbox + i] - 1; + + for( j = sR - 1; j < eR; j++) + for( k = sC - 1; k < eC; k++) + coefficients[k * int(filterSize[0]) + j] += weights[i]; + } + + //Computing centre of filter + centre = (double*)malloc(sizeof(double) * 2); + centre[0] = ceil(filterSize[0]/2.0); + centre[1] = ceil(filterSize[1]/2.0); + + //Storing coefficient matrix in list + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 3, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 3, piAddr, 1, int(filterSize[0]), int(filterSize[1]), coefficients); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Storing centre of filter in list + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 4, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 4, piAddr, 1, 1, 2, centre); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Storing size of filter in list + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 5, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 5, piAddr, 1, 1, 2, filterSize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Return output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx) + 3; + AssignOutputVariable(pvApiCtx, 4) = nbInputArgument(pvApiCtx) + 4; + AssignOutputVariable(pvApiCtx, 5) = nbInputArgument(pvApiCtx) + 5; + ReturnArguments(pvApiCtx); + + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_isEpipoleInImage.cpp b/sci_gateway1/cpp/opencv_isEpipoleInImage.cpp new file mode 100644 index 0000000..8663032 --- /dev/null +++ b/sci_gateway1/cpp/opencv_isEpipoleInImage.cpp @@ -0,0 +1,164 @@ +/******************************************************** +Author: Suraj Prakash +[isepi, epipole] = isEpipoleInImage(fundamental_matrix, imagesize) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_isEpipoleInImage(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j, n = 0; + + int iRows = 0; + int iCols = 0; + + int *piLen = NULL; + int *piAddr = NULL; + int *piAddr2 = NULL; + + double *pdblReal = NULL; + double *imagesize = NULL; + + int isepi = 0; + double *epipole = NULL; + double inf = 1e17; + /// take the fundamental matrix + Mat fundamental_matrix(3, 3, CV_64F); + + epipole = (double*)malloc(sizeof(double*) * 2); + memset(epipole, 0, 2); + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows != 3 and iCols != 3){ + Scierror(999, "Incorrect dimension of martrix for argument\n"); + return 0; + } + + for(i = 0; i < 3; ++i) + for(j = 0; j < 3; ++j) + fundamental_matrix.at(i, j) = pdblReal[i + j * 3]; + + + /// Get image size + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows != 1 and iCols != 2){ + Scierror(999, "Invalid array for imagesize provided\n"); + return 0; + } + + imagesize = (double*)malloc(sizeof(double) * 2); + imagesize[0] = pdblReal[0]; + imagesize[1] = pdblReal[1]; + + /// compute the SVD of the matrix provided + Mat w, u, vt; + SVD::compute(fundamental_matrix, w, u, vt); + + transpose(vt, vt); + + /// getting the last column of the vt + Mat epipoleHmg(1, 3, CV_64F); + + epipoleHmg.at(0,0) = vt.at(0, 2); + epipoleHmg.at(0,1) = vt.at(1, 2); + epipoleHmg.at(0,2) = vt.at(2, 2); + + + if(epipoleHmg.at(0,2) != 0){ + /// location of epipolecl + + epipole[0] = epipoleHmg.at(0, 0) / epipoleHmg.at(0, 2); + epipole[1] = epipoleHmg.at(0, 1) / epipoleHmg.at(0, 2); + + double *imageOrigin = NULL; + imageOrigin = (double*)malloc(sizeof(double) * 2); + memset(imageOrigin, 0.5, 2); + + double *imageEnd = NULL; + imageEnd = (double*)malloc(sizeof(double) * 2); + memset(imageEnd, 0, 2); + + /// imageend = imageorigin + imagesize(::-1) + imageEnd[0] = imageOrigin[0] + imagesize[1]; + imageEnd[1] = imageOrigin[1] + imagesize[0]; + + if(epipole[0] >= imageOrigin[0] and epipole[1] >= imageOrigin[1] and epipole[0] <= imageEnd[0] and epipole[1] <= imageEnd[1]) + isepi = 1; + else + isepi = 0; + } + + else{ + epipole[0] = epipoleHmg.at(0, 0) > 0 ? inf : -inf; + epipole[1] = epipoleHmg.at(0, 1) > 0 ? inf : -inf; + } + + + + + + int iRet = createScalarBoolean(pvApiCtx, nbInputArgument(pvApiCtx) + 1, isepi); + if(iRet){ + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2, 1, 2, epipole); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_isfilter.cpp b/sci_gateway1/cpp/opencv_isfilter.cpp new file mode 100644 index 0000000..963ae0a --- /dev/null +++ b/sci_gateway1/cpp/opencv_isfilter.cpp @@ -0,0 +1,237 @@ +/********************************************************************************* +*Author : Kevin George +* +*-> To execute, isfilter("Data",..,"Size",..) +* +* +*********************************************************************************/ +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + // # include "../common.cpp" + + int opencv_isfilter(char *fname, unsigned long fname_len) + { + //-> Error Management variables + SciErr sciErr; + int intErr=0; + + //-> Address of Various Arguments + int *piAddr = NULL; + + //-> Local variables + double size; + double *data = NULL; + + Mat s; + Mat u; + Mat v; + + int num_InputArgs; //-> gives total number of arguments + int iRows, iCols; + int *piLen = NULL; + char **pstData = NULL; //-> why double pointer?? and what is it + char *currentArg = NULL; //-> Stores current string representing 'name' of name,value pair arguments + bool *providedArgs = NULL; //-> Used to check that optional argument is not entered more than once + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + //-> Checks number of input and output arguments + CheckInputArgument(pvApiCtx, 4 , 4); + CheckOutputArgument(pvApiCtx, 1, 1); + + //-> Count number of input arguments + num_InputArgs = *getNbInputArgument(pvApiCtx); + + providedArgs = (bool*) malloc(sizeof(bool) * 2); + +//***************************************************** Getting Input Arguments ************************************************************* + for(int iter = 1; iter<= num_InputArgs; iter++) + { + //-> Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Extracting name of next argument takes three calls to getMatrixOfString + //-> First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + //-> Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + //-> Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + + + +//****************************************************** Name,Value - Data ***************************************************************** + + if(strcmp(currentArg, "Data")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &data); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + /*if(iRows*iCols!=) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } */ + + providedArgs[0] = 1; + } + + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + } + +//****************************************************** Name,Value - Size ***************************************************************** + else if(strcmp(currentArg, "Size")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr, &size); + if(intErr) + { + return intErr; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if( size < 0) + { + Scierror(999," Invalid Value for NumPyramidLevels. Please enter a non negative Double value\\n"); + return 0; + } + providedArgs[1] = 1; + } + + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + } + + } +//***************************************************** Actual Processing ************************************************************* + + int size2 = int(size); + float *kdata = new float[size2*size2]; + + for(int i = 0;i(i,j) != 0) + count++; + } + } + + if (count == 1) + sciprint("\nFilter is seperale\n"); + + else + sciprint("\nFilter is not seperale\n"); + + ReturnArguments(pvApiCtx); + return 0; + } + + } \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_laplacian.cpp b/sci_gateway1/cpp/opencv_laplacian.cpp new file mode 100644 index 0000000..46ce129 --- /dev/null +++ b/sci_gateway1/cpp/opencv_laplacian.cpp @@ -0,0 +1,156 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + return_image = laplacian(input_image , depth , ksize , scale, delta); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_laplacian(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **ddepth = NULL; + int i,j,k; + double ksize,scale,delta; + + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of ksize + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&ksize); + if(intErr) + return intErr; + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&scale); + if(intErr) + return intErr; + + //for value of delta + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&delta); + if(intErr) + return intErr; + + + Mat new_image(image.rows,image.cols,CV_8UC3); + if((int)ksize!=1 && (int)ksize!=3 && (int)ksize!=5 && (int)ksize!=7) + { + sciprint("Value of 1,3,5,7 Required for ksize. Default value of 3 was used instead"); + ksize=3; + } + + if(strcmp(ddepth[0],"CV_8U")==0) + Laplacian(image,new_image,CV_8U,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_16U")==0) + Laplacian(image,new_image,CV_16U,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_16S")==0) + Laplacian(image,new_image,CV_16S,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_32F")==0) + Laplacian(image,new_image,CV_32F,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_64F")==0) + Laplacian(image,new_image,CV_64F,ksize,scale,delta); + else + { + Laplacian(image,new_image,CV_8U,ksize,scale,delta); + sciprint("Wrong depth given,using CV_8U instead"); + } + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_line.cpp b/sci_gateway1/cpp/opencv_line.cpp new file mode 100644 index 0000000..c047381 --- /dev/null +++ b/sci_gateway1/cpp/opencv_line.cpp @@ -0,0 +1,211 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + return_image = line(image , x1 , y1 , x2,y2,r_value,g_value,b_value,thickness,linetype,shift); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_line(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11=NULL; + int i,j,k; + double thickness=1,linetype=8,shift=0 ,x1,y1,x2,y2,r_value,g_value,b_value; + + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 11); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2 ,&x1); + if(intErr) + return intErr; + + //for value of y coordinate of first point + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&y1); + if(intErr) + return intErr; + + //for value of x coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4 ,&x2); + if(intErr) + return intErr; + + ///for value of y coordinate of second point + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5 ,&y2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&r_value); + if(intErr) + return intErr; + + // for G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7 ,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&b_value); + if(intErr) + return intErr; + + // for thickness of line default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr9,&thickness); + if(intErr) + return intErr; + + //for line type of line default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr10 ,&linetype); + if(intErr) + return intErr; + + // for shift in line defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Syntax: sciErr = getMatrixOfInteger8(pvApiCtx, piAddr8, &iRows8, &iCols8, &pcData); + intErr = getScalarDouble(pvApiCtx, piAddr11 ,&shift); + if(intErr) + return intErr; + + + //temporary pt variable, to use in function as centre + Point pt1(x1,y1); + Point pt2(x2,y2); + /// checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead"); + } + +//calling the opencv function + line( src, pt1, pt2, Scalar(b_value,g_value,r_value), thickness, linetype, shift); + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway1/cpp/opencv_matchFeatures.cpp b/sci_gateway1/cpp/opencv_matchFeatures.cpp new file mode 100644 index 0000000..c8a35fd --- /dev/null +++ b/sci_gateway1/cpp/opencv_matchFeatures.cpp @@ -0,0 +1,490 @@ +/******************************************************************************************************************************************************************************************************** +* Author: Umang Agrawal * +* Code: matchFeatures.cpp * +* Function Format: [ index_Pairs_of_Matched_fetaures Metric ] = matchFeatures( feature_Vector_1, feature_Vector_2, Optional Arguments ) * +* Optional Arguments: Name Value * +* 'Method' [ 'Exhaustive' : 'Brute Force Matching', 'Approximate' : FANN Based Matching' ] * +* 'Metric' [ 'SSD', 'SAD', 'Hamming', 'Hamming_2' ] * +* 'Unique' [ Boolean True or False ] * +* 'MatchThreshold' [ Percentage: 0 - 100 ] * +********************************************************************************************************************************************************************************************************/ +#include +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/imgproc/imgproc.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_matchFeatures(char *fname, unsigned long fname_len) + { + // Error Handling + SciErr sciErr; + + // Address of Various Scilab API + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + + // Number of input and output Arguments + int inputArguments = 0; + + int Rows_1,Cols_1; + int Rows_2,Cols_2; + + double *feature_1 = NULL; + double *feature_2 = NULL; + + int iRows, iCols; + int *pilen = NULL; + char **arg = NULL; + char **method = NULL; + char **metric = NULL; + double unique = 0; + double thresh = 10; + + int count_method = 0,count_metric = 0, count_unique = 0, count_thresh = 0; + + double max_dist = 0, min_dist; + double *indexPairs = NULL; + double *matchMetric = NULL; + + vector matches; + vector valid_m; + + // Checks on Number of Input and Output Arguments + CheckInputArgument(pvApiCtx, 2, 10); + CheckOutputArgument(pvApiCtx, 1, 2); + + inputArguments = *getNbInputArgument(pvApiCtx); + + if( inputArguments%2 == 0 ) + { + //Feature Matrix 1 + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !isVarMatrixType(pvApiCtx, piAddr1)) + { + Scierror(999,"Expecting a Matrix of MxN order containing the Binary Feature Set of Image 1 (Query Image)\n"); + return 0; + } + if( !isDoubleType(pvApiCtx, piAddr1)) + { + Scierror(999,"Feature Matrix should be of Double Type\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &Rows_1, &Cols_1, &feature_1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Feature Matrix 2 + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !isVarMatrixType(pvApiCtx, piAddr2)) + { + Scierror(999,"Expecting a Matrix of MxN order containing the Binary Feature Set of Image 1 (Query Image)\n"); + return 0; + } + if( !isDoubleType(pvApiCtx, piAddr2)) + { + Scierror(999,"Feature Matrix should be of Double Type\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &Rows_2, &Cols_2, &feature_2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + Scierror(999,"Required Arguments are Missing\n"); + return 0; + } + + for( int i=3; i<=inputArguments; i++) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr3)) + { + Scierror(999, "%s: Wrong type of argument #%d. A string is expected.\n", fname, 1); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + arg = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + arg[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, pilen, arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(arg); + return 0; + } + + if(strcmp(arg[0],"Method") == 0) + { + if(count_method != 0) + { + Scierror(999,"Method argument has been called twice.\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr4)) + { + Scierror(999, "%s: Wrong type of value for Method Argument. A string is expected.\n"); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + method = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + method[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, pilen, method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(method); + return 0; + } + i++; + count_method += 1; + } + else if(strcmp(arg[0],"Metric") == 0) + { + if(count_metric != 0) + { + Scierror(999,"Metric argument has been called twice.\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr4)) + { + Scierror(999, "%s: Wrong type of Value for Metric Argument. A string is expected.\n"); + return 0; + } + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + metric = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + metric[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, pilen, metric); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(metric); + return 0; + } + i++; + count_metric += 1; + } + else if(strcmp(arg[0],"Unique") == 0) + { + if(count_unique != 0) + { + Scierror(999,"Unique argument has been called twice.\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !(isDoubleType(pvApiCtx, piAddr4)) ) + { + Scierror(999,"Unique Value must be a logic scalar\n"); + return 0; + } + if(getScalarDouble(pvApiCtx, piAddr4, &unique)) + { + Scierror(999,"Cannot Read Upright Value\n"); + return 0; + } + if( !(int(unique)==0 || int(unique)==1) ) + { + Scierror(999,"Unique Value must be a logic scalar\n"); + return 0; + } + i++; + count_unique += 1; + } + else if(strcmp(arg[0],"MatchThreshold") == 0) + { + if(count_thresh != 0) + { + Scierror(999,"Match Threshold argument has been called twice.\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check for Argument type + if( !(isDoubleType(pvApiCtx, piAddr4)) ) + { + Scierror(999,"Match Threshold Value must be a scalar Double\n"); + return 0; + } + if(getScalarDouble(pvApiCtx, piAddr4, &thresh)) + { + Scierror(999,"Cannot Read Threshold Value\n"); + return 0; + } + if( !(thresh>0 && thresh<=100) ) + { + Scierror(999,"Threshold Value must be in the range of 1 to 100\n"); + return 0; + } + i++; + count_thresh += 1; + } + else + { + Scierror(999, "Invalid Argument Name. Provide Valid Arguments\n"); + return 0; + } + } + + if( count_metric == 0 ) + { + metric = (char**)malloc(sizeof(char*) * 1 * 1); + metric[0] = (char*)malloc(sizeof(char) * 4); + strcpy(metric[0], "SSD"); + } + + if( count_method == 0) + { + method = (char**)malloc(sizeof(char*) * 1 * 1); + method[0] = (char*)malloc(sizeof(char) * 11); + strcpy(method[0], "Exhaustive"); + } + + + if( strcmp(method[0],"Exhaustive") == 0) + { + Mat descriptor_1(Rows_1, Cols_1, CV_8UC1); + Mat descriptor_2(Rows_2, Cols_2, CV_8UC1); + + for(int i=0; i(i,j) = int(feature_1[j*Rows_1 + i]); + } + + for(int i=0; i(i,j) = int(feature_2[j*Rows_2 + i]); + } + if( strcmp(metric[0],"SSD") == 0) + { + BFMatcher matcher(NORM_L2, int(unique)); + matcher.match(descriptor_1, descriptor_2, matches); + } + else if( strcmp(metric[0],"SAD") == 0) + { + BFMatcher matcher(NORM_L1, int(unique)); + matcher.match(descriptor_1, descriptor_2, matches); + } + else if( strcmp(metric[0],"Hamming") == 0) + { + BFMatcher matcher(NORM_HAMMING, int(unique)); + matcher.match(descriptor_1, descriptor_2, matches); + } + else if( strcmp(metric[0],"Hamming_2") == 0) + { + BFMatcher matcher(NORM_HAMMING2, int(unique)); + matcher.match(descriptor_1, descriptor_2, matches); + } + else + { + Scierror(999,"Enter a Valid value for Metric Argument\n"); + return 0; + } + } + else if( strcmp(method[0],"Approximate") == 0) + { + Mat descriptor_1(Rows_1, Cols_1, CV_32FC1); + Mat descriptor_2(Rows_2, Cols_2, CV_32FC1); + + for(int i=0; i(i,j) = float(feature_1[j*Rows_1 + i]); + } + + for(int i=0; i(i,j) = float(feature_2[j*Rows_2 + i]); + } + FlannBasedMatcher matcher; + matcher.match(descriptor_1,descriptor_2,matches); + } + else + { + Scierror(999,"Enter a Valid value for Method Argument\n"); + return 0; + } + + for( int i=0; i max_dist ) + max_dist = dist; + } + min_dist = (thresh * max_dist)/100; + + for( int i=0; i= min_dist) + valid_m.push_back(matches[i]); + } + indexPairs = (double*)malloc(sizeof(double) * valid_m.size() * 2); + for( int i=0; i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_medianblur(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double ksize; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + //for value of ksize + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &ksize); + if(intErr) + { + return intErr; + } + + Mat new_image(image.rows,image.cols,CV_8UC3); + + //checking input parameters + if((int)ksize != 1 && (int)ksize != 3 && (int)ksize != 5 && (int)ksize != 7) + { + sciprint("Value of 1,3,5,7 Required for ksize. Default value of 3 was used instead"); + ksize = 3; + } + medianBlur(image,new_image,ksize); + + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_montage.cpp b/sci_gateway1/cpp/opencv_montage.cpp new file mode 100644 index 0000000..fcb66d7 --- /dev/null +++ b/sci_gateway1/cpp/opencv_montage.cpp @@ -0,0 +1,216 @@ +/*************************************************** +Author : Sukul Bagai +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + +int opencv_montage(char *fname, unsigned long fname_len) +{ + SciErr sciErr; + int iLen = 0; + //variable info + int iRows = 0; + int iCols = 0; + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddrChild = NULL; + int *piAddrNew = NULL; + int piRows = 0; + int piCols = 0; + int *piLen = NULL; + char **pstData = NULL; + int **pstData1 = NULL; + int i,j,k=0,iItem=0,height,width,intErr=0; + double new_rows,new_cols; + unsigned char *pstDataR = NULL; + unsigned char *pstDataG = NULL; + unsigned char *pstDataB = NULL; + vector image; + Mat tempimage; + + + //Checking number of input and output arguments (enviromnet variable, min arguments, max arguments) + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx,piAddr,&iItem); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int x=0;x(i,j)[2]=pstDataR[k]; + tempimage.at(i,j)[1]=pstDataG[k]; + tempimage.at(i,j)[0]=pstDataB[k++]; + } + } + image.push_back(tempimage); + } + + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &new_rows); + if(intErr) + return intErr; + + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &new_cols); + if(intErr) + return intErr; + + for(i=1;i= iItem) || ((new_rows*new_cols) < iItem) ) + { + sciprint("Given Row and Column Dimensions do not match with number of images in list. Montage cannot be created. Please check arguments\n"); + return 0; + } + height=new_rows*image[0].rows; + width=new_cols*image[0].cols; + + Mat new_image = Mat::zeros(height,width,CV_8UC3); + + k=0; + for(int x=0;x=iItem) + { + new_image.at(image[0].rows*x+i,image[0].cols*y+j)[2]=black_image.at(i,j)[2]; + new_image.at(image[0].rows*x+i,image[0].cols*y+j)[1]=black_image.at(i,j)[1]; + new_image.at(image[0].rows*x+i,image[0].cols*y+j)[0]=black_image.at(i,j)[0]; + }*/ + if(k(image[k].rows*x+i,image[k].cols*y+j)[2]=image[k].at(i,j)[2]; + new_image.at(image[k].rows*x+i,image[k].cols*y+j)[1]=image[k].at(i,j)[1]; + new_image.at(image[k].rows*x+i,image[k].cols*y+j)[0]=image[k].at(i,j)[0]; + } + } + } + k++; + } + } + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + + return 0; +} +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_morphologyEx.cpp b/sci_gateway1/cpp/opencv_morphologyEx.cpp new file mode 100644 index 0000000..cc1666a --- /dev/null +++ b/sci_gateway1/cpp/opencv_morphologyEx.cpp @@ -0,0 +1,232 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = morphologyEx(input_image,"",kernel_matrix,anchor_x,anchor_y,iterations,""); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_morphologyEx(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **op = NULL; + char **borderType = NULL; + int i,j,k; + double *kernel,anchorX,anchorY,iterations,border; + + //checking input argument + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for op + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + op = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + op[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, op); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for kernel matrix + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&kernel); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n=iRows; + double kernelArray[n][n]; + //assigning values to actual kernelMatrix + for(i=0;i= n || anchorY >=n) + { + sciprint("Invalid anchor point given. Centre point (-1,-1) was used instead"); + anchorY = -1; + anchorX = -1; + } + Mat new_image(image.rows,image.cols,image.type()); + Point pt(anchorX,anchorY); + + int actualop; + + if(strcmp(op[0],"MORPH_OPEN")==0) + actualop=MORPH_OPEN; + else if(strcmp(op[0],"MORPH_CLOSE")==0) + actualop=MORPH_CLOSE; + else if(strcmp(op[0],"MORPH_GRADIENT")==0) + actualop=MORPH_GRADIENT; + else if(strcmp(op[0],"MORPH_TOPHAT")==0) + actualop=MORPH_TOPHAT; + else if(strcmp(op[0],"MORPH_BLACKHAT")==0) + actualop=MORPH_BLACKHAT; + else + { + sciprint("Invalid type %s used. MORPH_OPEN was used instead by default",op[0]); + actualop = MORPH_OPEN; + } + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + //applying function + filter2D(image,new_image,actualop,kernelMatrix,pt,iterations,border); + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_ocr.cpp b/sci_gateway1/cpp/opencv_ocr.cpp new file mode 100644 index 0000000..dc1dc6d --- /dev/null +++ b/sci_gateway1/cpp/opencv_ocr.cpp @@ -0,0 +1,175 @@ +/*************************************************** +Author : Rohit Suri +TODO : Extract confidences for each character + : Calculate Position of words +***************************************************/ +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include +#include +using namespace cv; +using namespace std; +using namespace tesseract; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + /*Calling syntax: ocr(I) */ + + int opencv_ocr(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + Mat sourceImage; + TessBaseAPI tesseract; + int *wordConfidences = NULL; + int wordCount = 0, characterCount = 0, count=0, coordinate; + string word=""; + char *text = NULL; + char **words = NULL; + char *boxInformation = NULL; + int *characterBoundingBoxes = NULL; + int characterBoundingBoxesPos = 0; + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 4); + + //------Get input arguments------// + retrieveImage(sourceImage, 1); + + //------Actual processing------// + + tesseract.Init(NULL, "eng", OEM_TESSERACT_ONLY); + tesseract.SetPageSegMode(PSM_SINGLE_BLOCK); + tesseract.SetVariable("tessedit_char_whitelist","0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*()-_=+[]{}:'\";\|,.<>/"); + tesseract.SetImage(sourceImage.data, sourceImage.cols, sourceImage.rows, sourceImage.elemSize(), sourceImage.step); + wordConfidences = tesseract.AllWordConfidences(); + boxInformation = tesseract.GetBoxText(0); + for(int iter = 0; ;iter++) + { + if(wordConfidences[iter]!=-1) + { + wordCount++; + } + else + { + break; + } + } + + words = (char**) malloc(sizeof(char*)*wordCount); + text = tesseract.GetUTF8Text(); + for(int iter = 0; count < wordCount; iter++) + { + + if(count == wordCount) + { + break; + } + if(text[iter]==' ' || text[iter]=='\n') + { + words[count] = (char*) malloc(sizeof(char)*word.length()+1); + for(int char_iter=0; char_iter Syntax : opticalFlowFarneback("Filename",..,"NumPyramidLevels,..,"PyramidScale",.., + "Num_Iteration",..,"NeighborhoodSize",..,"FilterSize",..) + +-> First argument "FileName" is necessary,rest are optional +*******************************************************************************************************/ +#include +#include +#include +#include +#include +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + // # include "../common.cpp" + + //-> Name,Value Pair Variables + char *fileName = NULL; //-> Stores fileName + int NumPyramidLevels; + int NeighborhoodSize; + int NumIteration; + int FilterSize; + double PyramidScale; + bool objectStatus3 = false; + + int opencv_opticalFlowFarneback ( char *fname, unsigned long fname_len) + { + + //-> Error Management variables + SciErr sciErr; + int intErr=0; + + //-> Mat containers for images + Mat image_1; + Mat image_2; + + //-> Address of Various Arguments + int *piAddr = NULL; + + //-> Local variables + int num_InputArgs; //-> gives total number of arguments + int iRows, iCols; + int *piLen = NULL; + char **pstData = NULL; //-> why double pointer?? and what is it + char *currentArg = NULL; //-> Stores current string representing 'name' of name,value pair arguments + bool *providedArgs = NULL; //-> Used to check that optional argument is not entered more than once + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + //-> Checks number of input and output arguments + CheckInputArgument(pvApiCtx, 0 , 5); + CheckOutputArgument(pvApiCtx, 1, 5); + + //-> Count number of input arguments + num_InputArgs = *getNbInputArgument(pvApiCtx); + + providedArgs = (bool*) malloc(sizeof(bool) * 6); + +//************************************************ Retrieval of Name, Value Argument Pair ************************************************* + for(int iter = 1; iter<= num_InputArgs; iter++) + { + //-> Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Extracting name of next argument takes three calls to getMatrixOfString + //-> First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + //-> Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + //-> Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + +//****************************************************** Name,Value - NumPyramidLevels ***************************************************************** + + if(strcmp(currentArg, "FileName")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Extracting name of next argument takes three calls to getMatrixOfString + //-> First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + //-> Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + //-> Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + fileName = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "NumPyramidLevels")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &NumPyramidLevels); + if(intErr) + { + return intErr; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if( NumPyramidLevels < 0) + { + Scierror(999," Invalid Value for NumPyramidLevels. Please enter a non negative Double value\\n"); + return 0; + } + + providedArgs[1] = 1; + } + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } +//****************************************************** Name,Value - PyramidScale ***************************************************************** + + else if(strcmp(currentArg, "PyramidScale")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddr)) + { + Scierror(999," Invalid Value for PyramidScale. Please enter a non negative Double value\\n"); + return 0; + } + getScalarDouble(pvApiCtx, piAddr, &PyramidScale); + + //-> Checking if values are in proper range. Same for all optional arguments + if( PyramidScale < 0) + { + Scierror(999," Invalid Value for NumPyramidLevels. Please enter a non negative Double value\\n"); + return 0; + } + + providedArgs[2] = 1; + } + + else if(providedArgs[2]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + } + +//****************************************************** Name,Value - NumIteration ***************************************************************** + + else if(strcmp(currentArg, "NumIteration")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &NumIteration); + if(intErr) + { + return intErr; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if( NumIteration < 0) + { + Scierror(999," Invalid Value for NumPyramidLevels. Please enter a non negative Double value\\n"); + return 0; + } + + providedArgs[3] = 1; + } + + else if(providedArgs[3]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } +//****************************************************** Name,Value - NeighborhoodSize ***************************************************************** + + else if(strcmp(currentArg, "NeighborhoodSize")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[4]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &NeighborhoodSize); + if(intErr) + { + return intErr; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if( NeighborhoodSize < 0) + { + Scierror(999," Invalid Value for size of pixel Neighborhood. Please enter a non negative Double value\\n"); + return 0; + } + + providedArgs[4] = 1; + } + + else if(providedArgs[4]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } +//****************************************************** Name,Value - FilterSize ***************************************************************** + + else if(strcmp(currentArg, "FilterSize")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[5]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarInteger32(pvApiCtx, piAddr, &FilterSize); + if(intErr) + { + return intErr; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if( FilterSize < 0) + { + Scierror(999," Invalid Value for size of pixel Neighborhood. Please enter a non negative Double value\\n"); + return 0; + } + + providedArgs[5] = 1; + } + + else if(providedArgs[5]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + else + { + Scierror(999, "Incorrect Name type. Please re-enter the correct Name-Value pair arguments\n"); + return 0; + } + + }//-> Braces for ending of long for loop + +//******************************************************** Giving Default Arguments ******************************************************************* + + if(providedArgs[0] == 0) + { + NumPyramidLevels = 3; + } + + + if(providedArgs[1] == 0) + { + PyramidScale = 0.5; + } + + if(providedArgs[2] == 0) + { + NumIteration = 3; + } + + if(providedArgs[3] == 0) + { + NeighborhoodSize = 5; + } + + if(providedArgs[4] == 0) + { + FilterSize = 15; + } +//**************************************************************Actual Processing******************************************************************************* + + objectStatus3 = true; + ReturnArguments(pvApiCtx); + return 0; + } + + int opencv_readopticalFlowFarneback(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int *piAddr = NULL; + int *outList = NULL; + unsigned char *red = NULL; + unsigned char *green = NULL; + unsigned char *blue = NULL; + + if(!objectStatus3) + { + Scierror(999,"Set up Foreground Detection properties through ForegroundDetection() first.\n"); + return 0; + } + + Mat image_prev, image_next, flow, GetImg, cflow; + int s = 2; + //char fileName[100] = "ped.avi"; + //VideoCapture stream1(fileName); + + //if(!(stream1.read(GetImg))) //get one frame from video + // return 0; + + retrieveImage(GetImg,1); + + resize(GetImg, image_prev, Size(GetImg.size().width/s, GetImg.size().height/s) ); + cvtColor(image_prev, image_prev, CV_BGR2GRAY); + + //namedWindow( "prvs", WINDOW_NORMAL); + //namedWindow( "next", WINDOW_NORMAL); + namedWindow( "OpticalFlowFarneback", WINDOW_NORMAL); + + //while(true) + //{ + //if(!(stream1.read(GetImg))) //get one frame from video + // break; + + retrieveImage(GetImg,2); + //-> Resize + resize(GetImg, image_next, Size(GetImg.size().width/s, GetImg.size().height/s) ); + cvtColor(image_next, image_next, CV_BGR2GRAY); + + calcOpticalFlowFarneback(image_prev,image_next, flow, 0.5, 3, 15, 3, 5, 1.2, 0); + + cvtColor(image_prev, cflow, CV_GRAY2BGR); + + //drawOptFlowMap(flow, cflow, 10, CV_RGB(0, 255, 0)); + int step = 10; + for(int y = 0; y < cflow.rows; y += step) + { + for(int x = 0; x < cflow.cols; x += step) + { + const Point2f& fxy = flow.at< Point2f>(y, x); + line(cflow, Point(x,y), Point(cvRound(x+fxy.x), cvRound(y+fxy.y)), + CV_RGB(0, 255, 0) ); + //circle(cflowmap, Point(cvRound(x+fxy.x), cvRound(y+fxy.y)), 1, color, -1); + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + red = (unsigned char *)malloc(sizeof(unsigned char)*cflow.rows*cflow.cols); + green = (unsigned char *)malloc(sizeof(unsigned char)*cflow.rows*cflow.cols); + blue = (unsigned char *)malloc(sizeof(unsigned char)*cflow.rows*cflow.cols); + + for(int k=0;k(k, p); + red[k+cflow.rows*p]=intensity.val[2]; + green[k+cflow.rows*p]=intensity.val[1]; + blue[k+cflow.rows*p]=intensity.val[0]; + } + } + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, cflow.rows, cflow.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 2, cflow.rows, cflow.cols, green); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 3, cflow.rows, cflow.cols, blue); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + free(green); + free(blue); + + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + + //imshow("OpticalFlowFarneback", cflow); + + //->Display + //imshow("prvs", image_prev); + //imshow("next", image_next); + + //if (waitKey(5) >= 0) + // break; + + // image_prev = image_next.clone(); + // sleep(0.5); + //} + + + } + + } \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_partition.cpp b/sci_gateway1/cpp/opencv_partition.cpp new file mode 100644 index 0000000..0c8d37f --- /dev/null +++ b/sci_gateway1/cpp/opencv_partition.cpp @@ -0,0 +1,488 @@ +/*************************************************** +Author : Rohit Suri +***************************************************/ +#include +#include +#include +#include +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + + /* Calling syntax: [set1,set2,...,setN] = partition(imgSet,groupSizes) + [set1,set2,...,setN] = partition(imgSet,groupPercentages) + [set1,set2,...,setN] = partition(___,method) */ + + double findSum(double *array, int n) + { + double sum = 0; + for(int iter=0; iter0 && groupSizesOrPercentages[0]<1) + { + sizeProvided = 0; + } + else if(groupSizesOrPercentages[0]>=1) + { + sizeProvided = 1; + } + else + { + Scierror(999, "Error: Please enter positive values groupSizes argument.\n"); + return 0; + } + for(int iter = 0; iter=1)) + { + Scierror(999, "Error: Please enter proper groupPercentages argument. Values should lie between 0-1.\n"); + return 0; + } + groupSizesOrPercentagesSum+=groupSizesOrPercentages[iter]; + } + if(!sizeProvided && groupSizesOrPercentagesSum>1) + { + Scierror(999, "Error: Sum of values of groupPercentages should be less than or equal to one. \n"); + return 0; + } + if(sizeProvided) + { + for(int iter = 0; iter= 0.5) + { + temp = (int)temp + 1; + } + else + { + temp = (int)temp; + } + } + if(tempCount[iter2]==0) + { + countOutput[iter][iter2] = 0; + } + else if(tempCount[iter2]-temp<=0) + { + countOutput[iter][iter2] = tempCount[iter2]; + tempCount[iter2] = 0; + zeroWarning = 1; + } + else + { + if(temp==0) + { + zeroWarning = 1; + } + countOutput[iter][iter2] = temp; + tempCount[iter2] -= temp; + } + } + } + countOutput[partitionCount-1] = (int*) malloc(sizeof(int) * descriptionCount); + for(int iter = 0; iterimage + +Optional arguments : + +// 2->hitThreshold +// 3->winStride +// 4->padding +// 5->scale +// 6->finalThreshold +// 7->useMeanshiftGrouping + +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_peopleDetector(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j; + int intErr; + int iRows = 0; + int iCols = 0; + int iRows1 = 0; + int iCols1 = 0; + + int *piLen = NULL; + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + + double hitThreshold = 0; + double scale = 1.05; + double *winStride = NULL; + double *padding = NULL; + double finalThreshold = 2.0; + int useMeanshiftGrouping = 0; + + double *bboxes = NULL; + + + /// for the Hog_SVM_Detector + HOGDescriptor hog; + + /// found -> for the found rectangles + /// found_filtered -> the size of the rectangles found is usually greater so, the rectangles are filtered out + /// bboxes in matlab correspond to found_filtered in this code + /// answer to hold the modified rectangle after filtering + vector< Rect > found, found_filtered, answer; + + /// checking input argument + // 1->image + // 2->hitThreshold + // 3->winStride + // 4->padding + // 5->scale + // 6->finalThreshold + // 7->useMeanshiftGrouping + + + CheckInputArgument(pvApiCtx, 1, 13); + CheckOutputArgument(pvApiCtx, 1, 1); + + /// retrieve image + Mat image; + retrieveImage(image, 1); + + /// Train hog detector for people + /// Default training is 48 * 96 people images + /// can be used for detection of larger instances + + + /// For the optional arguments + int nbInputArguments = *getNbInputArgument(pvApiCtx); + + providedArgs = (bool*) malloc(sizeof(bool) * 5); + memset(providedArgs, 0, 5); + + winStride = (double*)malloc(sizeof(double) * 2); + winStride[0] = 8; + winStride[1] = 8; + + padding = (double*)malloc(sizeof(double) * 2); + padding[0] = 16; + padding[1] = 16; + + + for(int iter = 2; iter <= nbInputArguments; ++iter){ + + /// Address of next argument + + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int k = 0; k < iRows * iCols; ++k) + { + pstData[k] = (char*) malloc(sizeof(char) * piLen[k] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + + /// getting hit_threshold + if(strcmp(currentArg, "hitThreshold") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &hitThreshold); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(hitThreshold < 0) + { + Scierror(999, "Error: Nonnegative hitThreshold only allowed\n"); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// WindowStride + if(strcmp(currentArg, "winStride") == 0) + { + if(iter+1 <= nbInputArguments && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &winStride); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[1] = 1; + } + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// Padding + if(strcmp(currentArg, "padding") == 0) + { + if(iter+1 <= nbInputArguments and !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &padding); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[2] = 1; + } + else if(providedArgs[2]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// Scalefactor + if(strcmp(currentArg, "scale") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &scale); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(scale < 1.0001) + { + Scierror(999, "Error: Wrong ScaleFactor given. Cannot be less than 1.0001\n"); + + return 0; + } + providedArgs[3] = 1; + } + else if(providedArgs[3]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + + + /// finalThreshold + if(strcmp(currentArg, "finalThreshold") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[4]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &finalThreshold); + if(intErr) + { + return intErr; + } + + if(finalThreshold < 0) + { + Scierror(999, "Incorrect value for argument .\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[4] = 1; + } + else if(providedArgs[4]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// UseMeanshiftGrouping + if(strcmp(currentArg, "useMeanshiftGrouping")==0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[5]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarBoolean(pvApiCtx, piAddr, &useMeanshiftGrouping); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(useMeanshiftGrouping > 1 or useMeanshiftGrouping < 0) + { + Scierror(999, "Incorrect value for argument .\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[5] = 1; + } + else if(providedArgs[5]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + } + + /// End of error check and input get; + + hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector()); + + ///virtual void cv::HOGDescriptor::detectMultiScale + // ( InputArray img, + // std::vector< Rect > & foundLocations, + // double hitThreshold = 0, + // Size winStride = Size(), + // Size padding = Size(), + // double scale = 1.05, + // double finalThreshold = 2.0, + // bool useMeanshiftGrouping = false + // ) + + hog.detectMultiScale(image, found, hitThreshold, Size(winStride[0], winStride[1]), Size(padding[0], padding[0]), scale, finalThreshold, useMeanshiftGrouping); + + /// finding out the rectangles + + for (i = 0; i < found.size(); ++i){ + Rect r = found[i]; + for (j = 0; j < found.size(); ++j) + /// removing same identified people + if (j != i and (r & found[j]) == r) + break; + + /// if no duplicate found + if (j == found.size()) + found_filtered.push_back(r); + } + + /// filtering out the rectangles + for (i=0; i +#include +#include +#include + +using namespace std; +using namespace cv; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_predict(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr extractor = new SurfDescriptorExtractor(1, 4, 2, 1, int(upright)); + BOWImgDescriptorExtractor bowDE(extractor, matcher); + SurfFeatureDetector detector(1, 4, 2, 1, int(upright)); + char *classifierLocation = NULL; + Mat dictionary,features; + double response; + vector keyPoints; + CvSVM svm; + int dictionarySize; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + char **classifierDescription = NULL; + int classifierDescriptionCount; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + Mat input; + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + retrieveImage(input,2); + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + // Extracting object type and checking if type is classifier + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"classifier")==0)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for classifier argument."); + return 0; + } + classifierLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + bagOfFeaturesLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + classifierDescription = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + classifierDescription[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, classifierDescription); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //------Actual processing------// + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + dictionarySize = dictionary.rows; + bowDE.setVocabulary(dictionary); + svm.load(classifierLocation); + detector.detect(input, keyPoints); + bowDE.compute(input, keyPoints, features); + response = svm.predict(features); + //------Create output arguments------// + sciErr = createMatrixOfString(pvApiCtx, nbInputArgument(pvApiCtx)+1, 1, 1, &classifierDescription[(int)response]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_projectPoints.cpp b/sci_gateway1/cpp/opencv_projectPoints.cpp new file mode 100644 index 0000000..d242875 --- /dev/null +++ b/sci_gateway1/cpp/opencv_projectPoints.cpp @@ -0,0 +1,256 @@ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/calib3d/calib3d.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_projectPoints(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iComplex = 0; + int iType = 0; + int *piAddr = NULL; + int i,size,j; + double *worldPoints=NULL,*rVector=NULL,*tVector=NULL,*intrinsicMatrix=NULL,*distCoefficients=NULL; + int wpRows,wpCols,rvRows,rvCols,tvRows,tvCols,imRows,imCols,dcRows,dcCols; + double *points=NULL; + + vector objectPoints; + vector imagePoints; + Mat intrisicMat(3, 3, cv::DataType::type); + Mat rVec(3, 1, cv::DataType::type); + Mat tVec(3, 1, cv::DataType::type); + Mat distCoeffs(5, 1, cv::DataType::type); + + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &wpRows, &wpCols, &worldPoints); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(wpCols!=2) + { + Scierror(999, "%s: Wrong value for input argument: World points must be M*2\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rvRows, &rvCols, &rVector); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(rvCols!=3) + { + + } + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &tvRows, &tvCols, &tVector); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(tvCols!=3) + { + + } + if(rvRows!=tvRows) + { + + } + + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &imRows, &imCols, &intrinsicMatrix); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(imRows!=3 || imCols!=3) + { + + } + + + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &dcRows, &dcCols, &distCoefficients); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(dcRows!=1 || dcCols!=3) + { + + } + + for(i=0;i(0, 0) = intrinsicMatrix[0]; + intrisicMat.at(1, 0) = 0; + intrisicMat.at(2, 0) = 0; + + intrisicMat.at(0, 1) = 0; + intrisicMat.at(1, 1) = intrinsicMatrix[4]; + intrisicMat.at(2, 1) = 0; + + intrisicMat.at(0, 2) = intrinsicMatrix[2]; + intrisicMat.at(1, 2) = intrinsicMatrix[5]; + intrisicMat.at(2, 2) = 1; + + rVec.at(0) = rVector[0]; + rVec.at(1) = rVector[1]; + rVec.at(2) = rVector[2]; + + tVec.at(0) = tVector[0]; + tVec.at(1) = tVector[1]; + tVec.at(2) = tVector[2]; + + distCoeffs.at(0) = distCoefficients[0]; + distCoeffs.at(1) = distCoefficients[1]; + distCoeffs.at(2) = distCoefficients[2]; + distCoeffs.at(3) = distCoefficients[3]; + if(dcCols==5) + { + distCoeffs.at(4) = distCoefficients[4]; + } + + //Actual process + projectPoints(objectPoints, rVec, tVec, intrisicMat, distCoeffs, imagePoints); + size=imagePoints.size(); + points = (double*) malloc(sizeof(double) * size * 2); + for(i = 0 ; i < size ; i++) + { + points[i] = imagePoints[i].x; + points[i+size] = imagePoints[i].y; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, size , 2, points); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway1/cpp/opencv_puttext.cpp b/sci_gateway1/cpp/opencv_puttext.cpp new file mode 100644 index 0000000..97bf62c --- /dev/null +++ b/sci_gateway1/cpp/opencv_puttext.cpp @@ -0,0 +1,311 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + return_image = puttext(image ,x1 ,y1 ,fontscale,r_value,g_value,b_value,text,fontface ,thickness,linetype,bottomleft(0/1)); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_puttext(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piLen2 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11 = NULL; + int *piAddr12 = NULL; + int i,j,k , bottom , fontFace; + char **fontface = NULL,**text= NULL; + double thickness=1 ,linetype=8,bottomleft ; + double x1,y1,fontscale,r_value,g_value,b_value; + bool bottomLeftOrigin; + + //checking input argument + CheckInputArgument(pvApiCtx, 9, 12); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of point of bootom-left corner of the text string + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,&x1); + if(intErr) + return intErr; + + //for value of y coordinate of point of bootom-left corner of the text string + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&y1); + if(intErr) + return intErr; + + //for value of fontscale + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&fontscale); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&r_value); + if(intErr) + return intErr; + + //for value of G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&g_value); + if(intErr) + return intErr; + + //for value of Bvalue of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7 ,&b_value); + if(intErr) + return intErr; + + /// for text to be written on the image + //Syntax : SciErr getMatrixOfString(void* _pvCtx, int* _piAddress, int* _piRows, int* _piCols, int* _piLength, char** _pstStrings) + //fisrt call to retrieve dimensions + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr8, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr8, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + text = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + text[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr8, &iRows, &iCols, piLen, text); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ///for the fontface of text + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr9, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen2 = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr9, &iRows, &iCols, piLen2, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + fontface = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + fontface[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr9, &iRows, &iCols, piLen2, fontface); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // for thickness of text + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr10,&thickness); + if(intErr) + return intErr; + + //for line type of text default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr11,&linetype); + if(intErr) + return intErr; + + // for shift in line defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,12,&piAddr12); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr12,&bottomleft); + if(intErr) + return intErr; + + + if (bottomleft == 0) + bottomLeftOrigin = false; + else if (bottomleft == 1) + bottomLeftOrigin = true; + else + { + bottomLeftOrigin = false; + sciprint("Only 0 and 1 acceptable , taking false instead"); + } + /// setting fontface FONT_HERSHEY_SIMPLEX, FONT_HERSHEY_PLAIN, FONT_HERSHEY_DUPLEX, FONT_HERSHEY_COMPLEX, FONT_HERSHEY_TRIPLEX, FONT_HERSHEY_COMPLEX_SMALL, + // FONT_HERSHEY_SCRIPT_SIMPLEX, or FONT_HERSHEY_SCRIPT_COMPLEX, + if(strcmp(fontface[0],"FONT_HERSHEY_SIMPLEX")==0) + fontFace = FONT_HERSHEY_SIMPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_PLAIN")==0) + fontFace= FONT_HERSHEY_PLAIN; + else if (strcmp(fontface[0],"FONT_HERSHEY_DUPLEX")==0) + fontFace= FONT_HERSHEY_DUPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_COMPLEX")==0) + fontFace= FONT_HERSHEY_COMPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_TRIPLEX")==0) + fontFace = FONT_HERSHEY_TRIPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_COMPLEX_SMALL")==0) + fontFace = FONT_HERSHEY_COMPLEX_SMALL; + else if (strcmp(fontface[0],"FONT_HERSHEY_SCRIPT_SIMPLEX")==0) + fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + else if (strcmp(fontface[0],"FONT_HERSHEY_SCRIPT_COMPLEX")==0) + fontFace = FONT_HERSHEY_SCRIPT_COMPLEX; + else + { + sciprint("Invalid fontface given. FONT_HERSHEY_PLAIN was used instead"); + fontFace = FONT_HERSHEY_PLAIN; + } + //temporary pt variable, to use in function as bottom left corner + Point pt1(x1,y1); + /// checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead"); + } + + + + //syntax: putText(Mat& img, const string& text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=8, bool bottomLeftOrigin=false ) + putText( src, text[0], pt1, fontFace, fontscale, Scalar(b_value,g_value,r_value), thickness, linetype, bottomLeftOrigin ); + + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway1/cpp/opencv_pyrDown.cpp b/sci_gateway1/cpp/opencv_pyrDown.cpp new file mode 100644 index 0000000..7a8df72 --- /dev/null +++ b/sci_gateway1/cpp/opencv_pyrDown.cpp @@ -0,0 +1,156 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** + return_image = pyrDown(input_image, dstsize_width, distsize_height, border; +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_pyrDown(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + char **borderType = NULL; + double dstsize_width,dstsize_height; + int border; + int i; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of dstsize_width + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &dstsize_width); + if(intErr) + return intErr; + + //for value of dstsize_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&dstsize_height); + if(intErr) + return intErr; + + //for border + sciErr = getVarAddressFromPosition(pvApiCtx,4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //error checks + if(dstsize_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead\n"); + dstsize_height=1; + } + if(dstsize_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead\n"); + dstsize_width=1; + } + if((dstsize_width != image.cols*2) && (dstsize_width != image.cols*2+1) && (dstsize_width != image.cols*2-1)) + { + sciprint("Incorrect dstsize_width value was used. src.cols*2+1 value was calculated and used instead\n"); + dstsize_width = image.cols*2+1; + } + if((dstsize_height != image.rows*2) && (dstsize_height != image.rows*2+1) && (dstsize_height != image.rows*2-1)) + { + sciprint("Incorrect dstsize_height value was used. src.rows*2+1 value was calculated and used instead\n"); + dstsize_height = image.rows*2+1; + } + + Mat new_image(dstsize_height,dstsize_width,image.type()); + + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + pyrDown(image,new_image,Size(dstsize_width,dstsize_height),border); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_pyrUp.cpp b/sci_gateway1/cpp/opencv_pyrUp.cpp new file mode 100644 index 0000000..18459c3 --- /dev/null +++ b/sci_gateway1/cpp/opencv_pyrUp.cpp @@ -0,0 +1,156 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** + return_image = pyrUp(input_image, dstsize_width, distsize_height, border; +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_pyrUp(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piLen = NULL; + char **borderType = NULL; + double dstsize_width,dstsize_height; + int border; + int i; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of dstsize_width + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &dstsize_width); + if(intErr) + return intErr; + + //for value of dstsize_width + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&dstsize_height); + if(intErr) + return intErr; + + sciErr = getVarAddressFromPosition(pvApiCtx,4, &piAddr4); + + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + borderType = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + borderType[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, borderType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //error checks + if(dstsize_height<0) + { + sciprint("Positive Value Required for Height. 1 value was used instead\n"); + dstsize_height=1; + } + if(dstsize_width<0) + { + sciprint("Positive Value Required for Width. 1 value was used instead\n"); + dstsize_width=1; + } + if((dstsize_width != image.cols*2) && (dstsize_width != image.cols*2+1) && (dstsize_width != image.cols*2-1)) + { + sciprint("Incorrect dstsize_width value was used. src.cols*2+1 value was calculated and used instead\n"); + dstsize_width = image.cols*2+1; + } + if((dstsize_height != image.rows*2) && (dstsize_height != image.rows*2+1) && (dstsize_height != image.rows*2-1)) + { + sciprint("Incorrect dstsize_height value was used. src.rows*2+1 value was calculated and used instead\n"); + dstsize_height = image.rows*2+1; + } + + Mat new_image(dstsize_height,dstsize_width,image.type()); + + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + pyrUp(image,new_image,Size(dstsize_width,dstsize_height),border); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_pyramid.cpp b/sci_gateway1/cpp/opencv_pyramid.cpp new file mode 100644 index 0000000..a158a93 --- /dev/null +++ b/sci_gateway1/cpp/opencv_pyramid.cpp @@ -0,0 +1,134 @@ +/******************************************************** +Author: Diwakar Bhardwaj +********************************************************* +Usage :return_image=pyramid(input_image,'direction',level) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_pyramid(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piLen = NULL; + char **direction = NULL; + int i,j,k=0,l=0; + double alpha, beta,x,n; + + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat image,new_image; + + retrieveImage(image, 1); + + sciErr = getVarAddressFromPosition(pvApiCtx,2, &piAddr2); + + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + direction = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + direction[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, direction); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3 ,&x); + if(intErr) + return intErr; + n=x; + k=image.rows; + l=image.cols; + while(n--) + { + + //if expand operation is performed + if(strcmp(direction[0], "expand") == 0) + { + pyrUp(image, new_image, Size( image.cols*2, image.rows*2 )); + //Size size((k*2)-1,(l*2)-1); + //resize(image,new_image,size); + } + + + //if reduced operation is performed + else if(strcmp(direction[0], "reduce") == 0) + { + pyrDown( image, new_image, Size( image.cols/2, image.rows/2 )); + //Size size(k/2,l/2); + //resize(image,new_image,size); + } + image=new_image; + + } + + + + + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_rectangle.cpp b/sci_gateway1/cpp/opencv_rectangle.cpp new file mode 100644 index 0000000..36d24f0 --- /dev/null +++ b/sci_gateway1/cpp/opencv_rectangle.cpp @@ -0,0 +1,207 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + return_image = circle(input_image , x_coordinate_of_point1 , y_coordinate_point1, x_coordinate_of_point2 , y_coordinate_point2 , r_value,g_value,b_value,thickness,linetype,shift); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_rectangle(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + int *piAddr11 = NULL; + int i,j,k; + double thickness=1 , linetype=8,shift =0 ,point1_x ,point1_y,point2_x ,point2_y,r_value,g_value,b_value; + + + //checking input argument + CheckInputArgument(pvApiCtx, 11, 11); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat src; + retrieveImage(src,1); + + //for value of x coordinate of point1 + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,&point1_x); + if(intErr) + return intErr; + + + //for value of y coordinate of point1 + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&point1_y); + if(intErr) + return intErr; + + //for value of x coordinate of point2 + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4,&point2_x); + if(intErr) + return intErr; + + + //for value of y coordinate of point2 + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5,&point2_y); + if(intErr) + return intErr; + + //for value of R value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6 ,&r_value); + if(intErr) + return intErr; + + //for value of G value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7 ,&g_value); + if(intErr) + return intErr; + + // for B value of colour + sciErr = getVarAddressFromPosition(pvApiCtx,8,&piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr8,&b_value); + if(intErr) + return intErr; + + // for thickness of circle default: 1 + sciErr = getVarAddressFromPosition(pvApiCtx,9,&piAddr9); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr9,&thickness); + if(intErr) + return intErr; + + //for line type of circle default: 8 + sciErr = getVarAddressFromPosition(pvApiCtx,10,&piAddr10); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr10,&linetype); + if(intErr) + return intErr; + + // for shift in circle defulat : 0 + sciErr = getVarAddressFromPosition(pvApiCtx,11,&piAddr11); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr11,&shift); + if(intErr) + return intErr; + + // checking the parmeters for correct values + if( r_value <0 || r_value >255) + { + r_value=0; + sciprint(" r value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( g_value <0 || g_value >255) + { + g_value=0; + sciprint(" g value of colour should be between 0 and 255 , using 0 instead\n"); + } + if( b_value <0 || b_value >255) + { + b_value=0; + sciprint(" b value of colour should be between 0 and 255 , using 0 instead\n"); + } + if ((linetype!=0) && (linetype!=4)&& (linetype!=8)) + { + linetype=8; + sciprint("Only 0/4/8 allowed , using 8 instead\n"); + } + + // Draw the circles detected + rectangle( src, Point(point1_x,point1_y), Point(point2_x,point2_y), Scalar(r_value,g_value,b_value), thickness, linetype, shift); + +//// sending the image + string tempstring = type2str(src.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,src,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + + } + +} + diff --git a/sci_gateway1/cpp/opencv_rectifyStereoImages.cpp b/sci_gateway1/cpp/opencv_rectifyStereoImages.cpp new file mode 100644 index 0000000..6b26e27 --- /dev/null +++ b/sci_gateway1/cpp/opencv_rectifyStereoImages.cpp @@ -0,0 +1,243 @@ +/******************************************************** +Author: Diwakar Bhardwaj +********************************************************* +Usage :[image1,image2] = rectifyStereoImages(input_image1,cameramatrix1,Distortion_coff1,input_image2,cameramatrix2,Distortioncoff2,Translationvector,Rotationvector) + +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_rectifyStereoImages(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0,iRows2=0,iCols2=0,iRows3=0,iCols3=0,iRows4=0,iCols4=0,iRows5=0,iCols5=0; + int iRows6=0,iCols6=0,iRows7=0,iCols7=0,iRows8=0,iCols8=0,iRows9=0,iCols9=0,iRows10=0,iCols10=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + int *piAddr9 = NULL; + int *piAddr10 = NULL; + //stereoParams + double * D1real = NULL;//Distortion Matrix 5*1 + double * M1real = NULL;//Camera Matrix 3*3 + double * P1real = NULL;//Projection Matrix 3*3 + double * R1real = NULL;//Rotation Matrix 3*3 + double * D2real = NULL;//Distortion Matrix 5*1 + double * M2real = NULL;//Camera Matrix 3*3 + double * P2real = NULL;//Projection Matrix 3*3 + double * R2real = NULL;//Rotation Matrix 3*3 + double * Treal = NULL;//Translation vector 3*1 + double * Rreal = NULL;//Rotation Matrix 3*3 + + Mat D1(5,1,CV_64F); + Mat M1(3,3,CV_64F); + Mat D2(5,1,CV_64F); + Mat M2(3,3,CV_64F); + Mat T (3,1,CV_64F); + Mat R (3,3,CV_64F); + + CheckInputArgument(pvApiCtx, 8, 8); + CheckOutputArgument(pvApiCtx, 2, 2) ; + + Mat image1,image2; + retrieveImage(image1,1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows2, &iCols2, &M1real); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows3, &iCols3, &D1real); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + retrieveImage(image2,4); + + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr5, &iRows5, &iCols5, &M2real); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr6, &iRows6, &iCols6, &D2real); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, 7, &piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr7, &iRows7, &iCols7, &Treal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, 8, &piAddr8); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr7, &iRows8, &iCols8, &Rreal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows2!=3 and iCols2!=3 or iRows5!=3 and iCols5!=3) + { + sciprint("camera matrix must be size of 3*3"); + } + + if(iRows3!=1 and iCols3!=5 or iRows6!=1 and iCols6!=5) + { + sciprint("Distortion cofficient matrix must be size of 5*1"); + } + if(iRows7!=1 and iCols7!=3) + { + sciprint("Translation vector must be 3*1"); + } + + + if(iRows8!=3 and iCols8!=3) + { + sciprint("Rotation Matrix must be 3*3"); + } + + for(int i=0;i<3;i++) + for(int j=0;j<3;j++) + M1.at(i,j)=M1real[i+j*3]; + + for(int i=0;i<5;i++) + for(int j=0; j<1; ++j) + D1.at(i,j)=D1real[i+j*5]; + + for(int i=0;i<3;i++) + for(int j=0;j<3;j++) + M2.at(i,j)=M2real[i+j*3]; + + for(int i=0;i<5;i++) + for(int j=0; j<1; ++j) + D2.at(i,j)=D2real[i+j*5]; + + for(int i=0; i<3; ++i) + for(int j=0; j<1; ++j) + T.at(i,j)=Treal[i+j*3]; + + for(int i=0;i<3;i++) + for(int j=0;j<3;j++) + R.at(i,j)=Rreal[i+j*3]; + + Size imgSize(image1.rows*2,image1.cols*2); + Mat R1,R2,P1,P2,Q; + + stereoRectify(M1,D1,M2,D2, imgSize, R, T, R1, R2,P1, P2, Q); + + + Mat left, right; //Create matrices for storing input images + + // transformation and rectification maps + Mat camera1map1, camera1map2; + Mat camera2map1, camera2map2; + + initUndistortRectifyMap(M1, D1, R1, P1, imgSize , CV_32F, camera1map1, camera1map2); + initUndistortRectifyMap(M2, D2, R2, P2, imgSize, CV_32F, camera2map1, camera2map2); + + Mat leftStereoUndistorted, rightStereoUndistorted; + //Rectify and undistort images + remap(image1, left, camera1map1, camera1map2, INTER_LINEAR); + remap(image2, right, camera2map1, camera2map2, INTER_LINEAR); + /*Size size(image1.rows,image1.cols); + resize(image1,left,size); + Size size1(image2.rows,image2.cols); + resize(image2,right,size1); + Combining left and right rectified image in one image plane + Size sz1 = left.size(); + Size sz2 = right.size(); + Mat final_image (sz1.height, sz1.width+sz2.width, CV_8UC3); + final_image.adjustROI(0, 0, 0, -sz2.width); + left.copyTo(final_image); + final_image.adjustROI(0, 0, -sz1.width, sz2.width); + right.copyTo(final_image); + final_image.adjustROI(0, 0, sz1.width, 0);*/ + + int temp1 = nbInputArgument(pvApiCtx) + 1; + string tempstring1 = type2str(left.type()); + char *checker1; + checker1 = (char *)malloc(tempstring1.size() + 1); + memcpy(checker1, tempstring1.c_str(), tempstring1.size() + 1); + returnImage(checker1,left,1); + free(checker1); + + int temp2 = nbInputArgument(pvApiCtx) + 2; + string tempstring2 = type2str(right.type()); + char *checker2; + checker2 = (char *)malloc(tempstring2.size() + 2); + memcpy(checker2, tempstring2.c_str(), tempstring2.size() + 2); + returnImage(checker2,right,2); + free(checker2); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_retrieveImages.cpp b/sci_gateway1/cpp/opencv_retrieveImages.cpp new file mode 100644 index 0000000..cace204 --- /dev/null +++ b/sci_gateway1/cpp/opencv_retrieveImages.cpp @@ -0,0 +1,506 @@ +/******************************************************************************************************************************** +* Author: Umang Agrawal * +* Code: reteieveImage.cpp * +* Funtion Call: [ Index, Score, ImageWord ] = retrieveImage( QureyImage, IndexImage, OptionalArgument) * +* Optional Argument: Name Value * +* NumResults Any Value greater than 0 * +* ROI [ x_coordinate y_coordinate width height ] * +********************************************************************************************************************************/ +#include +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/ml/ml.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + bool count_based(const vector& a, const vector& b) { + return a[1] > b[1]; + } + + int opencv_retrieveImages(char *fname, unsigned long fname_len) + { + SciErr sciErr; + + int *piAddr = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piChild = NULL; + int *piGrandChild = NULL; + int *piLen = NULL; + char **pstData = NULL; + int iRows, iCols; + int no_item_list = 0; + int no_item_list_2 = 0; + int inp_params = 0; + char **arg = NULL; + + char ***location_img = NULL; + double **WordIndex = NULL; + double **Location = NULL; + double **Vocab_size = NULL; + double **Count = NULL; + double *wordFrequency = NULL; + char *bagOfFeaturesLocation = NULL; + int count_res = 0, count_roi = 0; + double numRes = 0; + double *roi_arg = NULL; + double x, y, width, height; + + Mat image; + Mat feature_des; + Mat dictionary; + Mat des_matched; + Mat hist; + vector keypoints; + vector valid_key; + vector< vector > clusterID; + vector WordIndex_size; + + int indx; + int search_ind; + double *Index_Out = NULL; + double *Score = NULL; + double **ImageWords = NULL; + + CheckInputArgument(pvApiCtx, 2, 6); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 3); //Check on Number of Output Arguments + + retrieveImage(image, 1); + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr2)) + { + Scierror(999, "Error: Invalid Second argument. List Expected.\n"); + return 0; + } + sciErr = getListItemNumber(pvApiCtx, piAddr2, &no_item_list); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr2, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!(strcmp(pstData[0],"invertedImageIndex")==0)) + { + Scierror(999, "Error: The input argument 2 is not of type invertedImageIndex.\n"); + return 0; + } + + sciErr = getListItemAddress(pvApiCtx, piAddr2, 2, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getListItemNumber(pvApiCtx, piChild, &no_item_list_2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + WordIndex = (double**)malloc(sizeof(double*)*no_item_list_2); + Location = (double**)malloc(sizeof(double*)*no_item_list_2); + Vocab_size = (double**)malloc(sizeof(double*)*no_item_list_2); + Count = (double**)malloc(sizeof(double*)*no_item_list_2); + for( int i=0; i> dictionary; + fs.release(); + + SurfFeatureDetector detector(100, 4, 2, 1, 1); + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr extractor = new SurfDescriptorExtractor(100, 4, 2, 1, 1); + BOWImgDescriptorExtractor bowDE(extractor, matcher); + bowDE.setVocabulary(dictionary); + detector.detect(image, keypoints); + bowDE.compute(image, keypoints, hist, &clusterID, &des_matched); + + ImageWords = (double**)malloc(sizeof(double*)*4*1); + ImageWords[0] = (double*)malloc(sizeof(double)*keypoints.size()*1); + ImageWords[1] = (double*)malloc(sizeof(double)*keypoints.size()*2); + ImageWords[2] = (double*)malloc(sizeof(double)*1); + ImageWords[3] = (double*)malloc(sizeof(double)*1); + + ImageWords[2][0] = Vocab_size[0][0]; + ImageWords[3][0] = keypoints.size(); + + vector > count_index(no_item_list_2 , vector(3)); + + for( int i=0; i +using namespace std; +extern "C" +{ +#include "api_scilab.h" +#include "Scierror.h" +#include "BOOL.h" +#include +#include "sciprint.h" + + int opencv_rotationVectorToMatrix(char *fname, unsigned long fname_len) + { + + //Error management variable + SciErr sciErr; + + //Variable declaration + int iComplex = 0; + int iType = 0; + int iRows = 0; + int iCols = 0; + int *piAddr = NULL; + double sinOfangle = 0; + double cosOfangle = 0; + double t, x, y, z; + double theta; + double *pdbl = NULL; + double *matrix; + double *identity; + double *A; + double *B; + + //Check input output arguments + checkInputArgument(pvApiCtx, 1, 1); + checkOutputArgument(pvApiCtx, 1, 1); + + //Get variable address of the first input argument + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdbl); + if(sciErr.iErr || iCols != 3 || iRows != 1) + { + printError(&sciErr, 0); + return 0; + } + + x = pdbl[0]; + y = pdbl[1]; + z = pdbl[2]; + + theta = sqrt(x * x + y * y + z * z); + + sinOfangle = sin(theta); + cosOfangle = cos(theta); + t = 1 - cosOfangle; + x = pdbl[0] / theta; + y = pdbl[1] / theta; + z = pdbl[2] / theta; + + matrix = (double*)malloc(sizeof(double) * 9); + A = (double*)malloc(sizeof(double) * 9); + B = (double*)malloc(sizeof(double) * 9); + identity = (double*)malloc(sizeof(double) * 9); + + for(int i = 0; i < 3; i++) + { + for(int j = 0; j < 3; j++) + { + if(i == 0) + { + if(j == 0) + A[j * 3 + i] = x * x; + else if(j == 1) + A[j * 3 + i] = x * y; + else if(j == 2) + A[j * 3 + i] = x * z; + + } + else if(i == 1) + { + if(j == 0) + A[j * 3 + i] = x * y; + else if(j == 1) + A[j * 3 + i] = y * y; + else if(j == 2) + A[j * 3 + i] = y * z; + } + else + { + if(j == 0) + A[j * 3 + i] = x * z; + else if(j == 1) + A[j * 3 + i] = y * z; + else if(j == 2) + A[j * 3 + i] = z * z; + } + + } + } + + for(int i = 0; i < 3; i++) + { + for(int j = 0; j < 3; j++) + { + if(i == 0) + { + if(j == 0) + B[j * 3 + i] = 0; + else if(j == 1) + B[j * 3 + i] = -z; + else if(j == 2) + B[j * 3 + i] = y; + + } + else if(i == 1) + { + if(j == 0) + B[j * 3 + i] = z; + else if(j == 1) + B[j * 3 + i] = 0; + else if(j == 2) + B[j * 3 + i] = -x; + } + else + { + if(j == 0) + B[j * 3 + i] = -y; + else if(j == 1) + B[j * 3 + i] = x; + else if(j == 2) + B[j * 3 + i] = 0; + } + + } + } + + for(int i = 0; i < 3; i++) + { + for(int j = 0;j < 3; j++) + { + if(i == j) + identity[j * 3 + i] = 1; + else + identity[j * 3 + i] = 0; + } + } + + for(int i = 0; i < 3; i++) + { + for(int j = 0; j < 3; j++) + { + matrix[i * 3 + j] = identity[j * 3 + i] * cosOfangle + t * A[j * 3 + i] + sinOfangle * B[j * 3 + i]; + } + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 3, 3, matrix); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Return output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} + + + diff --git a/sci_gateway1/cpp/opencv_scharr.cpp b/sci_gateway1/cpp/opencv_scharr.cpp new file mode 100644 index 0000000..cb897d0 --- /dev/null +++ b/sci_gateway1/cpp/opencv_scharr.cpp @@ -0,0 +1,155 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + return_image = scharr(input_image, ddepth, dx, dy, scale, delta); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_scharr(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **ddepth = NULL; + int i,j,k; + double dx,dy,scale,delta; + + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of dx + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &dx); + if(intErr) + return intErr; + + //for value of dy + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &dy); + if(intErr) + return intErr; + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &scale); + if(intErr) + return intErr; + + //for value of delta + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &delta); + if(intErr) + return intErr; + + Mat new_image(image.rows,image.cols,image.type()); + + if(strcmp(ddepth[0],"CV_8U")==0) + Scharr(image,new_image,CV_8U,dx,dy,scale,delta); + else if(strcmp(ddepth[0],"CV_16U")==0) + Scharr(image,new_image,CV_16U,dx,dy,scale,delta); + else if(strcmp(ddepth[0],"CV_16S")==0) + Scharr(image,new_image,CV_16S,dx,dy,scale,delta); + else if(strcmp(ddepth[0],"CV_32F")==0) + Scharr(image,new_image,CV_32F,dx,dy,scale,delta); + else if(strcmp(ddepth[0],"CV_64F")==0) + Scharr(image,new_image,CV_64F,dx,dy,scale,delta); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_sepFilter2D.cpp b/sci_gateway1/cpp/opencv_sepFilter2D.cpp new file mode 100644 index 0000000..cf3db15 --- /dev/null +++ b/sci_gateway1/cpp/opencv_sepFilter2D.cpp @@ -0,0 +1,261 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = sepFilter2D(input_image,"",kernel_x,kernel_y,anchor_x,anchor_y,delta,""); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_sepFilter2D(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + char **ddepth = NULL; + char **borderType = NULL; + int i,j,k; + double *kernelX,*kernelY,anchorX,anchorY,delta,border,actualddepth; + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 8); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); +cout<<"here1"; + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for kernel_x matrix + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&kernelX); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n; + + if(iRows == 1) + n=iCols; + else + n=iRows; + double kernelArray_x[n]; + //assigning values to actual kernelMatrix + for(i=0;i= n || anchorY >=n) + { + sciprint("Invalid anchor point given. Centre point (-1,-1) was used instead"); + anchorY = -1; + anchorX = -1; + } + Mat new_image(image.rows,image.cols,image.type()); + Point pt(anchorX,anchorY); + + if(strcmp(ddepth[0],"CV_8U")==0) + actualddepth=CV_8U; + else if(strcmp(ddepth[0],"CV_16U")==0) + actualddepth=CV_16U; + else if(strcmp(ddepth[0],"CV_16S")==0) + actualddepth=CV_16S; + else if(strcmp(ddepth[0],"CV_32F")==0) + actualddepth=CV_32F; + else if(strcmp(ddepth[0],"CV_64F")==0) + actualddepth=CV_64F; + else + { + sciprint("Invalid type %s used. CV_8U was used instead by default",ddepth[0]); + actualddepth = CV_8U; + } + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + //applying function + sepFilter2D(image,new_image,actualddepth,kernelMatrix_x,kernelMatrix_y,pt,delta,border); + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_showMatchedFeatures.cpp b/sci_gateway1/cpp/opencv_showMatchedFeatures.cpp new file mode 100644 index 0000000..ea26e80 --- /dev/null +++ b/sci_gateway1/cpp/opencv_showMatchedFeatures.cpp @@ -0,0 +1,275 @@ +/********************************************************************************* +*Author : Kevin George +* +*-> To execute, showMatchedFeatures(I1,I2) +* where I1 & I2 are images +* +*********************************************************************************/ +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/nonfree/nonfree.hpp" +#include +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + // # include "../common.cpp" + + int opencv_showMatchedFeatures(char *fname, unsigned long fname_len) + { + + //-> Error Management variables + SciErr sciErr; + int intErr=0; + + //-> Mat containers for images + Mat image_1; + Mat image_2; + + //-> Address of Various Arguments + int *piAddr = NULL; + + //-> Local variables + int minHessian = 400; //-> For SURF algorithm + int num_InputArgs; + //-> KeyPoint is data structure to store a point feature + // detected by a keypoint detector(OpenCV) + vector keypoints_1; + vector keypoints_2; + Mat descriptors_1, descriptors_2; + Mat img_matches; + vector< DMatch > good_matches; + vector< DMatch > matches; + double max_dist = 0; double min_dist = 100; + int *outList = NULL; + unsigned char *red = NULL; + unsigned char *green = NULL; + unsigned char *blue = NULL; + /*double *indexPairs = NULL; + double *matchMetric = NULL; + double size=1; + int iRows, iCols; */ + + //KeyPoint *X = new KeyPoint; //-> Remember to free this Kevin + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + CheckInputArgument(pvApiCtx, 2, 5); //Check on Number of Input Arguments + CheckOutputArgument(pvApiCtx, 1, 5); //Check on Number of Output Arguments + + //-> Read Image + retrieveImage( image_1, 1); + retrieveImage(image_2, 2); + + //-> Count number of input arguments + //num_InputArgs = *getNbInputArgument(pvApiCtx); + + //-> Based on number of input arguments + + +//****************************************************** Retrieval of 3rd,4th,5th args ******************************************************************* + + /* + /*************** Size of good matches ************** + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getScalarDouble(pvApiCtx, piAddr, &size); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(getScalarDouble(pvApiCtx, piAddr, &size)) + { + Scierror(999,"Cannot Read Upright Value\n"); + return 0; + } + + vector good_matches(size); + + + /*************** For indexPairs ************* + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &indexPairs); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + //-> Extract X and Ycoordinates of KeyPoints + + //X->pt = Point2f(keypoints[i],keypoints[iRows+i]); + //keypoints_1.push_back(*X); + + + /*************** For matchMetric ************** + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &matchMetric); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(int i=0; i< size; i++) + { + good_matches[i].queryIdx = indexPairs[i]; + good_matches[i].trainIdx = indexPairs[ static_cast(size)+i]; + good_matches[i].distance = matchMetric[i]; + + } + */ + +//***************************************************** Actual Processing ************************************************************* + + //-- Step 1: Calculate keypoints + SurfFeatureDetector detector( minHessian ); + detector.detect( image_1, keypoints_1 ); + detector.detect( image_2, keypoints_2 ); + + //-- Step 2: Calculate descriptors (feature vectors) + SurfDescriptorExtractor extractor; + extractor.compute( image_1, keypoints_1, descriptors_1 ); + extractor.compute( image_2, keypoints_2, descriptors_2 ); + + //-- Step 3: Matching descriptor vectors using FLANN matcher + FlannBasedMatcher matcher; + matcher.match( descriptors_1, descriptors_2, matches ); + + //-- Quick calculation of max and min distances between keypoints + for( int i = 0; i < descriptors_1.rows; i++ ) + { + double dist = matches[i].distance; + if( dist < min_dist ) min_dist = dist; + if( dist > max_dist ) max_dist = dist; + } + + for( int i = 0; i < descriptors_1.rows; i++ ) + { + //if( matches[i].distance <= max(2*min_dist, 0.02) ) + if( matches[i].distance <= (2*min_dist) ) + { good_matches.push_back( matches[i]); } + } + + //-> Draw only "good" matches + drawMatches( image_1, keypoints_1, image_2, keypoints_2, + good_matches,img_matches, Scalar::all(-1), Scalar::all(-1), + vector(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); + + //-> Show detected matches + //imshow( "Good Matches", img_matches ); + //waitKey(0); + + if( img_matches.channels() == 1) + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + red = (unsigned char *)malloc(sizeof(unsigned char)*img_matches.rows*img_matches.cols); + + for(int k=0;k(k, p); + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, img_matches.rows, img_matches.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + } + + else + { + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &outList); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + red = (unsigned char *)malloc(sizeof(unsigned char)*img_matches.rows*img_matches.cols); + green = (unsigned char *)malloc(sizeof(unsigned char)*img_matches.rows*img_matches.cols); + blue = (unsigned char *)malloc(sizeof(unsigned char)*img_matches.rows*img_matches.cols); + + for(int k=0;k(k, p); + red[k+img_matches.rows*p]=intensity.val[2]; + green[k+img_matches.rows*p]=intensity.val[1]; + blue[k+img_matches.rows*p]=intensity.val[0]; + } + } + + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, img_matches.rows, img_matches.cols, red); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 2, img_matches.rows, img_matches.cols, green); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 3, img_matches.rows, img_matches.cols, blue); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + free(red); + free(green); + free(blue); + + } + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } + +} diff --git a/sci_gateway1/cpp/opencv_sobel.cpp b/sci_gateway1/cpp/opencv_sobel.cpp new file mode 100644 index 0000000..912be4d --- /dev/null +++ b/sci_gateway1/cpp/opencv_sobel.cpp @@ -0,0 +1,172 @@ +/******************************************************** + Author: Sukul Bagai +********************************************************* + return_image = sobel(input_image, ddepth, dx, dy, kszie, scale, delta); +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_sobel(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **ddepth = NULL; + int i,j,k; + double dx,dy,ksize,scale,delta; + + + //checking input argument + CheckInputArgument(pvApiCtx, 7, 7); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for value of ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of dx + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &dx); + if(intErr) + return intErr; + + //for value of dy + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &dy); + if(intErr) + return intErr; + + //for value of ksize + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &ksize); + if(intErr) + return intErr; + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &scale); + if(intErr) + return intErr; + + //for value of delta + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr7); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr7, &delta); + if(intErr) + return intErr; + + Mat new_image(image.rows,image.cols,CV_8UC3); + if((int)ksize!=1 && (int)ksize!=3 && (int)ksize!=5 && (int)ksize!=7) + { + sciprint("Value of 1,3,5,7 Required for ksize. Default value of 3 was used instead"); + ksize=3; + } + + //void Sobel(InputArray src, OutputArray dst, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT ) + if(strcmp(ddepth[0],"CV_8U")==0) + Sobel(image,new_image,CV_8U,dx,dy,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_16U")==0) + Sobel(image,new_image,CV_16U,dx,dy,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_16S")==0) + Sobel(image,new_image,CV_16S,dx,dy,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_32F")==0) + Sobel(image,new_image,CV_32F,dx,dy,ksize,scale,delta); + else if(strcmp(ddepth[0],"CV_64F")==0) + Sobel(image,new_image,CV_64F,dx,dy,ksize,scale,delta); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_threshold.cpp b/sci_gateway1/cpp/opencv_threshold.cpp new file mode 100644 index 0000000..1210d47 --- /dev/null +++ b/sci_gateway1/cpp/opencv_threshold.cpp @@ -0,0 +1,146 @@ +/******************************************************** + Author: Abhilasha Sancheti & Sukul Bagai +********************************************************* + [return_image, double] = threshold(input_image, threshold_value, max_value, ""); +********************************************************/ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_threshold(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddrNew2 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int i,j,k; + double thresh,max_val; + int type; + char **gettype = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 2, 2) ; + Mat image; + retrieveImage(image,1); + if(no_of_channels(image.type()) != 1) + cvtColor( image, image, CV_BGR2GRAY ); + + + //for threshold value (thresh) + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2,&thresh); + if(intErr) + return intErr; + + //for max_value + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3,&max_val); + if(intErr) + return intErr; + + +// for type of thresholding (gettype) + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + //checking for error if any + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + gettype= (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + { + gettype[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data of each string of first argument + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, piLen, gettype); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //for value of type + + Mat new_image(image.rows,image.cols,CV_8UC1); + if(strcmp(gettype[0],"THRESH_BINARY")==0) + type = 0; + else if (strcmp(gettype[0],"THRESH_BINARY_INV")==0) + type=1; + else if (strcmp(gettype[0],"THRESH_TRUNC")==0) + type=2; + else if (strcmp(gettype[0],"THRESH_TOZERO")==0) + type=3; + else if (strcmp(gettype[0],"THRESH_TOZERO_INV")==0) + type=4; + else + { + sciprint("Wrong type given. THRESH_BINARY used instead"); + type=0; + } + double res = threshold( image ,new_image, thresh, max_val,type); + // Converting resultant image to 3D matrix + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + intErr = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2,res); + if(intErr) + { + return intErr; + } + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_trainImageCategoryClassifier.cpp b/sci_gateway1/cpp/opencv_trainImageCategoryClassifier.cpp new file mode 100644 index 0000000..e2a504a --- /dev/null +++ b/sci_gateway1/cpp/opencv_trainImageCategoryClassifier.cpp @@ -0,0 +1,349 @@ +/*************************************************** +Author : Rohit Suri +***************************************************/ +#include +#include +#include +#include + +using namespace std; +using namespace cv; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + int opencv_trainImageCategoryClassifier(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr extractor = new SurfDescriptorExtractor(1, 4, 2, 1, int(upright)); + BOWImgDescriptorExtractor bowDE(extractor, matcher); + SurfFeatureDetector detector(1, 4, 2, 1, int(upright)); + char *fileName = NULL; + Mat dictionary,inp,features; + vector keyPoints; + + + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + int *count = NULL; + char **description = NULL; + char ***location = NULL; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + char *classifierLocation = "classifier.yml"; + char *objectType = "classifier"; + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type imageSet.\n"); + return 0; + } + + // Extracting object type and checking if type is imageSet + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"imageSet")==0)) + { + Scierror(999, "Error: The input argument #1 is not of type imageSet.\n"); + return 0; + } + + // Extracting Description attribute of input argument + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + description = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + description[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + descriptionCount = iRows; + + // Extracting Count attribute of input argument + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &count); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location = (char***) malloc(sizeof(char**) * descriptionCount); + sciErr = getListItemAddress(pvApiCtx, piAddr, 4, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int iter = 1; iter<=descriptionCount; iter++) + { + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location[iter-1] = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int colIter = 0 ; colIter < iRows * iCols ; colIter++) + { + location[iter-1][colIter] = (char*)malloc(sizeof(char) * (piLen[colIter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, location[iter-1]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + // Second argument + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #2 is not of type bagOfFeatures.\n"); + return 0; + } + + // Extracting object type and checking if type is bagOfFeatures + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"bagOfFeatures")==0)) + { + Scierror(999, "Error: The input argument #2 is not of type bagOfFeatures.\n"); + return 0; + } + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + bagOfFeaturesLocation = pstData[0]; + //------Actual processing------// + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + if(dictionary.rows==0 || dictionary.cols==0) + { + sciprint("Error: The provided file for bagOfFeatures may be invalid.\n"); + } + sciprint("Training an image category classifier for %d categories.\n",descriptionCount); + sciprint("-------------------------------------------------------\n\n"); + for(int i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/calib3d/calib3d.hpp" +#include +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_triangulatePoints(char *fname, unsigned long fname_len) + { + //variables + int i,j,n,m; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + + double *pdblReal = NULL; + + SciErr sciErr; + + /*------------------------------------------- Checking Input Argument ----------------------------------------------------*/ + + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + /*------------------------------------- Projection matrix 1 :- First argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=3 or iCols!=4){ + Scierror(iRows,"Projection matrix (arg 1) must be a 3 X 4 matrix"); + return 0; + } + Mat projMat1(iRows, iCols, CV_64F); + for(i = 0; i < iRows; i++) + for(j = 0; j < iCols; j++){ + projMat1.at(i,j) = pdblReal[i + (j * iRows)]; + } + + /* ------------------------------------ Projection matrix 2 :- Second argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=3 or iCols!=4){ + Scierror(iRows,"Projection matrix (arg 2) must be a 3 X 4 matrix"); + return 0; + } + Mat projMat2(iRows, iCols, CV_64F); + for(i=0;i(i,j) = pdblReal[i + (j * iRows)]; + + + /* ------------------------------------ Points matrix 1 :- Third argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + n = iCols; + if(iRows!=2){ + Scierror(iRows,"Points matrix (arg 3) must be a 2 X N matrix"); + return 0; + } + Mat newPoint1(2, iCols, CV_64F); + for(i = 0; i < 2; i++) + for(j = 0; j < iCols; j++) + newPoint1.at(i,j) = pdblReal[ i + ( j * iRows )]; + + + /* ------------------------------------ Points matrix 2 :- Fourth argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + m = iCols; + if(iRows!=2){ + Scierror(iRows,"Points matrix (arg 4) must be a 2 X N matrix"); + return 0; + } + Mat newPoint2(iRows, iCols, CV_64F); + for(i = 0; i < iRows; i++) + for(j = 0; j < iCols; j++) + newPoint2.at(i,j) = pdblReal[ i + ( j * iRows )]; + + if(n!=m){ + Scierror(n,"No of columns of arg 3 and arg 4 must be same"); + return 0; + } + Mat new_image(4, n, CV_64F); + + triangulatePoints(projMat1, projMat2, newPoint1, newPoint2, new_image); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/opencv_undistort.cpp b/sci_gateway1/cpp/opencv_undistort.cpp new file mode 100644 index 0000000..da72d8e --- /dev/null +++ b/sci_gateway1/cpp/opencv_undistort.cpp @@ -0,0 +1,118 @@ +/*************************************************** +Author : Sukul Bagai, Shubheksha Jalan +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_undistort(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double cameraMatrix [3][3], newCameraMatrix [3][3]; + double *temp = NULL; + //checking input argument + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + Mat image; + retrieveImage(image, 1); + + //for camera matrix + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &temp); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(i=0;i<3;i++) + for(j=0;j<3;j++) + cameraMatrix[i][j]=temp[(j*3)+i]; + Mat cameraMat(3,3,CV_64F,&cameraMatrix); + +//for array of coefficients + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols, &temp); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n; + if(iRows==1) + n=iCols; + else + n=iRows; + + double distCoeffs[n]; + for(i=0;i +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C"{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_undistortImage(char *fname, unsigned long fname_len){ + + /// Error management variable + SciErr sciErr; + + /// Variables + int i, j, n = 0; + + int iRows = 0; + int iCols = 0; + int iRows1 = 0; + int iCols1 = 0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + double *pdblReal = NULL; + + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + + double cameraMatrix[3][3]; + double *newCameraMatrix = NULL; + double *distCoeffs = NULL; + + /// checking input argument + // 1-> image + // 2-> cameraMatrix + // 3-> distCoeffs + // 4-> newCameraMatrix + + CheckInputArgument(pvApiCtx, 2, 6); + CheckOutputArgument(pvApiCtx, 1, 1); + + /// Take distorted image + Mat image; + retrieveImage(image, 1); + + newCameraMatrix = (double*)malloc(sizeof(double) * 3 * 3); + memset(newCameraMatrix, 0, sizeof(newCameraMatrix)); + + /// Taking input for cameraMatrix + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + /// Take input in cameraMatrix + Mat _cameraMatrix(3, 3, CV_64F); + for(i = 0; i < 3; ++i) + for(j = 0;j < 3; ++j) + _cameraMatrix.at(i, j) = pdblReal[(j * 3) + i]; + + + int nbInputArguments = *getNbInputArgument(pvApiCtx); + + + providedArgs = (bool*) malloc(sizeof(bool) * 2); + memset(providedArgs, 0, 2); + + for(int iter = 3; iter <= nbInputArguments; ++iter){ + + /// Address of next argument + + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr3); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + // Three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int k = 0; k < iRows * iCols; ++k) + { + pstData[k] = (char*) malloc(sizeof(char) * piLen[k] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + + if(strcmp(currentArg, "distCoeffs") == 0){ + + if(iter+1 <= nbInputArguments and !providedArgs[0]){ + + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr4); + + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows1, &iCols1, &pdblReal); + + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + if(iRows1 == 1 or iCols1 == 1) + n = iCols1 * iRows1; + + else{ + Scierror(999, "Incorrect dimension of vector for argument\n"); + return 0; + } + + // if(n != 4 or n != 5 or n != 8){ + // Scierror(999, "Incorrect dimension of matrix for argument. Only size of 4, 5, or 8 required\n"); + // return 0; + // } + + distCoeffs = (double*)malloc(sizeof(double) * n); + + for(i = 0; i < n; ++i){ + distCoeffs[i] = pdblReal[i]; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[0] = 1; + } + // Send an error message if an argument is provided more than once. Same for all optional arguments. + + else if(providedArgs[0]){ + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + + // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + + else { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + else if(strcmp(currentArg, "newCameraMatrix") == 0){ + + if(iter+1 <= nbInputArguments and !providedArgs[1]){ + + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr4); + + if (sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows1, &iCols1, &newCameraMatrix); + + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + if(iRows1 !=3 and iCols1 != 3){ + Scierror(999, "Incorrect dimension of matrix for argument\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[1] = 1; + } + // Send an error message if an argument is provided more than once. Same for all optional arguments. + + else if(providedArgs[1]){ + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + + // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + + else { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else{ + sciprint("Invalid argument provided; Using defalult values\n"); + } + + } + + Mat _distCoeffs = Mat::zeros(1, n, CV_64F); + + /// if distCoeffs was provided else it will be NULL / empty + if(providedArgs[0]){ + for(i = 0; i < n; ++i) + _distCoeffs.at(0, i) = distCoeffs[i]; + } + + /// By default the _newCameraMatrix has the same value of CameraMatrix + Mat _newCameraMatrix(3, 3, CV_64F, &cameraMatrix); + + if(providedArgs[1]){ + for(i = 0; i < 3; ++i) + for(j = 0; j < 3; ++j) + _newCameraMatrix.at(i, j) = newCameraMatrix[j * 3 + i]; + } + + Mat new_image; + undistort(image, new_image, _cameraMatrix, _distCoeffs, _newCameraMatrix); + + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, new_image, 1); + free(checker); + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway1/cpp/opencv_undistortPoints.cpp b/sci_gateway1/cpp/opencv_undistortPoints.cpp new file mode 100644 index 0000000..dff80e4 --- /dev/null +++ b/sci_gateway1/cpp/opencv_undistortPoints.cpp @@ -0,0 +1,190 @@ +/*************************************************** +Author : Deepshikha +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_undistortPoints(char *fname, unsigned long fname_len) + { + //variables + int i,j,k,n,m; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + + double *pdblReal = NULL; + + SciErr sciErr; + + //function varible + std::vector< Point2d > Src; + std::vector< Point2d > dst; + double cameraMatrix [3][3]; + double newCameraMatrix [3][3]; + double *distCoeffs = NULL; + double RectificationMatrix[3][3]; + + /*------------------------------------------- Checking Input Argument ----------------------------------------------------*/ + + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + /*------------------------------------- Source matrix :- First argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + // For 1 X N single channel + for(i = 0; i < iRows; ++i) + Src.push_back( Point2d(pdblReal[(0 * iRows) + i], pdblReal[(1 * iRows) + i])); + + /* ------------------------------------ Camera matrix :- Second argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + Mat cameraMat(3, 3, CV_64F); + for(i=0;i<3;i++) + for(j=0;j<3;j++) + cameraMat.at(i,j) = pdblReal[(j * 3) + i]; + + + /* ------------------------------------ Distortion Coefficient matrix :- Third argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows== 1) + n = iCols; + else if (iCols == 1) + n = iRows; + else{ + Scierror(1,"Points matrix (arg 4) must be a 2 X N matrix"); + return 0; + } + if(n==4 or n==5 or n==8); + else{ + Scierror(1," n must be 4 or 5 or 8"); + return 0; + } + + Mat distCoeffsActual(1,n,CV_64F); + for(i=0;i(0,i) = pdblReal[i]; + + + /* ------------------------------------ Rectification matrix :- Fourth argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + Mat RectMat(3, 3, CV_64F); + for(i=0;i<3;i++) + for(j=0;j<3;j++) + RectMat.at(i,j) =pdblReal[(j*3)+i]; + + + /* ------------------------------------ New Camera matrix :- Fifth argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr5, &iRows, &iCols , &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + Mat newCameraMat(3,3,CV_64F); + for(i=0;i<3;i++) + for(j=0;j<3;j++) + newCameraMat.at(i, j) =pdblReal[(j*3)+i]; + + + + /* opencv function for undistortPoints */ + undistortPoints(Src, dst, cameraMat, distCoeffsActual, RectMat, newCameraMat); + + /* Writing to Scilab */ + double *pstdata = NULL; + pstdata = (double*)malloc(sizeof(double) * dst.size() * 2); + + for(i = 0; i < dst.size(); i++) + { + pstdata[i + 0 * dst.size()] = dst[i].x; + pstdata[i + 1 * dst.size()] = dst[i].y; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, dst.size(), 2, pstdata); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file diff --git a/sci_gateway1/cpp/rotationMatrixToVector.cpp b/sci_gateway1/cpp/rotationMatrixToVector.cpp new file mode 100644 index 0000000..40ef093 --- /dev/null +++ b/sci_gateway1/cpp/rotationMatrixToVector.cpp @@ -0,0 +1,139 @@ +/*************************************************** +Author : Shashank Shekhar +**************************************************/ + +#include +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + + int rotationMTV(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int *piAddr = NULL; + int iRows = 0; + int iCols = 0; + int *piLen = 0; + char **pstData = NULL; + double RotMat[9]; + double *temp = NULL; + int noOfarguments = *getNbInputArgument(pvApiCtx); + double outMat[3]; + double theta; + int i; + + + + CheckInputArgument(pvApiCtx,1,1); + CheckOutputArgument(pvApiCtx,1,1); + + sciErr = getVarAddressFromPosition(pvApiCtx,1,&piAddr); + if (sciErr.iErr) + { + printError(&scisErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999,"A 3x3 Matrix expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &temp); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=3 || iCols!=3) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + for(i=0;i<3;i++) + { + for(int j=0;j<3;j++) + { + RotMat[j*3+i]=temp[i*3+j]; + } + } + +// ----------------------------------------------------------- Evaluation -------------------------------------------------------- + + double myMat[3][3]; + for(int i=0;i<3;i++) + for(int j=0;j<3;j++) + myMat[i][j] = RotMat[i*3+j]; + + double trace = RotMat[0]+RotMat[4]+RotMat[8]; + theta = acos((trace-1)/2); + outMat[0] = myMat[2][1]-myMat[1][2]; + outMat[1] = myMat[0][2]-myMat[2][0]; + outMat[2] = myMat[1][0]-myMat[0][1]; + + double element; + double threshold = 1e-4; + if(sin(theta) >= threshold) + { + element = theta/(2*sin(theta)); + for(int i=0;i<3;i++) + outMat[i]=outMat[i]*element; + } + else if (theta-1 > 0) + { + element = (0.5-(theta-3)/12); + for(int i=0;i<3;i++) + outMat[i]=outMat[i]*element; + } + else + { + double myMax=myMat[0][0]; + int iter1=0; + int iter2,iter3; + for(int i=0;i<3;i++) + { + if(myMat[i][i]>myMax) + { + myMax=myMat[i][i]; + iter1=i; + } + } + + iter2 = (iter1%2) +1; + iter3 = ((iter1+1)%2) +1; + + double ev = sqrt(myMat[iter1][iter1] - myMat[iter2][iter2] - myMat[iter3][iter3] + 1); + outMat[0] = ev/2; + outMat[1] = (myMat[iter2][iter1] + myMat[iter1][iter2])/(2*ev); + outMat[2] = (myMat[iter3][iter1] + myMat[iter1][iter3])/(2*ev); + + for(int i=0;i<3;i++) + ev+=pow(outMat[i],2); + ev = sqrt(ev); + + for(int i=0;i<3;i++) + outMat[i]=(outMat[i]/ev)*theta; + } + +// --------------------------------------------------------Creating 1x3 Mat to Return ------------------------------------------------------------- + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3,1,outMat); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway1/loader_gateway.sce b/sci_gateway1/loader_gateway.sce new file mode 100644 index 0000000..6809d99 --- /dev/null +++ b/sci_gateway1/loader_gateway.sce @@ -0,0 +1,24 @@ +// This file is released under the 3-clause BSD license. See COPYING-BSD. +// Generated by builder_gateway.sce: Please, do not edit this file + +try + v = getversion("scilab"); +catch + v = [ 5 0 ]; // or older +end +if (v(1) <= 5) & (v(2) < 3) then + // new API in scilab 5.3 + error(gettext("Scilab 5.3 or more is required.")); +end + +sci_gateway_dir = get_absolute_file_path("loader_gateway.sce"); +current_dir = pwd(); + +chdir(sci_gateway_dir); +if ( isdir("cpp") ) then + chdir("cpp"); + exec("loader.sce"); +end + +chdir(current_dir); +clear sci_gateway_dir current_dir v; diff --git a/thirdparty/linux/include/opencv/cv.h b/thirdparty/linux/include/opencv/cv.h new file mode 100644 index 0000000..19a74e2 --- /dev/null +++ b/thirdparty/linux/include/opencv/cv.h @@ -0,0 +1,73 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CV_H +#define OPENCV_OLD_CV_H + +#if defined(_MSC_VER) + #define CV_DO_PRAGMA(x) __pragma(x) + #define __CVSTR2__(x) #x + #define __CVSTR1__(x) __CVSTR2__(x) + #define __CVMSVCLOC__ __FILE__ "("__CVSTR1__(__LINE__)") : " + #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (__CVMSVCLOC__ _msg)) +#elif defined(__GNUC__) + #define CV_DO_PRAGMA(x) _Pragma (#x) + #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (_msg)) +#else + #define CV_DO_PRAGMA(x) + #define CV_MSG_PRAGMA(_msg) +#endif +#define CV_WARNING(x) CV_MSG_PRAGMA("Warning: " #x) + +//CV_WARNING("This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module") + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#include "opencv2/photo/photo_c.h" +#include "opencv2/video/tracking_c.h" +#include "opencv2/objdetect/objdetect_c.h" + +#if !defined(CV_IMPL) +#define CV_IMPL extern "C" +#endif //CV_IMPL + +#endif // __OPENCV_OLD_CV_H_ diff --git a/thirdparty/linux/include/opencv/cv.hpp b/thirdparty/linux/include/opencv/cv.hpp new file mode 100644 index 0000000..8673956 --- /dev/null +++ b/thirdparty/linux/include/opencv/cv.hpp @@ -0,0 +1,60 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CV_HPP +#define OPENCV_OLD_CV_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cv.h" +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" +#include "opencv2/photo.hpp" +#include "opencv2/video.hpp" +#include "opencv2/highgui.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/calib3d.hpp" +#include "opencv2/objdetect.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv/cvaux.h b/thirdparty/linux/include/opencv/cvaux.h new file mode 100644 index 0000000..c0367cc --- /dev/null +++ b/thirdparty/linux/include/opencv/cvaux.h @@ -0,0 +1,57 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_AUX_H +#define OPENCV_OLD_AUX_H + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#include "opencv2/photo/photo_c.h" +#include "opencv2/video/tracking_c.h" +#include "opencv2/objdetect/objdetect_c.h" + +#endif + +/* End of file. */ diff --git a/thirdparty/linux/include/opencv/cvaux.hpp b/thirdparty/linux/include/opencv/cvaux.hpp new file mode 100644 index 0000000..4888eef --- /dev/null +++ b/thirdparty/linux/include/opencv/cvaux.hpp @@ -0,0 +1,52 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_AUX_HPP +#define OPENCV_OLD_AUX_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cvaux.h" +#include "opencv2/core/utility.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv/cvwimage.h b/thirdparty/linux/include/opencv/cvwimage.h new file mode 100644 index 0000000..ec0ab14 --- /dev/null +++ b/thirdparty/linux/include/opencv/cvwimage.h @@ -0,0 +1,46 @@ +/////////////////////////////////////////////////////////////////////////////// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to +// this license. If you do not agree to this license, do not download, +// install, copy or use the software. +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2008, Google, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation or contributors may not be used to endorse +// or promote products derived from this software without specific +// prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" +// and any express or implied warranties, including, but not limited to, the +// implied warranties of merchantability and fitness for a particular purpose +// are disclaimed. In no event shall the Intel Corporation or contributors be +// liable for any direct, indirect, incidental, special, exemplary, or +// consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. + + +#ifndef OPENCV_OLD_WIMAGE_HPP +#define OPENCV_OLD_WIMAGE_HPP + +#include "opencv2/core/wimage.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv/cxcore.h b/thirdparty/linux/include/opencv/cxcore.h new file mode 100644 index 0000000..dc070c7 --- /dev/null +++ b/thirdparty/linux/include/opencv/cxcore.h @@ -0,0 +1,52 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CXCORE_H +#define OPENCV_OLD_CXCORE_H + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "opencv2/core/core_c.h" + +#endif diff --git a/thirdparty/linux/include/opencv/cxcore.hpp b/thirdparty/linux/include/opencv/cxcore.hpp new file mode 100644 index 0000000..c371677 --- /dev/null +++ b/thirdparty/linux/include/opencv/cxcore.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CXCORE_HPP +#define OPENCV_OLD_CXCORE_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cxcore.h" +#include "opencv2/core.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv/cxeigen.hpp b/thirdparty/linux/include/opencv/cxeigen.hpp new file mode 100644 index 0000000..1d3df91 --- /dev/null +++ b/thirdparty/linux/include/opencv/cxeigen.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_EIGEN_HPP +#define OPENCV_OLD_EIGEN_HPP + +#include "opencv2/core/eigen.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv/cxmisc.h b/thirdparty/linux/include/opencv/cxmisc.h new file mode 100644 index 0000000..9b9bc82 --- /dev/null +++ b/thirdparty/linux/include/opencv/cxmisc.h @@ -0,0 +1,8 @@ +#ifndef OPENCV_OLD_CXMISC_H +#define OPENCV_OLD_CXMISC_H + +#ifdef __cplusplus +# include "opencv2/core/utility.hpp" +#endif + +#endif diff --git a/thirdparty/linux/include/opencv/highgui.h b/thirdparty/linux/include/opencv/highgui.h new file mode 100644 index 0000000..69b394e --- /dev/null +++ b/thirdparty/linux/include/opencv/highgui.h @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_HIGHGUI_H +#define OPENCV_OLD_HIGHGUI_H + +#include "opencv2/core/core_c.h" +#include "opencv2/highgui/highgui_c.h" + +#endif diff --git a/thirdparty/linux/include/opencv/ml.h b/thirdparty/linux/include/opencv/ml.h new file mode 100644 index 0000000..0c376ba --- /dev/null +++ b/thirdparty/linux/include/opencv/ml.h @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_ML_H +#define OPENCV_OLD_ML_H + +#include "opencv2/core/core_c.h" +#include "opencv2/ml.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv2/aruco.hpp b/thirdparty/linux/include/opencv2/aruco.hpp new file mode 100644 index 0000000..e9e88c5 --- /dev/null +++ b/thirdparty/linux/include/opencv2/aruco.hpp @@ -0,0 +1,541 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_ARUCO_HPP__ +#define __OPENCV_ARUCO_HPP__ + +#include +#include +#include "opencv2/aruco/dictionary.hpp" + +/** + * @defgroup aruco ArUco Marker Detection + * This module is dedicated to square fiducial markers (also known as Augmented Reality Markers) + * These markers are useful for easy, fast and robust camera pose estimation.ç + * + * The main functionalities are: + * - Detection of markers in a image + * - Pose estimation from a single marker or from a board/set of markers + * - Detection of ChArUco board for high subpixel accuracy + * - Camera calibration from both, ArUco boards and ChArUco boards. + * - Detection of ChArUco diamond markers + * The samples directory includes easy examples of how to use the module. + * + * The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado. + * + * @sa S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez. 2014. + * "Automatic generation and detection of highly reliable fiducial markers under occlusion". + * Pattern Recogn. 47, 6 (June 2014), 2280-2292. DOI=10.1016/j.patcog.2014.01.005 + * + * @sa http://www.uco.es/investiga/grupos/ava/node/26 + * + * This module has been originally developed by Sergio Garrido-Jurado as a project + * for Google Summer of Code 2015 (GSoC 15). + * + * +*/ + +namespace cv { +namespace aruco { + +//! @addtogroup aruco +//! @{ + + + +/** + * @brief Parameters for the detectMarker process: + * - adaptiveThreshWinSizeMin: minimum window size for adaptive thresholding before finding + * contours (default 3). + * - adaptiveThreshWinSizeMax: maximum window size for adaptive thresholding before finding + * contours (default 23). + * - adaptiveThreshWinSizeStep: increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax + * during the thresholding (default 10). + * - adaptiveThreshConstant: constant for adaptive thresholding before finding contours (default 7) + * - minMarkerPerimeterRate: determine minimum perimeter for marker contour to be detected. This + * is defined as a rate respect to the maximum dimension of the input image (default 0.03). + * - maxMarkerPerimeterRate: determine maximum perimeter for marker contour to be detected. This + * is defined as a rate respect to the maximum dimension of the input image (default 4.0). + * - polygonalApproxAccuracyRate: minimum accuracy during the polygonal approximation process to + * determine which contours are squares. + * - minCornerDistanceRate: minimum distance between corners for detected markers relative to its + * perimeter (default 0.05) + * - minDistanceToBorder: minimum distance of any corner to the image border for detected markers + * (in pixels) (default 3) + * - minMarkerDistanceRate: minimum mean distance beetween two marker corners to be considered + * similar, so that the smaller one is removed. The rate is relative to the smaller perimeter + * of the two markers (default 0.05). + * - doCornerRefinement: do subpixel refinement or not + * - cornerRefinementWinSize: window size for the corner refinement process (in pixels) (default 5). + * - cornerRefinementMaxIterations: maximum number of iterations for stop criteria of the corner + * refinement process (default 30). + * - cornerRefinementMinAccuracy: minimum error for the stop cristeria of the corner refinement + * process (default: 0.1) + * - markerBorderBits: number of bits of the marker border, i.e. marker border width (default 1). + * - perpectiveRemovePixelPerCell: number of bits (per dimension) for each cell of the marker + * when removing the perspective (default 8). + * - perspectiveRemoveIgnoredMarginPerCell: width of the margin of pixels on each cell not + * considered for the determination of the cell bit. Represents the rate respect to the total + * size of the cell, i.e. perpectiveRemovePixelPerCell (default 0.13) + * - maxErroneousBitsInBorderRate: maximum number of accepted erroneous bits in the border (i.e. + * number of allowed white bits in the border). Represented as a rate respect to the total + * number of bits per marker (default 0.35). + * - minOtsuStdDev: minimun standard deviation in pixels values during the decodification step to + * apply Otsu thresholding (otherwise, all the bits are set to 0 or 1 depending on mean higher + * than 128 or not) (default 5.0) + * - errorCorrectionRate error correction rate respect to the maximun error correction capability + * for each dictionary. (default 0.6). + */ +struct CV_EXPORTS_W DetectorParameters { + + DetectorParameters(); + + CV_WRAP static Ptr create(); + + CV_PROP_RW int adaptiveThreshWinSizeMin; + CV_PROP_RW int adaptiveThreshWinSizeMax; + CV_PROP_RW int adaptiveThreshWinSizeStep; + CV_PROP_RW double adaptiveThreshConstant; + CV_PROP_RW double minMarkerPerimeterRate; + CV_PROP_RW double maxMarkerPerimeterRate; + CV_PROP_RW double polygonalApproxAccuracyRate; + CV_PROP_RW double minCornerDistanceRate; + CV_PROP_RW int minDistanceToBorder; + CV_PROP_RW double minMarkerDistanceRate; + CV_PROP_RW bool doCornerRefinement; + CV_PROP_RW int cornerRefinementWinSize; + CV_PROP_RW int cornerRefinementMaxIterations; + CV_PROP_RW double cornerRefinementMinAccuracy; + CV_PROP_RW int markerBorderBits; + CV_PROP_RW int perspectiveRemovePixelPerCell; + CV_PROP_RW double perspectiveRemoveIgnoredMarginPerCell; + CV_PROP_RW double maxErroneousBitsInBorderRate; + CV_PROP_RW double minOtsuStdDev; + CV_PROP_RW double errorCorrectionRate; +}; + + + +/** + * @brief Basic marker detection + * + * @param image input image + * @param dictionary indicates the type of markers that will be searched + * @param corners vector of detected marker corners. For each marker, its four corners + * are provided, (e.g std::vector > ). For N detected markers, + * the dimensions of this array is Nx4. The order of the corners is clockwise. + * @param ids vector of identifiers of the detected markers. The identifier is of type int + * (e.g. std::vector). For N detected markers, the size of ids is also N. + * The identifiers have the same order than the markers in the imgPoints array. + * @param parameters marker detection parameters + * @param rejectedImgPoints contains the imgPoints of those squares whose inner code has not a + * correct codification. Useful for debugging purposes. + * + * Performs marker detection in the input image. Only markers included in the specific dictionary + * are searched. For each detected marker, it returns the 2D position of its corner in the image + * and its corresponding identifier. + * Note that this function does not perform pose estimation. + * @sa estimatePoseSingleMarkers, estimatePoseBoard + * + */ +CV_EXPORTS_W void detectMarkers(InputArray image, const Ptr &dictionary, OutputArrayOfArrays corners, + OutputArray ids, const Ptr ¶meters = DetectorParameters::create(), + OutputArrayOfArrays rejectedImgPoints = noArray()); + + + +/** + * @brief Pose estimation for single markers + * + * @param corners vector of already detected markers corners. For each marker, its four corners + * are provided, (e.g std::vector > ). For N detected markers, + * the dimensions of this array should be Nx4. The order of the corners should be clockwise. + * @sa detectMarkers + * @param markerLength the length of the markers' side. The returning translation vectors will + * be in the same unit. Normally, unit is meters. + * @param cameraMatrix input 3x3 floating-point camera matrix + * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ + * @param distCoeffs vector of distortion coefficients + * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements + * @param rvecs array of output rotation vectors (@sa Rodrigues) (e.g. std::vector). + * Each element in rvecs corresponds to the specific marker in imgPoints. + * @param tvecs array of output translation vectors (e.g. std::vector). + * Each element in tvecs corresponds to the specific marker in imgPoints. + * + * This function receives the detected markers and returns their pose estimation respect to + * the camera individually. So for each marker, one rotation and translation vector is returned. + * The returned transformation is the one that transforms points from each marker coordinate system + * to the camera coordinate system. + * The marker corrdinate system is centered on the middle of the marker, with the Z axis + * perpendicular to the marker plane. + * The coordinates of the four corners of the marker in its own coordinate system are: + * (-markerLength/2, markerLength/2, 0), (markerLength/2, markerLength/2, 0), + * (markerLength/2, -markerLength/2, 0), (-markerLength/2, -markerLength/2, 0) + */ +CV_EXPORTS_W void estimatePoseSingleMarkers(InputArrayOfArrays corners, float markerLength, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvecs, OutputArray tvecs); + + + +/** + * @brief Board of markers + * + * A board is a set of markers in the 3D space with a common cordinate system. + * The common form of a board of marker is a planar (2D) board, however any 3D layout can be used. + * A Board object is composed by: + * - The object points of the marker corners, i.e. their coordinates respect to the board system. + * - The dictionary which indicates the type of markers of the board + * - The identifier of all the markers in the board. + */ +class CV_EXPORTS_W Board { + + public: + /** + * @brief Provide way to create Board by passing nessesary data. Specially needed in Python. + * + * @param objPoints array of object points of all the marker corners in the board + * @param dictionary the dictionary of markers employed for this board + * @param ids vector of the identifiers of the markers in the board + * + */ + CV_WRAP static Ptr create(InputArrayOfArrays objPoints, const Ptr &dictionary, InputArray ids); + /// array of object points of all the marker corners in the board + /// each marker include its 4 corners in CCW order. For M markers, the size is Mx4. + CV_PROP std::vector< std::vector< Point3f > > objPoints; + + /// the dictionary of markers employed for this board + CV_PROP Ptr dictionary; + + /// vector of the identifiers of the markers in the board (same size than objPoints) + /// The identifiers refers to the board dictionary + CV_PROP std::vector< int > ids; +}; + + + +/** + * @brief Planar board with grid arrangement of markers + * More common type of board. All markers are placed in the same plane in a grid arrangment. + * The board can be drawn using drawPlanarBoard() function (@sa drawPlanarBoard) + */ +class CV_EXPORTS_W GridBoard : public Board { + + public: + /** + * @brief Draw a GridBoard + * + * @param outSize size of the output image in pixels. + * @param img output image with the board. The size of this image will be outSize + * and the board will be on the center, keeping the board proportions. + * @param marginSize minimum margins (in pixels) of the board in the output image + * @param borderBits width of the marker borders. + * + * This function return the image of the GridBoard, ready to be printed. + */ + CV_WRAP void draw(Size outSize, OutputArray img, int marginSize = 0, int borderBits = 1); + + + /** + * @brief Create a GridBoard object + * + * @param markersX number of markers in X direction + * @param markersY number of markers in Y direction + * @param markerLength marker side length (normally in meters) + * @param markerSeparation separation between two markers (same unit as markerLength) + * @param dictionary dictionary of markers indicating the type of markers + * @param firstMarker id of first marker in dictionary to use on board. + * @return the output GridBoard object + * + * This functions creates a GridBoard object given the number of markers in each direction and + * the marker size and marker separation. + */ + CV_WRAP static Ptr create(int markersX, int markersY, float markerLength, + float markerSeparation, const Ptr &dictionary, int firstMarker = 0); + + /** + * + */ + CV_WRAP Size getGridSize() const { return Size(_markersX, _markersY); } + + /** + * + */ + CV_WRAP float getMarkerLength() const { return _markerLength; } + + /** + * + */ + CV_WRAP float getMarkerSeparation() const { return _markerSeparation; } + + + private: + // number of markers in X and Y directions + int _markersX, _markersY; + + // marker side lenght (normally in meters) + float _markerLength; + + // separation between markers in the grid + float _markerSeparation; +}; + + + +/** + * @brief Pose estimation for a board of markers + * + * @param corners vector of already detected markers corners. For each marker, its four corners + * are provided, (e.g std::vector > ). For N detected markers, the + * dimensions of this array should be Nx4. The order of the corners should be clockwise. + * @param ids list of identifiers for each marker in corners + * @param board layout of markers in the board. The layout is composed by the marker identifiers + * and the positions of each marker corner in the board reference system. + * @param cameraMatrix input 3x3 floating-point camera matrix + * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ + * @param distCoeffs vector of distortion coefficients + * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements + * @param rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board + * (@sa Rodrigues). Used as initial guess if not empty. + * @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board. + * Used as initial guess if not empty. + * + * This function receives the detected markers and returns the pose of a marker board composed + * by those markers. + * A Board of marker has a single world coordinate system which is defined by the board layout. + * The returned transformation is the one that transforms points from the board coordinate system + * to the camera coordinate system. + * Input markers that are not included in the board layout are ignored. + * The function returns the number of markers from the input employed for the board pose estimation. + * Note that returning a 0 means the pose has not been estimated. + */ +CV_EXPORTS_W int estimatePoseBoard(InputArrayOfArrays corners, InputArray ids, const Ptr &board, + InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, + OutputArray tvec); + + + + +/** + * @brief Refind not detected markers based on the already detected and the board layout + * + * @param image input image + * @param board layout of markers in the board. + * @param detectedCorners vector of already detected marker corners. + * @param detectedIds vector of already detected marker identifiers. + * @param rejectedCorners vector of rejected candidates during the marker detection process. + * @param cameraMatrix optional input 3x3 floating-point camera matrix + * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ + * @param distCoeffs optional vector of distortion coefficients + * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements + * @param minRepDistance minimum distance between the corners of the rejected candidate and the + * reprojected marker in order to consider it as a correspondence. + * @param errorCorrectionRate rate of allowed erroneous bits respect to the error correction + * capability of the used dictionary. -1 ignores the error correction step. + * @param checkAllOrders Consider the four posible corner orders in the rejectedCorners array. + * If it set to false, only the provided corner order is considered (default true). + * @param recoveredIdxs Optional array to returns the indexes of the recovered candidates in the + * original rejectedCorners array. + * @param parameters marker detection parameters + * + * This function tries to find markers that were not detected in the basic detecMarkers function. + * First, based on the current detected marker and the board layout, the function interpolates + * the position of the missing markers. Then it tries to find correspondence between the reprojected + * markers and the rejected candidates based on the minRepDistance and errorCorrectionRate + * parameters. + * If camera parameters and distortion coefficients are provided, missing markers are reprojected + * using projectPoint function. If not, missing marker projections are interpolated using global + * homography, and all the marker corners in the board must have the same Z coordinate. + */ +CV_EXPORTS_W void refineDetectedMarkers( + InputArray image,const Ptr &board, InputOutputArrayOfArrays detectedCorners, + InputOutputArray detectedIds, InputOutputArrayOfArrays rejectedCorners, + InputArray cameraMatrix = noArray(), InputArray distCoeffs = noArray(), + float minRepDistance = 10.f, float errorCorrectionRate = 3.f, bool checkAllOrders = true, + OutputArray recoveredIdxs = noArray(), const Ptr ¶meters = DetectorParameters::create()); + + + +/** + * @brief Draw detected markers in image + * + * @param image input/output image. It must have 1 or 3 channels. The number of channels is not + * altered. + * @param corners positions of marker corners on input image. + * (e.g std::vector > ). For N detected markers, the dimensions of + * this array should be Nx4. The order of the corners should be clockwise. + * @param ids vector of identifiers for markers in markersCorners . + * Optional, if not provided, ids are not painted. + * @param borderColor color of marker borders. Rest of colors (text color and first corner color) + * are calculated based on this one to improve visualization. + * + * Given an array of detected marker corners and its corresponding ids, this functions draws + * the markers in the image. The marker borders are painted and the markers identifiers if provided. + * Useful for debugging purposes. + */ +CV_EXPORTS_W void drawDetectedMarkers(InputOutputArray image, InputArrayOfArrays corners, + InputArray ids = noArray(), + Scalar borderColor = Scalar(0, 255, 0)); + + + +/** + * @brief Draw coordinate system axis from pose estimation + * + * @param image input/output image. It must have 1 or 3 channels. The number of channels is not + * altered. + * @param cameraMatrix input 3x3 floating-point camera matrix + * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ + * @param distCoeffs vector of distortion coefficients + * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements + * @param rvec rotation vector of the coordinate system that will be drawn. (@sa Rodrigues). + * @param tvec translation vector of the coordinate system that will be drawn. + * @param length length of the painted axis in the same unit than tvec (usually in meters) + * + * Given the pose estimation of a marker or board, this function draws the axis of the world + * coordinate system, i.e. the system centered on the marker/board. Useful for debugging purposes. + */ +CV_EXPORTS_W void drawAxis(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs, + InputArray rvec, InputArray tvec, float length); + + + +/** + * @brief Draw a canonical marker image + * + * @param dictionary dictionary of markers indicating the type of markers + * @param id identifier of the marker that will be returned. It has to be a valid id + * in the specified dictionary. + * @param sidePixels size of the image in pixels + * @param img output image with the marker + * @param borderBits width of the marker border. + * + * This function returns a marker image in its canonical form (i.e. ready to be printed) + */ +CV_EXPORTS_W void drawMarker(const Ptr &dictionary, int id, int sidePixels, OutputArray img, + int borderBits = 1); + + + +/** + * @brief Draw a planar board + * @sa _drawPlanarBoardImpl + * + * @param board layout of the board that will be drawn. The board should be planar, + * z coordinate is ignored + * @param outSize size of the output image in pixels. + * @param img output image with the board. The size of this image will be outSize + * and the board will be on the center, keeping the board proportions. + * @param marginSize minimum margins (in pixels) of the board in the output image + * @param borderBits width of the marker borders. + * + * This function return the image of a planar board, ready to be printed. It assumes + * the Board layout specified is planar by ignoring the z coordinates of the object points. + */ +CV_EXPORTS_W void drawPlanarBoard(const Ptr &board, Size outSize, OutputArray img, + int marginSize = 0, int borderBits = 1); + + + +/** + * @brief Implementation of drawPlanarBoard that accepts a raw Board pointer. + */ +void _drawPlanarBoardImpl(Board *board, Size outSize, OutputArray img, + int marginSize = 0, int borderBits = 1); + + + +/** + * @brief Calibrate a camera using aruco markers + * + * @param corners vector of detected marker corners in all frames. + * The corners should have the same format returned by detectMarkers (see #detectMarkers). + * @param ids list of identifiers for each marker in corners + * @param counter number of markers in each frame so that corners and ids can be split + * @param board Marker Board layout + * @param imageSize Size of the image used only to initialize the intrinsic camera matrix. + * @param cameraMatrix Output 3x3 floating-point camera matrix + * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS + * and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be + * initialized before calling the function. + * @param distCoeffs Output vector of distortion coefficients + * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements + * @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view + * (e.g. std::vector>). That is, each k-th rotation vector together with the corresponding + * k-th translation vector (see the next output parameter description) brings the board pattern + * from the model coordinate space (in which object points are specified) to the world coordinate + * space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1). + * @param tvecs Output vector of translation vectors estimated for each pattern view. + * @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. + * Order of deviations values: + * \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, + * s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero. + * @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. + * Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views, + * \f$R_i, T_i\f$ are concatenated 1x3 vectors. + * @param perViewErrors Output vector of average re-projection errors estimated for each pattern view. + * @param flags flags Different flags for the calibration process (see #calibrateCamera for details). + * @param criteria Termination criteria for the iterative optimization algorithm. + * + * This function calibrates a camera using an Aruco Board. The function receives a list of + * detected markers from several views of the Board. The process is similar to the chessboard + * calibration in calibrateCamera(). The function returns the final re-projection error. + */ +CV_EXPORTS_AS(calibrateCameraArucoExtended) double calibrateCameraAruco( + InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr &board, + Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, + OutputArray perViewErrors, int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)); + + +/** @brief It's the same function as #calibrateCameraAruco but without calibration error estimation. + */ +CV_EXPORTS_W double calibrateCameraAruco( + InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr &board, + Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs = noArray(), OutputArrayOfArrays tvecs = noArray(), int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)); + + +//! @} +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/aruco/charuco.hpp b/thirdparty/linux/include/opencv2/aruco/charuco.hpp new file mode 100644 index 0000000..7be82ed --- /dev/null +++ b/thirdparty/linux/include/opencv2/aruco/charuco.hpp @@ -0,0 +1,341 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_CHARUCO_HPP__ +#define __OPENCV_CHARUCO_HPP__ + +#include +#include +#include + + +namespace cv { +namespace aruco { + +//! @addtogroup aruco +//! @{ + + +/** + * @brief ChArUco board + * Specific class for ChArUco boards. A ChArUco board is a planar board where the markers are placed + * inside the white squares of a chessboard. The benefits of ChArUco boards is that they provide + * both, ArUco markers versatility and chessboard corner precision, which is important for + * calibration and pose estimation. + * This class also allows the easy creation and drawing of ChArUco boards. + */ +class CV_EXPORTS_W CharucoBoard : public Board { + + public: + // vector of chessboard 3D corners precalculated + CV_PROP std::vector< Point3f > chessboardCorners; + + // for each charuco corner, nearest marker id and nearest marker corner id of each marker + CV_PROP std::vector< std::vector< int > > nearestMarkerIdx; + CV_PROP std::vector< std::vector< int > > nearestMarkerCorners; + + /** + * @brief Draw a ChArUco board + * + * @param outSize size of the output image in pixels. + * @param img output image with the board. The size of this image will be outSize + * and the board will be on the center, keeping the board proportions. + * @param marginSize minimum margins (in pixels) of the board in the output image + * @param borderBits width of the marker borders. + * + * This function return the image of the ChArUco board, ready to be printed. + */ + CV_WRAP void draw(Size outSize, OutputArray img, int marginSize = 0, int borderBits = 1); + + + /** + * @brief Create a CharucoBoard object + * + * @param squaresX number of chessboard squares in X direction + * @param squaresY number of chessboard squares in Y direction + * @param squareLength chessboard square side length (normally in meters) + * @param markerLength marker side length (same unit than squareLength) + * @param dictionary dictionary of markers indicating the type of markers. + * The first markers in the dictionary are used to fill the white chessboard squares. + * @return the output CharucoBoard object + * + * This functions creates a CharucoBoard object given the number of squares in each direction + * and the size of the markers and chessboard squares. + */ + CV_WRAP static Ptr create(int squaresX, int squaresY, float squareLength, + float markerLength, const Ptr &dictionary); + + /** + * + */ + CV_WRAP Size getChessboardSize() const { return Size(_squaresX, _squaresY); } + + /** + * + */ + CV_WRAP float getSquareLength() const { return _squareLength; } + + /** + * + */ + CV_WRAP float getMarkerLength() const { return _markerLength; } + + private: + void _getNearestMarkerCorners(); + + // number of markers in X and Y directions + int _squaresX, _squaresY; + + // size of chessboard squares side (normally in meters) + float _squareLength; + + // marker side lenght (normally in meters) + float _markerLength; +}; + + + + +/** + * @brief Interpolate position of ChArUco board corners + * @param markerCorners vector of already detected markers corners. For each marker, its four + * corners are provided, (e.g std::vector > ). For N detected markers, the + * dimensions of this array should be Nx4. The order of the corners should be clockwise. + * @param markerIds list of identifiers for each marker in corners + * @param image input image necesary for corner refinement. Note that markers are not detected and + * should be sent in corners and ids parameters. + * @param board layout of ChArUco board. + * @param charucoCorners interpolated chessboard corners + * @param charucoIds interpolated chessboard corners identifiers + * @param cameraMatrix optional 3x3 floating-point camera matrix + * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ + * @param distCoeffs optional vector of distortion coefficients + * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements + * @param minMarkers number of adjacent markers that must be detected to return a charuco corner + * + * This function receives the detected markers and returns the 2D position of the chessboard corners + * from a ChArUco board using the detected Aruco markers. If camera parameters are provided, + * the process is based in an approximated pose estimation, else it is based on local homography. + * Only visible corners are returned. For each corner, its corresponding identifier is + * also returned in charucoIds. + * The function returns the number of interpolated corners. + */ +CV_EXPORTS_W int interpolateCornersCharuco(InputArrayOfArrays markerCorners, InputArray markerIds, + InputArray image, const Ptr &board, + OutputArray charucoCorners, OutputArray charucoIds, + InputArray cameraMatrix = noArray(), + InputArray distCoeffs = noArray(), int minMarkers = 2); + + + + +/** + * @brief Pose estimation for a ChArUco board given some of their corners + * @param charucoCorners vector of detected charuco corners + * @param charucoIds list of identifiers for each corner in charucoCorners + * @param board layout of ChArUco board. + * @param cameraMatrix input 3x3 floating-point camera matrix + * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ + * @param distCoeffs vector of distortion coefficients + * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements + * @param rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board + * (@sa Rodrigues). + * @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board. + * + * This function estimates a Charuco board pose from some detected corners. + * The function checks if the input corners are enough and valid to perform pose estimation. + * If pose estimation is valid, returns true, else returns false. + */ +CV_EXPORTS_W bool estimatePoseCharucoBoard(InputArray charucoCorners, InputArray charucoIds, + const Ptr &board, InputArray cameraMatrix, + InputArray distCoeffs, OutputArray rvec, OutputArray tvec); + + + + +/** + * @brief Draws a set of Charuco corners + * @param image input/output image. It must have 1 or 3 channels. The number of channels is not + * altered. + * @param charucoCorners vector of detected charuco corners + * @param charucoIds list of identifiers for each corner in charucoCorners + * @param cornerColor color of the square surrounding each corner + * + * This function draws a set of detected Charuco corners. If identifiers vector is provided, it also + * draws the id of each corner. + */ +CV_EXPORTS_W void drawDetectedCornersCharuco(InputOutputArray image, InputArray charucoCorners, + InputArray charucoIds = noArray(), + Scalar cornerColor = Scalar(255, 0, 0)); + + + +/** + * @brief Calibrate a camera using Charuco corners + * + * @param charucoCorners vector of detected charuco corners per frame + * @param charucoIds list of identifiers for each corner in charucoCorners per frame + * @param board Marker Board layout + * @param imageSize input image size + * @param cameraMatrix Output 3x3 floating-point camera matrix + * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS + * and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be + * initialized before calling the function. + * @param distCoeffs Output vector of distortion coefficients + * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements + * @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view + * (e.g. std::vector>). That is, each k-th rotation vector together with the corresponding + * k-th translation vector (see the next output parameter description) brings the board pattern + * from the model coordinate space (in which object points are specified) to the world coordinate + * space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1). + * @param tvecs Output vector of translation vectors estimated for each pattern view. + * @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. + * Order of deviations values: + * \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, + * s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero. + * @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. + * Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views, + * \f$R_i, T_i\f$ are concatenated 1x3 vectors. + * @param perViewErrors Output vector of average re-projection errors estimated for each pattern view. + * @param flags flags Different flags for the calibration process (see #calibrateCamera for details). + * @param criteria Termination criteria for the iterative optimization algorithm. + * + * This function calibrates a camera using a set of corners of a Charuco Board. The function + * receives a list of detected corners and its identifiers from several views of the Board. + * The function returns the final re-projection error. + */ +CV_EXPORTS_AS(calibrateCameraCharucoExtended) double calibrateCameraCharuco( + InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, const Ptr &board, + Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, + OutputArray perViewErrors, int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)); + +/** @brief It's the same function as #calibrateCameraCharuco but without calibration error estimation. +*/ +CV_EXPORTS_W double calibrateCameraCharuco( + InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, const Ptr &board, + Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs = noArray(), OutputArrayOfArrays tvecs = noArray(), int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)); + + + +/** + * @brief Detect ChArUco Diamond markers + * + * @param image input image necessary for corner subpixel. + * @param markerCorners list of detected marker corners from detectMarkers function. + * @param markerIds list of marker ids in markerCorners. + * @param squareMarkerLengthRate rate between square and marker length: + * squareMarkerLengthRate = squareLength/markerLength. The real units are not necessary. + * @param diamondCorners output list of detected diamond corners (4 corners per diamond). The order + * is the same than in marker corners: top left, top right, bottom right and bottom left. Similar + * format than the corners returned by detectMarkers (e.g std::vector > ). + * @param diamondIds ids of the diamonds in diamondCorners. The id of each diamond is in fact of + * type Vec4i, so each diamond has 4 ids, which are the ids of the aruco markers composing the + * diamond. + * @param cameraMatrix Optional camera calibration matrix. + * @param distCoeffs Optional camera distortion coefficients. + * + * This function detects Diamond markers from the previous detected ArUco markers. The diamonds + * are returned in the diamondCorners and diamondIds parameters. If camera calibration parameters + * are provided, the diamond search is based on reprojection. If not, diamond search is based on + * homography. Homography is faster than reprojection but can slightly reduce the detection rate. + */ +CV_EXPORTS_W void detectCharucoDiamond(InputArray image, InputArrayOfArrays markerCorners, + InputArray markerIds, float squareMarkerLengthRate, + OutputArrayOfArrays diamondCorners, OutputArray diamondIds, + InputArray cameraMatrix = noArray(), + InputArray distCoeffs = noArray()); + + + +/** + * @brief Draw a set of detected ChArUco Diamond markers + * + * @param image input/output image. It must have 1 or 3 channels. The number of channels is not + * altered. + * @param diamondCorners positions of diamond corners in the same format returned by + * detectCharucoDiamond(). (e.g std::vector > ). For N detected markers, + * the dimensions of this array should be Nx4. The order of the corners should be clockwise. + * @param diamondIds vector of identifiers for diamonds in diamondCorners, in the same format + * returned by detectCharucoDiamond() (e.g. std::vector). + * Optional, if not provided, ids are not painted. + * @param borderColor color of marker borders. Rest of colors (text color and first corner color) + * are calculated based on this one. + * + * Given an array of detected diamonds, this functions draws them in the image. The marker borders + * are painted and the markers identifiers if provided. + * Useful for debugging purposes. + */ +CV_EXPORTS_W void drawDetectedDiamonds(InputOutputArray image, InputArrayOfArrays diamondCorners, + InputArray diamondIds = noArray(), + Scalar borderColor = Scalar(0, 0, 255)); + + + + +/** + * @brief Draw a ChArUco Diamond marker + * + * @param dictionary dictionary of markers indicating the type of markers. + * @param ids list of 4 ids for each ArUco marker in the ChArUco marker. + * @param squareLength size of the chessboard squares in pixels. + * @param markerLength size of the markers in pixels. + * @param img output image with the marker. The size of this image will be + * 3*squareLength + 2*marginSize,. + * @param marginSize minimum margins (in pixels) of the marker in the output image + * @param borderBits width of the marker borders. + * + * This function return the image of a ChArUco marker, ready to be printed. + */ +// TODO cannot be exported yet; conversion from/to Vec4i is not wrapped in core +CV_EXPORTS void drawCharucoDiamond(const Ptr &dictionary, Vec4i ids, int squareLength, + int markerLength, OutputArray img, int marginSize = 0, + int borderBits = 1); + + + + +//! @} +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/aruco/dictionary.hpp b/thirdparty/linux/include/opencv2/aruco/dictionary.hpp new file mode 100644 index 0000000..b94ee25 --- /dev/null +++ b/thirdparty/linux/include/opencv2/aruco/dictionary.hpp @@ -0,0 +1,205 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_DICTIONARY_HPP__ +#define __OPENCV_DICTIONARY_HPP__ + +#include + +namespace cv { +namespace aruco { + +//! @addtogroup aruco +//! @{ + + +/** + * @brief Dictionary/Set of markers. It contains the inner codification + * + * bytesList contains the marker codewords where + * - bytesList.rows is the dictionary size + * - each marker is encoded using `nbytes = ceil(markerSize*markerSize/8.)` + * - each row contains all 4 rotations of the marker, so its length is `4*nbytes` + * + * `bytesList.ptr(i)[k*nbytes + j]` is then the j-th byte of i-th marker, in its k-th rotation. + */ +class CV_EXPORTS_W Dictionary { + + public: + CV_PROP Mat bytesList; // marker code information + CV_PROP int markerSize; // number of bits per dimension + CV_PROP int maxCorrectionBits; // maximum number of bits that can be corrected + + + /** + */ + Dictionary(const Mat &_bytesList = Mat(), int _markerSize = 0, int _maxcorr = 0); + + + /** + Dictionary(const Dictionary &_dictionary); + */ + + + /** + */ + Dictionary(const Ptr &_dictionary); + + + /** + * @see generateCustomDictionary + */ + CV_WRAP_AS(create) static Ptr create(int nMarkers, int markerSize); + + + /** + * @see generateCustomDictionary + */ + CV_WRAP_AS(create_from) static Ptr create(int nMarkers, int markerSize, + const Ptr &baseDictionary); + + /** + * @see getPredefinedDictionary + */ + CV_WRAP static Ptr get(int dict); + + /** + * @brief Given a matrix of bits. Returns whether if marker is identified or not. + * It returns by reference the correct id (if any) and the correct rotation + */ + bool identify(const Mat &onlyBits, int &idx, int &rotation, double maxCorrectionRate) const; + + /** + * @brief Returns the distance of the input bits to the specific id. If allRotations is true, + * the four posible bits rotation are considered + */ + int getDistanceToId(InputArray bits, int id, bool allRotations = true) const; + + + /** + * @brief Draw a canonical marker image + */ + CV_WRAP void drawMarker(int id, int sidePixels, OutputArray _img, int borderBits = 1) const; + + + /** + * @brief Transform matrix of bits to list of bytes in the 4 rotations + */ + static Mat getByteListFromBits(const Mat &bits); + + + /** + * @brief Transform list of bytes to matrix of bits + */ + static Mat getBitsFromByteList(const Mat &byteList, int markerSize); +}; + + + + +/** + * @brief Predefined markers dictionaries/sets + * Each dictionary indicates the number of bits and the number of markers contained + * - DICT_ARUCO_ORIGINAL: standard ArUco Library Markers. 1024 markers, 5x5 bits, 0 minimum + distance + */ +enum CV_EXPORTS_W_SIMPLE PREDEFINED_DICTIONARY_NAME { + DICT_4X4_50 = 0, + DICT_4X4_100, + DICT_4X4_250, + DICT_4X4_1000, + DICT_5X5_50, + DICT_5X5_100, + DICT_5X5_250, + DICT_5X5_1000, + DICT_6X6_50, + DICT_6X6_100, + DICT_6X6_250, + DICT_6X6_1000, + DICT_7X7_50, + DICT_7X7_100, + DICT_7X7_250, + DICT_7X7_1000, + DICT_ARUCO_ORIGINAL +}; + + +/** + * @brief Returns one of the predefined dictionaries defined in PREDEFINED_DICTIONARY_NAME + */ +CV_EXPORTS Ptr getPredefinedDictionary(PREDEFINED_DICTIONARY_NAME name); + + +/** + * @brief Returns one of the predefined dictionaries referenced by DICT_*. + */ +CV_EXPORTS_W Ptr getPredefinedDictionary(int dict); + + +/** + * @see generateCustomDictionary + */ +CV_EXPORTS_AS(custom_dictionary) Ptr generateCustomDictionary( + int nMarkers, + int markerSize); + + +/** + * @brief Generates a new customizable marker dictionary + * + * @param nMarkers number of markers in the dictionary + * @param markerSize number of bits per dimension of each markers + * @param baseDictionary Include the markers in this dictionary at the beginning (optional) + * + * This function creates a new dictionary composed by nMarkers markers and each markers composed + * by markerSize x markerSize bits. If baseDictionary is provided, its markers are directly + * included and the rest are generated based on them. If the size of baseDictionary is higher + * than nMarkers, only the first nMarkers in baseDictionary are taken and no new marker is added. + */ +CV_EXPORTS_AS(custom_dictionary_from) Ptr generateCustomDictionary( + int nMarkers, + int markerSize, + const Ptr &baseDictionary); + + + +//! @} +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/bgsegm.hpp b/thirdparty/linux/include/opencv2/bgsegm.hpp new file mode 100644 index 0000000..5a4ae3f --- /dev/null +++ b/thirdparty/linux/include/opencv2/bgsegm.hpp @@ -0,0 +1,194 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_BGSEGM_HPP__ +#define __OPENCV_BGSEGM_HPP__ + +#include "opencv2/video.hpp" + +#ifdef __cplusplus + +/** @defgroup bgsegm Improved Background-Foreground Segmentation Methods +*/ + +namespace cv +{ +namespace bgsegm +{ + +//! @addtogroup bgsegm +//! @{ + +/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm. + +The class implements the algorithm described in @cite KB2001 . + */ +class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor +{ +public: + CV_WRAP virtual int getHistory() const = 0; + CV_WRAP virtual void setHistory(int nframes) = 0; + + CV_WRAP virtual int getNMixtures() const = 0; + CV_WRAP virtual void setNMixtures(int nmix) = 0; + + CV_WRAP virtual double getBackgroundRatio() const = 0; + CV_WRAP virtual void setBackgroundRatio(double backgroundRatio) = 0; + + CV_WRAP virtual double getNoiseSigma() const = 0; + CV_WRAP virtual void setNoiseSigma(double noiseSigma) = 0; +}; + +/** @brief Creates mixture-of-gaussian background subtractor + +@param history Length of the history. +@param nmixtures Number of Gaussian mixtures. +@param backgroundRatio Background ratio. +@param noiseSigma Noise strength (standard deviation of the brightness or each color channel). 0 +means some automatic value. + */ +CV_EXPORTS_W Ptr + createBackgroundSubtractorMOG(int history=200, int nmixtures=5, + double backgroundRatio=0.7, double noiseSigma=0); + + +/** @brief Background Subtractor module based on the algorithm given in @cite Gold2012 . + + Takes a series of images and returns a sequence of mask (8UC1) + images of the same size, where 255 indicates Foreground and 0 represents Background. + This class implements an algorithm described in "Visual Tracking of Human Visitors under + Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere, + A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012. + */ +class CV_EXPORTS_W BackgroundSubtractorGMG : public BackgroundSubtractor +{ +public: + /** @brief Returns total number of distinct colors to maintain in histogram. + */ + CV_WRAP virtual int getMaxFeatures() const = 0; + /** @brief Sets total number of distinct colors to maintain in histogram. + */ + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + + /** @brief Returns the learning rate of the algorithm. + + It lies between 0.0 and 1.0. It determines how quickly features are "forgotten" from + histograms. + */ + CV_WRAP virtual double getDefaultLearningRate() const = 0; + /** @brief Sets the learning rate of the algorithm. + */ + CV_WRAP virtual void setDefaultLearningRate(double lr) = 0; + + /** @brief Returns the number of frames used to initialize background model. + */ + CV_WRAP virtual int getNumFrames() const = 0; + /** @brief Sets the number of frames used to initialize background model. + */ + CV_WRAP virtual void setNumFrames(int nframes) = 0; + + /** @brief Returns the parameter used for quantization of color-space. + + It is the number of discrete levels in each channel to be used in histograms. + */ + CV_WRAP virtual int getQuantizationLevels() const = 0; + /** @brief Sets the parameter used for quantization of color-space + */ + CV_WRAP virtual void setQuantizationLevels(int nlevels) = 0; + + /** @brief Returns the prior probability that each individual pixel is a background pixel. + */ + CV_WRAP virtual double getBackgroundPrior() const = 0; + /** @brief Sets the prior probability that each individual pixel is a background pixel. + */ + CV_WRAP virtual void setBackgroundPrior(double bgprior) = 0; + + /** @brief Returns the kernel radius used for morphological operations + */ + CV_WRAP virtual int getSmoothingRadius() const = 0; + /** @brief Sets the kernel radius used for morphological operations + */ + CV_WRAP virtual void setSmoothingRadius(int radius) = 0; + + /** @brief Returns the value of decision threshold. + + Decision value is the value above which pixel is determined to be FG. + */ + CV_WRAP virtual double getDecisionThreshold() const = 0; + /** @brief Sets the value of decision threshold. + */ + CV_WRAP virtual void setDecisionThreshold(double thresh) = 0; + + /** @brief Returns the status of background model update + */ + CV_WRAP virtual bool getUpdateBackgroundModel() const = 0; + /** @brief Sets the status of background model update + */ + CV_WRAP virtual void setUpdateBackgroundModel(bool update) = 0; + + /** @brief Returns the minimum value taken on by pixels in image sequence. Usually 0. + */ + CV_WRAP virtual double getMinVal() const = 0; + /** @brief Sets the minimum value taken on by pixels in image sequence. + */ + CV_WRAP virtual void setMinVal(double val) = 0; + + /** @brief Returns the maximum value taken on by pixels in image sequence. e.g. 1.0 or 255. + */ + CV_WRAP virtual double getMaxVal() const = 0; + /** @brief Sets the maximum value taken on by pixels in image sequence. + */ + CV_WRAP virtual void setMaxVal(double val) = 0; +}; + +/** @brief Creates a GMG Background Subtractor + +@param initializationFrames number of frames used to initialize the background models. +@param decisionThreshold Threshold value, above which it is marked foreground, else background. + */ +CV_EXPORTS_W Ptr createBackgroundSubtractorGMG(int initializationFrames=120, + double decisionThreshold=0.8); + +//! @} + +} +} + +#endif +#endif diff --git a/thirdparty/linux/include/opencv2/bioinspired.hpp b/thirdparty/linux/include/opencv2/bioinspired.hpp new file mode 100644 index 0000000..9c7e23b --- /dev/null +++ b/thirdparty/linux/include/opencv2/bioinspired.hpp @@ -0,0 +1,60 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_BIOINSPIRED_HPP__ +#define __OPENCV_BIOINSPIRED_HPP__ + +#include "opencv2/core.hpp" +#include "opencv2/bioinspired/retina.hpp" +#include "opencv2/bioinspired/retinafasttonemapping.hpp" +#include "opencv2/bioinspired/transientareassegmentationmodule.hpp" + +/** @defgroup bioinspired Biologically inspired vision models and derivated tools + +The module provides biological visual systems models (human visual system and others). It also +provides derivated objects that take advantage of those bio-inspired models. + +@ref bioinspired_retina + +*/ + +#endif diff --git a/thirdparty/linux/include/opencv2/bioinspired/bioinspired.hpp b/thirdparty/linux/include/opencv2/bioinspired/bioinspired.hpp new file mode 100644 index 0000000..40be285 --- /dev/null +++ b/thirdparty/linux/include/opencv2/bioinspired/bioinspired.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/bioinspired.hpp" diff --git a/thirdparty/linux/include/opencv2/bioinspired/retina.hpp b/thirdparty/linux/include/opencv2/bioinspired/retina.hpp new file mode 100644 index 0000000..583599c --- /dev/null +++ b/thirdparty/linux/include/opencv2/bioinspired/retina.hpp @@ -0,0 +1,456 @@ +/*#****************************************************************************** + ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + ** + ** By downloading, copying, installing or using the software you agree to this license. + ** If you do not agree to this license, do not download, install, + ** copy or use the software. + ** + ** + ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab. + ** Use: extract still images & image sequences features, from contours details to motion spatio-temporal features, etc. for high level visual scene analysis. Also contribute to image enhancement/compression such as tone mapping. + ** + ** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications) + ** + ** Creation - enhancement process 2007-2015 + ** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France + ** + ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr). + ** Refer to the following research paper for more information: + ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 + ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book: + ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. + ** + ** The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author : + ** _take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper: + ** ====> B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007 + ** _take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. + ** ====> more informations in the above cited Jeanny Heraults's book. + ** + ** License Agreement + ** For Open Source Computer Vision Library + ** + ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved. + ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. + ** + ** For Human Visual System tools (bioinspired) + ** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved. + ** + ** Third party copyrights are property of their respective owners. + ** + ** Redistribution and use in source and binary forms, with or without modification, + ** are permitted provided that the following conditions are met: + ** + ** * Redistributions of source code must retain the above copyright notice, + ** this list of conditions and the following disclaimer. + ** + ** * Redistributions in binary form must reproduce the above copyright notice, + ** this list of conditions and the following disclaimer in the documentation + ** and/or other materials provided with the distribution. + ** + ** * The name of the copyright holders may not be used to endorse or promote products + ** derived from this software without specific prior written permission. + ** + ** This software is provided by the copyright holders and contributors "as is" and + ** any express or implied warranties, including, but not limited to, the implied + ** warranties of merchantability and fitness for a particular purpose are disclaimed. + ** In no event shall the Intel Corporation or contributors be liable for any direct, + ** indirect, incidental, special, exemplary, or consequential damages + ** (including, but not limited to, procurement of substitute goods or services; + ** loss of use, data, or profits; or business interruption) however caused + ** and on any theory of liability, whether in contract, strict liability, + ** or tort (including negligence or otherwise) arising in any way out of + ** the use of this software, even if advised of the possibility of such damage. + *******************************************************************************/ + +#ifndef __OPENCV_BIOINSPIRED_RETINA_HPP__ +#define __OPENCV_BIOINSPIRED_RETINA_HPP__ + +/** +@file +@date Jul 19, 2011 +@author Alexandre Benoit +*/ + +#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support + + +namespace cv{ +namespace bioinspired{ + +//! @addtogroup bioinspired +//! @{ + +enum { + RETINA_COLOR_RANDOM, //!< each pixel position is either R, G or B in a random choice + RETINA_COLOR_DIAGONAL,//!< color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR... + RETINA_COLOR_BAYER//!< standard bayer sampling +}; + + +/** @brief retina model parameters structure + + For better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel + + Here is the default configuration file of the retina module. It gives results such as the first + retina output shown on the top of this page. + + @code{xml} + + + + 1 + 1 + 7.5e-01 + 9.0e-01 + 5.3e-01 + 0.01 + 0.5 + 7. + 7.5e-01 + + 1 + 0. + 0. + 7. + 2.0e+00 + 9.5e-01 + 0. + 7. + + @endcode + + Here is the 'realistic" setup used to obtain the second retina output shown on the top of this page. + + @code{xml} + + + + 1 + 1 + 8.9e-01 + 9.0e-01 + 5.3e-01 + 0.3 + 0.5 + 7. + 8.9e-01 + + 1 + 0. + 0. + 7. + 2.0e+00 + 9.5e-01 + 0. + 7. + + @endcode + */ + struct RetinaParameters{ + //! Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters + struct OPLandIplParvoParameters{ + OPLandIplParvoParameters():colorMode(true), + normaliseOutput(true), + photoreceptorsLocalAdaptationSensitivity(0.75f), + photoreceptorsTemporalConstant(0.9f), + photoreceptorsSpatialConstant(0.53f), + horizontalCellsGain(0.01f), + hcellsTemporalConstant(0.5f), + hcellsSpatialConstant(7.f), + ganglionCellsSensitivity(0.75f) { } // default setup + bool colorMode, normaliseOutput; + float photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity; + }; + //! Inner Plexiform Layer Magnocellular channel (IplMagno) + struct IplMagnoParameters{ + IplMagnoParameters(): + normaliseOutput(true), + parasolCells_beta(0.f), + parasolCells_tau(0.f), + parasolCells_k(7.f), + amacrinCellsTemporalCutFrequency(2.0f), + V0CompressionParameter(0.95f), + localAdaptintegration_tau(0.f), + localAdaptintegration_k(7.f) { } // default setup + bool normaliseOutput; + float parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k; + }; + OPLandIplParvoParameters OPLandIplParvo; + IplMagnoParameters IplMagno; + }; + + + +/** @brief class which allows the Gipsa/Listic Labs model to be used with OpenCV. + +This retina model allows spatio-temporal image processing (applied on still images, video sequences). +As a summary, these are the retina model properties: +- It applies a spectral whithening (mid-frequency details enhancement) +- high frequency spatio-temporal noise reduction +- low frequency luminance to be reduced (luminance range compression) +- local logarithmic luminance compression allows details to be enhanced in low light conditions + +USE : this model can be used basically for spatio-temporal video effects but also for : + _using the getParvo method output matrix : texture analysiswith enhanced signal to noise ratio and enhanced details robust against input images luminance ranges + _using the getMagno method output matrix : motion analysis also with the previously cited properties + +for more information, reer to the following papers : +Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 +Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. + +The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author : +take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper: +B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007 +take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. +more informations in the above cited Jeanny Heraults's book. + */ +class CV_EXPORTS_W Retina : public Algorithm { + +public: + + + /** @brief Retreive retina input buffer size + @return the retina input buffer size + */ + CV_WRAP virtual Size getInputSize()=0; + + /** @brief Retreive retina output buffer size that can be different from the input if a spatial log + transformation is applied + @return the retina output buffer size + */ + CV_WRAP virtual Size getOutputSize()=0; + + /** @brief Try to open an XML retina parameters file to adjust current retina instance setup + + - if the xml file does not exist, then default setup is applied + - warning, Exceptions are thrown if read XML file is not valid + @param retinaParameterFile the parameters filename + @param applyDefaultSetupOnFailure set to true if an error must be thrown on error + + You can retrieve the current parameters structure using the method Retina::getParameters and update + it before running method Retina::setup. + */ + CV_WRAP virtual void setup(String retinaParameterFile="", const bool applyDefaultSetupOnFailure=true)=0; + + /** @overload + @param fs the open Filestorage which contains retina parameters + @param applyDefaultSetupOnFailure set to true if an error must be thrown on error + */ + virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0; + + /** @overload + @param newParameters a parameters structures updated with the new target configuration. + */ + virtual void setup(RetinaParameters newParameters)=0; + + /** + @return the current parameters setup + */ + virtual RetinaParameters getParameters()=0; + + /** @brief Outputs a string showing the used parameters setup + @return a string which contains formated parameters information + */ + CV_WRAP virtual const String printSetup()=0; + + /** @brief Write xml/yml formated parameters information + @param fs the filename of the xml file that will be open and writen with formatted parameters + information + */ + CV_WRAP virtual void write( String fs ) const=0; + + /** @overload */ + virtual void write( FileStorage& fs ) const=0; + + /** @brief Setup the OPL and IPL parvo channels (see biologocal model) + + OPL is referred as Outer Plexiform Layer of the retina, it allows the spatio-temporal filtering + which withens the spectrum and reduces spatio-temporal noise while attenuating global luminance + (low frequency energy) IPL parvo is the OPL next processing stage, it refers to a part of the + Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision. See + reference papers for more informations. + for more informations, please have a look at the paper Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 + @param colorMode specifies if (true) color is processed of not (false) to then processing gray + level image + @param normaliseOutput specifies if (true) output is rescaled between 0 and 255 of not (false) + @param photoreceptorsLocalAdaptationSensitivity the photoreceptors sensitivity renage is 0-1 + (more log compression effect when value increases) + @param photoreceptorsTemporalConstant the time constant of the first order low pass filter of + the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is + frames, typical value is 1 frame + @param photoreceptorsSpatialConstant the spatial constant of the first order low pass filter of + the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is + pixels, typical value is 1 pixel + @param horizontalCellsGain gain of the horizontal cells network, if 0, then the mean value of + the output is zero, if the parameter is near 1, then, the luminance is not filtered and is + still reachable at the output, typicall value is 0 + @param HcellsTemporalConstant the time constant of the first order low pass filter of the + horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is + frames, typical value is 1 frame, as the photoreceptors + @param HcellsSpatialConstant the spatial constant of the first order low pass filter of the + horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, + typical value is 5 pixel, this value is also used for local contrast computing when computing + the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular + channel model) + @param ganglionCellsSensitivity the compression strengh of the ganglion cells local adaptation + output, set a value between 0.6 and 1 for best results, a high value increases more the low + value sensitivity... and the output saturates faster, recommended value: 0.7 + */ + CV_WRAP virtual void setupOPLandIPLParvoChannel(const bool colorMode=true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity=0.7f, const float photoreceptorsTemporalConstant=0.5f, const float photoreceptorsSpatialConstant=0.53f, const float horizontalCellsGain=0.f, const float HcellsTemporalConstant=1.f, const float HcellsSpatialConstant=7.f, const float ganglionCellsSensitivity=0.7f)=0; + + /** @brief Set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel + + this channel processes signals output from OPL processing stage in peripheral vision, it allows + motion information enhancement. It is decorrelated from the details channel. See reference + papers for more details. + + @param normaliseOutput specifies if (true) output is rescaled between 0 and 255 of not (false) + @param parasolCells_beta the low pass filter gain used for local contrast adaptation at the + IPL level of the retina (for ganglion cells local adaptation), typical value is 0 + @param parasolCells_tau the low pass filter time constant used for local contrast adaptation + at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical + value is 0 (immediate response) + @param parasolCells_k the low pass filter spatial constant used for local contrast adaptation + at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical + value is 5 + @param amacrinCellsTemporalCutFrequency the time constant of the first order high pass fiter of + the magnocellular way (motion information channel), unit is frames, typical value is 1.2 + @param V0CompressionParameter the compression strengh of the ganglion cells local adaptation + output, set a value between 0.6 and 1 for best results, a high value increases more the low + value sensitivity... and the output saturates faster, recommended value: 0.95 + @param localAdaptintegration_tau specifies the temporal constant of the low pas filter + involved in the computation of the local "motion mean" for the local adaptation computation + @param localAdaptintegration_k specifies the spatial constant of the low pas filter involved + in the computation of the local "motion mean" for the local adaptation computation + */ + CV_WRAP virtual void setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta=0.f, const float parasolCells_tau=0.f, const float parasolCells_k=7.f, const float amacrinCellsTemporalCutFrequency=1.2f, const float V0CompressionParameter=0.95f, const float localAdaptintegration_tau=0.f, const float localAdaptintegration_k=7.f)=0; + + /** @brief Method which allows retina to be applied on an input image, + + after run, encapsulated retina module is ready to deliver its outputs using dedicated + acccessors, see getParvo and getMagno methods + @param inputImage the input Mat image to be processed, can be gray level or BGR coded in any + format (from 8bit to 16bits) + */ + CV_WRAP virtual void run(InputArray inputImage)=0; + + /** @brief Method which processes an image in the aim to correct its luminance correct + backlight problems, enhance details in shadows. + + This method is designed to perform High Dynamic Range image tone mapping (compress \>8bit/pixel + images to 8bit/pixel). This is a simplified version of the Retina Parvocellular model + (simplified version of the run/getParvo methods call) since it does not include the + spatio-temporal filter modelling the Outer Plexiform Layer of the retina that performs spectral + whitening and many other stuff. However, it works great for tone mapping and in a faster way. + + Check the demos and experiments section to see examples and the way to perform tone mapping + using the original retina model and the method. + + @param inputImage the input image to process (should be coded in float format : CV_32F, + CV_32FC1, CV_32F_C3, CV_32F_C4, the 4th channel won't be considered). + @param outputToneMappedImage the output 8bit/channel tone mapped image (CV_8U or CV_8UC3 format). + */ + CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0; + + /** @brief Accessor of the details channel of the retina (models foveal vision). + + Warning, getParvoRAW methods return buffers that are not rescaled within range [0;255] while + the non RAW method allows a normalized matrix to be retrieved. + + @param retinaOutput_parvo the output buffer (reallocated if necessary), format can be : + - a Mat, this output is rescaled for standard 8bits image processing use in OpenCV + - RAW methods actually return a 1D matrix (encoding is R1, R2, ... Rn, G1, G2, ..., Gn, B1, + B2, ...Bn), this output is the original retina filter model output, without any + quantification or rescaling. + @see getParvoRAW + */ + CV_WRAP virtual void getParvo(OutputArray retinaOutput_parvo)=0; + + /** @brief Accessor of the details channel of the retina (models foveal vision). + @see getParvo + */ + CV_WRAP virtual void getParvoRAW(OutputArray retinaOutput_parvo)=0; + + /** @brief Accessor of the motion channel of the retina (models peripheral vision). + + Warning, getMagnoRAW methods return buffers that are not rescaled within range [0;255] while + the non RAW method allows a normalized matrix to be retrieved. + @param retinaOutput_magno the output buffer (reallocated if necessary), format can be : + - a Mat, this output is rescaled for standard 8bits image processing use in OpenCV + - RAW methods actually return a 1D matrix (encoding is M1, M2,... Mn), this output is the + original retina filter model output, without any quantification or rescaling. + @see getMagnoRAW + */ + CV_WRAP virtual void getMagno(OutputArray retinaOutput_magno)=0; + + /** @brief Accessor of the motion channel of the retina (models peripheral vision). + @see getMagno + */ + CV_WRAP virtual void getMagnoRAW(OutputArray retinaOutput_magno)=0; + + /** @overload */ + CV_WRAP virtual const Mat getMagnoRAW() const=0; + /** @overload */ + CV_WRAP virtual const Mat getParvoRAW() const=0; + + /** @brief Activate color saturation as the final step of the color demultiplexing process -\> this + saturation is a sigmoide function applied to each channel of the demultiplexed image. + @param saturateColors boolean that activates color saturation (if true) or desactivate (if false) + @param colorSaturationValue the saturation factor : a simple factor applied on the chrominance + buffers + */ + CV_WRAP virtual void setColorSaturation(const bool saturateColors=true, const float colorSaturationValue=4.0f)=0; + + /** @brief Clears all retina buffers + + (equivalent to opening the eyes after a long period of eye close ;o) whatchout the temporal + transition occuring just after this method call. + */ + CV_WRAP virtual void clearBuffers()=0; + + /** @brief Activate/desactivate the Magnocellular pathway processing (motion information extraction), by + default, it is activated + @param activate true if Magnocellular output should be activated, false if not... if activated, + the Magnocellular output can be retrieved using the **getMagno** methods + */ + CV_WRAP virtual void activateMovingContoursProcessing(const bool activate)=0; + + /** @brief Activate/desactivate the Parvocellular pathway processing (contours information extraction), by + default, it is activated + @param activate true if Parvocellular (contours information extraction) output should be + activated, false if not... if activated, the Parvocellular output can be retrieved using the + Retina::getParvo methods + */ + CV_WRAP virtual void activateContoursProcessing(const bool activate)=0; +}; + +//! @relates bioinspired::Retina +//! @{ + +/** @overload */ +CV_EXPORTS_W Ptr createRetina(Size inputSize); +/** @brief Constructors from standardized interfaces : retreive a smart pointer to a Retina instance + +@param inputSize the input frame size +@param colorMode the chosen processing mode : with or without color processing +@param colorSamplingMethod specifies which kind of color sampling will be used : +- cv::bioinspired::RETINA_COLOR_RANDOM: each pixel position is either R, G or B in a random choice +- cv::bioinspired::RETINA_COLOR_DIAGONAL: color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR... +- cv::bioinspired::RETINA_COLOR_BAYER: standard bayer sampling +@param useRetinaLogSampling activate retina log sampling, if true, the 2 following parameters can +be used +@param reductionFactor only usefull if param useRetinaLogSampling=true, specifies the reduction +factor of the output frame (as the center (fovea) is high resolution and corners can be +underscaled, then a reduction of the output is allowed without precision leak +@param samplingStrenght only usefull if param useRetinaLogSampling=true, specifies the strenght of +the log scale that is applied + */ +CV_EXPORTS_W Ptr createRetina(Size inputSize, const bool colorMode, int colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const float reductionFactor=1.0f, const float samplingStrenght=10.0f); + +//! @} + +//! @} + +} +} +#endif /* __OPENCV_BIOINSPIRED_RETINA_HPP__ */ diff --git a/thirdparty/linux/include/opencv2/bioinspired/retinafasttonemapping.hpp b/thirdparty/linux/include/opencv2/bioinspired/retinafasttonemapping.hpp new file mode 100644 index 0000000..c65709d --- /dev/null +++ b/thirdparty/linux/include/opencv2/bioinspired/retinafasttonemapping.hpp @@ -0,0 +1,138 @@ + +/*#****************************************************************************** + ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + ** + ** By downloading, copying, installing or using the software you agree to this license. + ** If you do not agree to this license, do not download, install, + ** copy or use the software. + ** + ** + ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab. + ** + ** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications) + ** + ** Creation - enhancement process 2007-2013 + ** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France + ** + ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr). + ** Refer to the following research paper for more information: + ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 + ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book: + ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. + ** + ** + ** + ** + ** + ** This class is based on image processing tools of the author and already used within the Retina class (this is the same code as method retina::applyFastToneMapping, but in an independent class, it is ligth from a memory requirement point of view). It implements an adaptation of the efficient tone mapping algorithm propose by David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite: + ** -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816 + ** + ** + ** License Agreement + ** For Open Source Computer Vision Library + ** + ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved. + ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. + ** + ** For Human Visual System tools (bioinspired) + ** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved. + ** + ** Third party copyrights are property of their respective owners. + ** + ** Redistribution and use in source and binary forms, with or without modification, + ** are permitted provided that the following conditions are met: + ** + ** * Redistributions of source code must retain the above copyright notice, + ** this list of conditions and the following disclaimer. + ** + ** * Redistributions in binary form must reproduce the above copyright notice, + ** this list of conditions and the following disclaimer in the documentation + ** and/or other materials provided with the distribution. + ** + ** * The name of the copyright holders may not be used to endorse or promote products + ** derived from this software without specific prior written permission. + ** + ** This software is provided by the copyright holders and contributors "as is" and + ** any express or implied warranties, including, but not limited to, the implied + ** warranties of merchantability and fitness for a particular purpose are disclaimed. + ** In no event shall the Intel Corporation or contributors be liable for any direct, + ** indirect, incidental, special, exemplary, or consequential damages + ** (including, but not limited to, procurement of substitute goods or services; + ** loss of use, data, or profits; or business interruption) however caused + ** and on any theory of liability, whether in contract, strict liability, + ** or tort (including negligence or otherwise) arising in any way out of + ** the use of this software, even if advised of the possibility of such damage. + *******************************************************************************/ + +#ifndef __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ +#define __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ + +/** +@file +@date May 26, 2013 +@author Alexandre Benoit + */ + +#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support + +namespace cv{ +namespace bioinspired{ + +//! @addtogroup bioinspired +//! @{ + +/** @brief a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV. + +This algorithm is already implemented in thre Retina class (retina::applyFastToneMapping) but used it does not require all the retina model to be allocated. This allows a light memory use for low memory devices (smartphones, etc. +As a summary, these are the model properties: +- 2 stages of local luminance adaptation with a different local neighborhood for each. +- first stage models the retina photorecetors local luminance adaptation +- second stage models th ganglion cells local information adaptation +- compared to the initial publication, this class uses spatio-temporal low pass filters instead of spatial only filters. + this can help noise robustness and temporal stability for video sequence use cases. + +for more information, read to the following papers : +Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 +regarding spatio-temporal filter and the bigger retina model : +Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. +*/ +class CV_EXPORTS_W RetinaFastToneMapping : public Algorithm +{ +public: + + /** @brief applies a luminance correction (initially High Dynamic Range (HDR) tone mapping) + + using only the 2 local adaptation stages of the retina parvocellular channel : photoreceptors + level and ganlion cells level. Spatio temporal filtering is applied but limited to temporal + smoothing and eventually high frequencies attenuation. This is a lighter method than the one + available using the regular retina::run method. It is then faster but it does not include + complete temporal filtering nor retina spectral whitening. Then, it can have a more limited + effect on images with a very high dynamic range. This is an adptation of the original still + image HDR tone mapping algorithm of David Alleyson, Sabine Susstruck and Laurence Meylan's + work, please cite: -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local + Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of + America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816 + + @param inputImage the input image to process RGB or gray levels + @param outputToneMappedImage the output tone mapped image + */ + CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0; + + /** @brief updates tone mapping behaviors by adjusing the local luminance computation area + + @param photoreceptorsNeighborhoodRadius the first stage local adaptation area + @param ganglioncellsNeighborhoodRadius the second stage local adaptation area + @param meanLuminanceModulatorK the factor applied to modulate the meanLuminance information + (default is 1, see reference paper) + */ + CV_WRAP virtual void setup(const float photoreceptorsNeighborhoodRadius=3.f, const float ganglioncellsNeighborhoodRadius=1.f, const float meanLuminanceModulatorK=1.f)=0; +}; + +//! @relates bioinspired::RetinaFastToneMapping +CV_EXPORTS_W Ptr createRetinaFastToneMapping(Size inputSize); + +//! @} + +} +} +#endif /* __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ */ diff --git a/thirdparty/linux/include/opencv2/bioinspired/transientareassegmentationmodule.hpp b/thirdparty/linux/include/opencv2/bioinspired/transientareassegmentationmodule.hpp new file mode 100644 index 0000000..b11b61d --- /dev/null +++ b/thirdparty/linux/include/opencv2/bioinspired/transientareassegmentationmodule.hpp @@ -0,0 +1,205 @@ +/*#****************************************************************************** + ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + ** + ** By downloading, copying, installing or using the software you agree to this license. + ** If you do not agree to this license, do not download, install, + ** copy or use the software. + ** + ** + ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. + ** TransientAreasSegmentationModule Use: extract areas that present spatio-temporal changes. + ** => It should be used at the output of the cv::bioinspired::Retina::getMagnoRAW() output that enhances spatio-temporal changes + ** + ** Maintainers : Listic lab (code author current affiliation & applications) + ** + ** Creation - enhancement process 2007-2015 + ** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France + ** + ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr). + ** Refer to the following research paper for more information: + ** Strat, S.T.; Benoit, A.; Lambert, P., "Retina enhanced bag of words descriptors for video classification," Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European , vol., no., pp.1307,1311, 1-5 Sept. 2014 (http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6952461&isnumber=6951911) + ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 + ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book: + ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891. + ** + ** + ** License Agreement + ** For Open Source Computer Vision Library + ** + ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved. + ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. + ** + ** For Human Visual System tools (bioinspired) + ** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved. + ** + ** Third party copyrights are property of their respective owners. + ** + ** Redistribution and use in source and binary forms, with or without modification, + ** are permitted provided that the following conditions are met: + ** + ** * Redistributions of source code must retain the above copyright notice, + ** this list of conditions and the following disclaimer. + ** + ** * Redistributions in binary form must reproduce the above copyright notice, + ** this list of conditions and the following disclaimer in the documentation + ** and/or other materials provided with the distribution. + ** + ** * The name of the copyright holders may not be used to endorse or promote products + ** derived from this software without specific prior written permission. + ** + ** This software is provided by the copyright holders and contributors "as is" and + ** any express or implied warranties, including, but not limited to, the implied + ** warranties of merchantability and fitness for a particular purpose are disclaimed. + ** In no event shall the Intel Corporation or contributors be liable for any direct, + ** indirect, incidental, special, exemplary, or consequential damages + ** (including, but not limited to, procurement of substitute goods or services; + ** loss of use, data, or profits; or business interruption) however caused + ** and on any theory of liability, whether in contract, strict liability, + ** or tort (including negligence or otherwise) arising in any way out of + ** the use of this software, even if advised of the possibility of such damage. + *******************************************************************************/ + +#ifndef SEGMENTATIONMODULE_HPP_ +#define SEGMENTATIONMODULE_HPP_ + +/** +@file +@date 2007-2013 +@author Alexandre BENOIT, benoit.alexandre.vision@gmail.com +*/ + +#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support + +namespace cv +{ +namespace bioinspired +{ +//! @addtogroup bioinspired +//! @{ + +/** @brief parameter structure that stores the transient events detector setup parameters +*/ +struct SegmentationParameters{ // CV_EXPORTS_W_MAP to export to python native dictionnaries + // default structure instance construction with default values + SegmentationParameters(): + thresholdON(100), + thresholdOFF(100), + localEnergy_temporalConstant(0.5), + localEnergy_spatialConstant(5), + neighborhoodEnergy_temporalConstant(1), + neighborhoodEnergy_spatialConstant(15), + contextEnergy_temporalConstant(1), + contextEnergy_spatialConstant(75){}; + // all properties list + float thresholdON; + float thresholdOFF; + //! the time constant of the first order low pass filter, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 0.5 frame + float localEnergy_temporalConstant; + //! the spatial constant of the first order low pass filter, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 5 pixel + float localEnergy_spatialConstant; + //! local neighborhood energy filtering parameters : the aim is to get information about the energy neighborhood to perform a center surround energy analysis + float neighborhoodEnergy_temporalConstant; + float neighborhoodEnergy_spatialConstant; + //! context neighborhood energy filtering parameters : the aim is to get information about the energy on a wide neighborhood area to filtered out local effects + float contextEnergy_temporalConstant; + float contextEnergy_spatialConstant; +}; + +/** @brief class which provides a transient/moving areas segmentation module + +perform a locally adapted segmentation by using the retina magno input data Based on Alexandre +BENOIT thesis: "Le système visuel humain au secours de la vision par ordinateur" + +3 spatio temporal filters are used: +- a first one which filters the noise and local variations of the input motion energy +- a second (more powerfull low pass spatial filter) which gives the neighborhood motion energy the +segmentation consists in the comparison of these both outputs, if the local motion energy is higher +to the neighborhood otion energy, then the area is considered as moving and is segmented +- a stronger third low pass filter helps decision by providing a smooth information about the +"motion context" in a wider area + */ + +class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm +{ +public: + + + /** @brief return the sze of the manage input and output images + */ + CV_WRAP virtual Size getSize()=0; + + /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup + + - if the xml file does not exist, then default setup is applied + - warning, Exceptions are thrown if read XML file is not valid + @param segmentationParameterFile : the parameters filename + @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error + */ + CV_WRAP virtual void setup(String segmentationParameterFile="", const bool applyDefaultSetupOnFailure=true)=0; + + /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup + + - if the xml file does not exist, then default setup is applied + - warning, Exceptions are thrown if read XML file is not valid + @param fs : the open Filestorage which contains segmentation parameters + @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error + */ + virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0; + + /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup + + - if the xml file does not exist, then default setup is applied + - warning, Exceptions are thrown if read XML file is not valid + @param newParameters : a parameters structures updated with the new target configuration + */ + virtual void setup(SegmentationParameters newParameters)=0; + + /** @brief return the current parameters setup + */ + virtual SegmentationParameters getParameters()=0; + + /** @brief parameters setup display method + @return a string which contains formatted parameters information + */ + CV_WRAP virtual const String printSetup()=0; + + /** @brief write xml/yml formated parameters information + @param fs : the filename of the xml file that will be open and writen with formatted parameters information + */ + CV_WRAP virtual void write( String fs ) const=0; + + /** @brief write xml/yml formated parameters information + @param fs : a cv::Filestorage object ready to be filled + */ + virtual void write( cv::FileStorage& fs ) const=0; + + /** @brief main processing method, get result using methods getSegmentationPicture() + @param inputToSegment : the image to process, it must match the instance buffer size ! + @param channelIndex : the channel to process in case of multichannel images + */ + CV_WRAP virtual void run(InputArray inputToSegment, const int channelIndex=0)=0; + + /** @brief access function + @return the last segmentation result: a boolean picture which is resampled between 0 and 255 for a display purpose + */ + CV_WRAP virtual void getSegmentationPicture(OutputArray transientAreas)=0; + + /** @brief cleans all the buffers of the instance + */ + CV_WRAP virtual void clearAllBuffers()=0; +}; + +/** @brief allocator +@param inputSize : size of the images input to segment (output will be the same size) +@relates bioinspired::TransientAreasSegmentationModule + */ +CV_EXPORTS_W Ptr createTransientAreasSegmentationModule(Size inputSize); + +//! @} + +}} // namespaces end : cv and bioinspired + + +#endif + + diff --git a/thirdparty/linux/include/opencv2/calib3d.hpp b/thirdparty/linux/include/opencv2/calib3d.hpp new file mode 100644 index 0000000..5a0e020 --- /dev/null +++ b/thirdparty/linux/include/opencv2/calib3d.hpp @@ -0,0 +1,2134 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CALIB3D_HPP +#define OPENCV_CALIB3D_HPP + +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/core/affine.hpp" + +/** + @defgroup calib3d Camera Calibration and 3D Reconstruction + +The functions in this section use a so-called pinhole camera model. In this model, a scene view is +formed by projecting 3D points into the image plane using a perspective transformation. + +\f[s \; m' = A [R|t] M'\f] + +or + +\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} +\begin{bmatrix} +r_{11} & r_{12} & r_{13} & t_1 \\ +r_{21} & r_{22} & r_{23} & t_2 \\ +r_{31} & r_{32} & r_{33} & t_3 +\end{bmatrix} +\begin{bmatrix} +X \\ +Y \\ +Z \\ +1 +\end{bmatrix}\f] + +where: + +- \f$(X, Y, Z)\f$ are the coordinates of a 3D point in the world coordinate space +- \f$(u, v)\f$ are the coordinates of the projection point in pixels +- \f$A\f$ is a camera matrix, or a matrix of intrinsic parameters +- \f$(cx, cy)\f$ is a principal point that is usually at the image center +- \f$fx, fy\f$ are the focal lengths expressed in pixel units. + +Thus, if an image from the camera is scaled by a factor, all of these parameters should be scaled +(multiplied/divided, respectively) by the same factor. The matrix of intrinsic parameters does not +depend on the scene viewed. So, once estimated, it can be re-used as long as the focal length is +fixed (in case of zoom lens). The joint rotation-translation matrix \f$[R|t]\f$ is called a matrix of +extrinsic parameters. It is used to describe the camera motion around a static scene, or vice versa, +rigid motion of an object in front of a still camera. That is, \f$[R|t]\f$ translates coordinates of a +point \f$(X, Y, Z)\f$ to a coordinate system, fixed with respect to the camera. The transformation above +is equivalent to the following (when \f$z \ne 0\f$ ): + +\f[\begin{array}{l} +\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ +x' = x/z \\ +y' = y/z \\ +u = f_x*x' + c_x \\ +v = f_y*y' + c_y +\end{array}\f] + +The following figure illustrates the pinhole camera model. + +![Pinhole camera model](pics/pinhole_camera_model.png) + +Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. +So, the above model is extended as: + +\f[\begin{array}{l} +\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ +x' = x/z \\ +y' = y/z \\ +x'' = x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\ +y'' = y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ +\text{where} \quad r^2 = x'^2 + y'^2 \\ +u = f_x*x'' + c_x \\ +v = f_y*y'' + c_y +\end{array}\f] + +\f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$ are radial distortion coefficients. \f$p_1\f$ and \f$p_2\f$ are +tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion +coefficients. Higher-order coefficients are not considered in OpenCV. + +The next figure shows two common types of radial distortion: barrel distortion (typically \f$ k_1 > 0 \f$ and pincushion distortion (typically \f$ k_1 < 0 \f$). + +![](pics/distortion_examples.png) + +In some cases the image sensor may be tilted in order to focus an oblique plane in front of the +camera (Scheimpfug condition). This can be useful for particle image velocimetry (PIV) or +triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and +\f$y''\f$. This distortion can be modelled in the following way, see e.g. @cite Louhichi07. + +\f[\begin{array}{l} +s\vecthree{x'''}{y'''}{1} = +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ +u = f_x*x''' + c_x \\ +v = f_y*y''' + c_y +\end{array}\f] + +where the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter \f$\tau_x\f$ +and \f$\tau_y\f$, respectively, + +\f[ +R(\tau_x, \tau_y) = +\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} +\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = +\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} +{0}{\cos(\tau_x)}{\sin(\tau_x)} +{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. +\f] + +In the functions below the coefficients are passed or returned as + +\f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f] + +vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion +coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera +parameters. And they remain the same regardless of the captured image resolution. If, for example, a +camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion +coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$, \f$c_x\f$, and +\f$c_y\f$ need to be scaled appropriately. + +The functions below use the above model to do the following: + +- Project 3D points to the image plane given intrinsic and extrinsic parameters. +- Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their +projections. +- Estimate intrinsic and extrinsic camera parameters from several views of a known calibration +pattern (every view is described by several 3D-2D point correspondences). +- Estimate the relative position and orientation of the stereo camera "heads" and compute the +*rectification* transformation that makes the camera optical axes parallel. + +@note + - A calibration sample for 3 cameras in horizontal position can be found at + opencv_source_code/samples/cpp/3calibration.cpp + - A calibration sample based on a sequence of images can be found at + opencv_source_code/samples/cpp/calibration.cpp + - A calibration sample in order to do 3D reconstruction can be found at + opencv_source_code/samples/cpp/build3dmodel.cpp + - A calibration sample of an artificially generated camera and chessboard patterns can be + found at opencv_source_code/samples/cpp/calibration_artificial.cpp + - A calibration example on stereo calibration can be found at + opencv_source_code/samples/cpp/stereo_calib.cpp + - A calibration example on stereo matching can be found at + opencv_source_code/samples/cpp/stereo_match.cpp + - (Python) A camera calibration sample can be found at + opencv_source_code/samples/python/calibrate.py + + @{ + @defgroup calib3d_fisheye Fisheye camera model + + Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the + matrix X) The coordinate vector of P in the camera reference frame is: + + \f[Xc = R X + T\f] + + where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y + and z the 3 coordinates of Xc: + + \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f] + + The pinhole projection coordinates of P is [a; b] where + + \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f] + + Fisheye distortion: + + \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f] + + The distorted point coordinates are [x'; y'] where + + \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f] + + Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where: + + \f[u = f_x (x' + \alpha y') + c_x \\ + v = f_y y' + c_y\f] + + @defgroup calib3d_c C API + + @} + */ + +namespace cv +{ + +//! @addtogroup calib3d +//! @{ + +//! type of the robust estimation algorithm +enum { LMEDS = 4, //!< least-median algorithm + RANSAC = 8, //!< RANSAC algorithm + RHO = 16 //!< RHO algorithm + }; + +enum { SOLVEPNP_ITERATIVE = 0, + SOLVEPNP_EPNP = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp + SOLVEPNP_P3P = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete + SOLVEPNP_DLS = 3, //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct + SOLVEPNP_UPNP = 4 //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive + +}; + +enum { CALIB_CB_ADAPTIVE_THRESH = 1, + CALIB_CB_NORMALIZE_IMAGE = 2, + CALIB_CB_FILTER_QUADS = 4, + CALIB_CB_FAST_CHECK = 8 + }; + +enum { CALIB_CB_SYMMETRIC_GRID = 1, + CALIB_CB_ASYMMETRIC_GRID = 2, + CALIB_CB_CLUSTERING = 4 + }; + +enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, + CALIB_FIX_ASPECT_RATIO = 0x00002, + CALIB_FIX_PRINCIPAL_POINT = 0x00004, + CALIB_ZERO_TANGENT_DIST = 0x00008, + CALIB_FIX_FOCAL_LENGTH = 0x00010, + CALIB_FIX_K1 = 0x00020, + CALIB_FIX_K2 = 0x00040, + CALIB_FIX_K3 = 0x00080, + CALIB_FIX_K4 = 0x00800, + CALIB_FIX_K5 = 0x01000, + CALIB_FIX_K6 = 0x02000, + CALIB_RATIONAL_MODEL = 0x04000, + CALIB_THIN_PRISM_MODEL = 0x08000, + CALIB_FIX_S1_S2_S3_S4 = 0x10000, + CALIB_TILTED_MODEL = 0x40000, + CALIB_FIX_TAUX_TAUY = 0x80000, + CALIB_USE_QR = 0x100000, //!< use QR instead of SVD decomposition for solving. Faster but potentially less precise + // only for stereo + CALIB_FIX_INTRINSIC = 0x00100, + CALIB_SAME_FOCAL_LENGTH = 0x00200, + // for stereo rectification + CALIB_ZERO_DISPARITY = 0x00400, + CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise + }; + +//! the algorithm for finding fundamental matrix +enum { FM_7POINT = 1, //!< 7-point algorithm + FM_8POINT = 2, //!< 8-point algorithm + FM_LMEDS = 4, //!< least-median algorithm + FM_RANSAC = 8 //!< RANSAC algorithm + }; + + + +/** @brief Converts a rotation matrix to a rotation vector or vice versa. + +@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). +@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. +@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial +derivatives of the output array components with respect to the input array components. + +\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos{\theta} I + (1- \cos{\theta} ) r r^T + \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f] + +Inverse transformation can be also done easily, since + +\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f] + +A rotation vector is a convenient and most compact representation of a rotation matrix (since any +rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry +optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP . + */ +CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() ); + +/** @brief Finds a perspective transformation between two planes. + +@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 +or vector\ . +@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or +a vector\ . +@param method Method used to computed a homography matrix. The following methods are possible: +- **0** - a regular method using all the points +- **RANSAC** - RANSAC-based robust method +- **LMEDS** - Least-Median robust method +- **RHO** - PROSAC-based robust method +@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier +(used in the RANSAC and RHO methods only). That is, if +\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \| > \texttt{ransacReprojThreshold}\f] +then the point \f$i\f$ is considered an outlier. If srcPoints and dstPoints are measured in pixels, +it usually makes sense to set this parameter somewhere in the range of 1 to 10. +@param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input +mask values are ignored. +@param maxIters The maximum number of RANSAC iterations, 2000 is the maximum it can be. +@param confidence Confidence level, between 0 and 1. + +The function finds and returns the perspective transformation \f$H\f$ between the source and the +destination planes: + +\f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] + +so that the back-projection error + +\f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f] + +is minimized. If the parameter method is set to the default value 0, the function uses all the point +pairs to compute an initial homography estimate with a simple least-squares scheme. + +However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective +transformation (that is, there are some outliers), this initial estimate will be poor. In this case, +you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different +random subsets of the corresponding point pairs (of four pairs each), estimate the homography matrix +using this subset and a simple least-square algorithm, and then compute the quality/goodness of the +computed homography (which is the number of inliers for RANSAC or the median re-projection error for +LMeDs). The best subset is then used to produce the initial estimate of the homography matrix and +the mask of inliers/outliers. + +Regardless of the method, robust or not, the computed homography matrix is refined further (using +inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the +re-projection error even more. + +The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the +noise is rather small, use the default method (method=0). + +The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is +determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an H matrix +cannot be estimated, an empty one will be returned. + +@sa +getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, +perspectiveTransform + + +@note + - A example on calculating a homography for image matching can be found at + opencv_source_code/samples/cpp/video_homography.cpp + + */ +CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints, + int method = 0, double ransacReprojThreshold = 3, + OutputArray mask=noArray(), const int maxIters = 2000, + const double confidence = 0.995); + +/** @overload */ +CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints, + OutputArray mask, int method = 0, double ransacReprojThreshold = 3 ); + +/** @brief Computes an RQ decomposition of 3x3 matrices. + +@param src 3x3 input matrix. +@param mtxR Output 3x3 upper-triangular matrix. +@param mtxQ Output 3x3 orthogonal matrix. +@param Qx Optional output 3x3 rotation matrix around x-axis. +@param Qy Optional output 3x3 rotation matrix around y-axis. +@param Qz Optional output 3x3 rotation matrix around z-axis. + +The function computes a RQ decomposition using the given rotations. This function is used in +decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera +and a rotation matrix. + +It optionally returns three rotation matrices, one for each axis, and the three Euler angles in +degrees (as the return value) that could be used in OpenGL. Note, there is always more than one +sequence of rotations about the three principal axes that results in the same orientation of an +object, eg. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angules +are only one of the possible solutions. + */ +CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ, + OutputArray Qx = noArray(), + OutputArray Qy = noArray(), + OutputArray Qz = noArray()); + +/** @brief Decomposes a projection matrix into a rotation matrix and a camera matrix. + +@param projMatrix 3x4 input projection matrix P. +@param cameraMatrix Output 3x3 camera matrix K. +@param rotMatrix Output 3x3 external rotation matrix R. +@param transVect Output 4x1 translation vector T. +@param rotMatrixX Optional 3x3 rotation matrix around x-axis. +@param rotMatrixY Optional 3x3 rotation matrix around y-axis. +@param rotMatrixZ Optional 3x3 rotation matrix around z-axis. +@param eulerAngles Optional three-element vector containing three Euler angles of rotation in +degrees. + +The function computes a decomposition of a projection matrix into a calibration and a rotation +matrix and the position of a camera. + +It optionally returns three rotation matrices, one for each axis, and three Euler angles that could +be used in OpenGL. Note, there is always more than one sequence of rotations about the three +principal axes that results in the same orientation of an object, eg. see @cite Slabaugh . Returned +tree rotation matrices and corresponding three Euler angules are only one of the possible solutions. + +The function is based on RQDecomp3x3 . + */ +CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, + OutputArray rotMatrix, OutputArray transVect, + OutputArray rotMatrixX = noArray(), + OutputArray rotMatrixY = noArray(), + OutputArray rotMatrixZ = noArray(), + OutputArray eulerAngles =noArray() ); + +/** @brief Computes partial derivatives of the matrix product for each multiplied matrix. + +@param A First multiplied matrix. +@param B Second multiplied matrix. +@param dABdA First output derivative matrix d(A\*B)/dA of size +\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ . +@param dABdB Second output derivative matrix d(A\*B)/dB of size +\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ . + +The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to +the elements of each of the two input matrices. The function is used to compute the Jacobian +matrices in stereoCalibrate but can also be used in any other similar optimization function. + */ +CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB ); + +/** @brief Combines two rotation-and-shift transformations. + +@param rvec1 First rotation vector. +@param tvec1 First translation vector. +@param rvec2 Second rotation vector. +@param tvec2 Second translation vector. +@param rvec3 Output rotation vector of the superposition. +@param tvec3 Output translation vector of the superposition. +@param dr3dr1 +@param dr3dt1 +@param dr3dr2 +@param dr3dt2 +@param dt3dr1 +@param dt3dt1 +@param dt3dr2 +@param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and +tvec2, respectively. + +The functions compute: + +\f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] + +where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and +\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details. + +Also, the functions can compute the derivatives of the output vectors with regards to the input +vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in +your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a +function that contains a matrix multiplication. + */ +CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1, + InputArray rvec2, InputArray tvec2, + OutputArray rvec3, OutputArray tvec3, + OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(), + OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(), + OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(), + OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() ); + +/** @brief Projects 3D points to an image plane. + +@param objectPoints Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or +vector\ ), where N is the number of points in the view. +@param rvec Rotation vector. See Rodrigues for details. +@param tvec Translation vector. +@param cameraMatrix Camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. +@param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or +vector\ . +@param jacobian Optional output 2Nx(10+\) jacobian matrix of derivatives of image +points with respect to components of the rotation vector, translation vector, focal lengths, +coordinates of the principal point and the distortion coefficients. In the old interface different +components of the jacobian are returned via different output parameters. +@param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the +function assumes that the aspect ratio (*fx/fy*) is fixed and correspondingly adjusts the jacobian +matrix. + +The function computes projections of 3D points to the image plane given intrinsic and extrinsic +camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of +image points coordinates (as functions of all the input parameters) with respect to the particular +parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in +calibrateCamera, solvePnP, and stereoCalibrate . The function itself can also be used to compute a +re-projection error given the current intrinsic and extrinsic parameters. + +@note By setting rvec=tvec=(0,0,0) or by setting cameraMatrix to a 3x3 identity matrix, or by +passing zero distortion coefficients, you can get various useful partial cases of the function. This +means that you can compute the distorted coordinates for a sparse set of points or apply a +perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup. + */ +CV_EXPORTS_W void projectPoints( InputArray objectPoints, + InputArray rvec, InputArray tvec, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray imagePoints, + OutputArray jacobian = noArray(), + double aspectRatio = 0 ); + +/** @brief Finds an object pose from 3D-2D point correspondences. + +@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or +1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, +where N is the number of points. vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Output translation vector. +@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses +the provided rvec and tvec values as initial approximations of the rotation and translation +vectors, respectively, and further optimizes them. +@param flags Method for solving a PnP problem: +- **SOLVEPNP_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In +this case the function finds such a pose that minimizes reprojection error, that is the sum +of squared distances between the observed projections imagePoints and the projected (using +projectPoints ) objectPoints . +- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang +"Complete Solution Classification for the Perspective-Three-Point Problem". In this case the +function requires exactly four object and image points. +- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the +paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation". +- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. +"A Direct Least-Squares (DLS) Method for PnP". +- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, +F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length +Estimation". In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ +assuming that both have the same value. Then the cameraMatrix is updated with the estimated +focal length. + +The function estimates the object pose given a set of object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients. + +@note + - An example of how to use solvePnP for planar augmented reality can be found at + opencv_source_code/samples/python/plane_ar.py + - If you are using Python: + - Numpy array slices won't work as input because solvePnP requires contiguous + arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of + modules/calib3d/src/solvepnp.cpp version 2.4.9) + - The P3P algorithm requires image points to be in an array of shape (N,1,2) due + to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) + which requires 2-channel information. + - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of + it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = + np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) + - The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are + unstable and sometimes give completly wrong results. If you pass one of these two flags, + **SOLVEPNP_EPNP** method will be used instead. + */ +CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, + bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE ); + +/** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. + +@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or +1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, +where N is the number of points. vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Output translation vector. +@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses +the provided rvec and tvec values as initial approximations of the rotation and translation +vectors, respectively, and further optimizes them. +@param iterationsCount Number of iterations. +@param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value +is the maximum allowed distance between the observed and computed point projections to consider it +an inlier. +@param confidence The probability that the algorithm produces a useful result. +@param inliers Output vector that contains indices of inliers in objectPoints and imagePoints . +@param flags Method for solving a PnP problem (see solvePnP ). + +The function estimates an object pose given a set of object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients. This function finds such +a pose that minimizes reprojection error, that is, the sum of squared distances between the observed +projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC +makes the function resistant to outliers. + +@note + - An example of how to use solvePNPRansac for object detection can be found at + opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/ + */ +CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, + bool useExtrinsicGuess = false, int iterationsCount = 100, + float reprojectionError = 8.0, double confidence = 0.99, + OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE ); + +/** @brief Finds an initial camera matrix from 3D-2D point correspondences. + +@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern +coordinate space. In the old interface all the per-view vectors are concatenated. See +calibrateCamera for details. +@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the +old interface all the per-view vectors are concatenated. +@param imageSize Image size in pixels used to initialize the principal point. +@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently. +Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ . + +The function estimates and returns an initial camera matrix for the camera calibration process. +Currently, the function only supports planar calibration patterns, which are patterns where each +object point has z-coordinate =0. + */ +CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, + Size imageSize, double aspectRatio = 1.0 ); + +/** @brief Finds the positions of internal corners of the chessboard. + +@param image Source chessboard view. It must be an 8-bit grayscale or color image. +@param patternSize Number of inner corners per a chessboard row and column +( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ). +@param corners Output array of detected corners. +@param flags Various operation flags that can be zero or a combination of the following values: +- **CV_CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black +and white, rather than a fixed threshold level (computed from the average image brightness). +- **CV_CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before +applying fixed or adaptive thresholding. +- **CV_CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter, +square-like shape) to filter out false quads extracted at the contour retrieval stage. +- **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners, +and shortcut the call if none is found. This can drastically speed up the call in the +degenerate condition when no chessboard is observed. + +The function attempts to determine whether the input image is a view of the chessboard pattern and +locate the internal chessboard corners. The function returns a non-zero value if all of the corners +are found and they are placed in a certain order (row by row, left to right in every row). +Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, +a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black +squares touch each other. The detected coordinates are approximate, and to determine their positions +more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with +different parameters if returned coordinates are not accurate enough. + +Sample usage of detecting and drawing chessboard corners: : +@code + Size patternsize(8,6); //interior number of corners + Mat gray = ....; //source image + vector corners; //this will be filled by the detected corners + + //CALIB_CB_FAST_CHECK saves a lot of time on images + //that do not contain any chessboard corners + bool patternfound = findChessboardCorners(gray, patternsize, corners, + CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + + CALIB_CB_FAST_CHECK); + + if(patternfound) + cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), + TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1)); + + drawChessboardCorners(img, patternsize, Mat(corners), patternfound); +@endcode +@note The function requires white space (like a square-thick border, the wider the better) around +the board to make the detection more robust in various environments. Otherwise, if there is no +border and the background is dark, the outer black squares cannot be segmented properly and so the +square grouping and ordering algorithm fails. + */ +CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, + int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE ); + +//! finds subpixel-accurate positions of the chessboard corners +CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size ); + +/** @brief Renders the detected chessboard corners. + +@param image Destination image. It must be an 8-bit color image. +@param patternSize Number of inner corners per a chessboard row and column +(patternSize = cv::Size(points_per_row,points_per_column)). +@param corners Array of detected corners, the output of findChessboardCorners. +@param patternWasFound Parameter indicating whether the complete board was found or not. The +return value of findChessboardCorners should be passed here. + +The function draws individual chessboard corners detected either as red circles if the board was not +found, or as colored corners connected with lines if the board was found. + */ +CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize, + InputArray corners, bool patternWasFound ); + +/** @brief Finds centers in the grid of circles. + +@param image grid view of input circles; it must be an 8-bit grayscale or color image. +@param patternSize number of circles per row and column +( patternSize = Size(points_per_row, points_per_colum) ). +@param centers output array of detected centers. +@param flags various operation flags that can be one of the following values: +- **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles. +- **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles. +- **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to +perspective distortions but much more sensitive to background clutter. +@param blobDetector feature detector that finds blobs like dark circles on light background. + +The function attempts to determine whether the input image contains a grid of circles. If it is, the +function locates centers of the circles. The function returns a non-zero value if all of the centers +have been found and they have been placed in a certain order (row by row, left to right in every +row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. + +Sample usage of detecting and drawing the centers of circles: : +@code + Size patternsize(7,7); //number of centers + Mat gray = ....; //source image + vector centers; //this will be filled by the detected centers + + bool patternfound = findCirclesGrid(gray, patternsize, centers); + + drawChessboardCorners(img, patternsize, Mat(centers), patternfound); +@endcode +@note The function requires white space (like a square-thick border, the wider the better) around +the board to make the detection more robust in various environments. + */ +CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, + OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID, + const Ptr &blobDetector = SimpleBlobDetector::create()); + +/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. + +@param objectPoints In the new interface it is a vector of vectors of calibration pattern points in +the calibration pattern coordinate space (e.g. std::vector>). The outer +vector contains as many elements as the number of the pattern views. If the same calibration pattern +is shown in each view and it is fully visible, all the vectors will be the same. Although, it is +possible to use partially occluded patterns, or even different patterns in different views. Then, +the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, +then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that +Z-coordinate of each input object point is 0. +In the old interface all the vectors of object points from different views are concatenated +together. +@param imagePoints In the new interface it is a vector of vectors of the projections of calibration +pattern points (e.g. std::vector>). imagePoints.size() and +objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. +In the old interface all the vectors of object points from different views are concatenated +together. +@param imageSize Size of the image used only to initialize the intrinsic camera matrix. +@param cameraMatrix Output 3x3 floating-point camera matrix +\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS +and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be +initialized before calling the function. +@param distCoeffs Output vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. +@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view +(e.g. std::vector>). That is, each k-th rotation vector together with the corresponding +k-th translation vector (see the next output parameter description) brings the calibration pattern +from the model coordinate space (in which object points are specified) to the world coordinate +space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). +@param tvecs Output vector of translation vectors estimated for each pattern view. +@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. + Order of deviations values: +\f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, + s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero. +@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. + Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views, + \f$R_i, T_i\f$ are concatenated 1x3 vectors. + @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. +@param flags Different flags that may be zero or a combination of the following values: +- **CV_CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of +fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image +center ( imageSize is used), and focal distances are computed in a least-squares fashion. +Note, that if intrinsic parameters are known, there is no need to use this function just to +estimate extrinsic parameters. Use solvePnP instead. +- **CV_CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global +optimization. It stays at the center or at a different location specified when +CV_CALIB_USE_INTRINSIC_GUESS is set too. +- **CV_CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The +ratio fx/fy stays the same as in the input cameraMatrix . When +CV_CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are +ignored, only their ratio is computed and used further. +- **CV_CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set +to zeros and stay zero. +- **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** The corresponding radial distortion +coefficient is not changed during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is +set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CV_CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the rational model and return 8 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the thin prism model and return 12 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during +the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the tilted sensor model and return 14 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during +the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +@param criteria Termination criteria for the iterative optimization algorithm. + +@return the overall RMS re-projection error. + +The function estimates the intrinsic camera parameters and extrinsic parameters for each of the +views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object +points and their corresponding 2D projections in each view must be specified. That may be achieved +by using an object with a known geometry and easily detectable feature points. Such an object is +called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as +a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters +(when CV_CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration +patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also +be used as long as initial cameraMatrix is provided. + +The algorithm performs the following steps: + +- Compute the initial intrinsic parameters (the option only available for planar calibration + patterns) or read them from the input parameters. The distortion coefficients are all set to + zeros initially unless some of CV_CALIB_FIX_K? are specified. + +- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is + done using solvePnP . + +- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, + that is, the total sum of squared distances between the observed feature points imagePoints and + the projected (using the current estimates for camera parameters and the poses) object points + objectPoints. See projectPoints for details. + +@note + If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and + calibrateCamera returns bad values (zero distortion coefficients, an image center very far from + (w/2-0.5,h/2-0.5), and/or large differences between \f$f_x\f$ and \f$f_y\f$ (ratios of 10:1 or more)), + then you have probably used patternSize=cvSize(rows,cols) instead of using + patternSize=cvSize(cols,rows) in findChessboardCorners . + +@sa + findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort + */ +CV_EXPORTS_AS(calibrateCameraExtended) double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviationsIntrinsics, + OutputArray stdDeviationsExtrinsics, + OutputArray perViewErrors, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); + +/** @overload double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviations, OutputArray perViewErrors, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ) + */ +CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); + +/** @brief Computes useful camera characteristics from the camera matrix. + +@param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or +stereoCalibrate . +@param imageSize Input image size in pixels. +@param apertureWidth Physical width in mm of the sensor. +@param apertureHeight Physical height in mm of the sensor. +@param fovx Output field of view in degrees along the horizontal sensor axis. +@param fovy Output field of view in degrees along the vertical sensor axis. +@param focalLength Focal length of the lens in mm. +@param principalPoint Principal point in mm. +@param aspectRatio \f$f_y/f_x\f$ + +The function computes various useful camera characteristics from the previously estimated camera +matrix. + +@note + Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for + the chessboard pitch (it can thus be any value). + */ +CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, + CV_OUT double& fovx, CV_OUT double& fovy, + CV_OUT double& focalLength, CV_OUT Point2d& principalPoint, + CV_OUT double& aspectRatio ); + +/** @brief Calibrates the stereo camera. + +@param objectPoints Vector of vectors of the calibration pattern points. +@param imagePoints1 Vector of vectors of the projections of the calibration pattern points, +observed by the first camera. +@param imagePoints2 Vector of vectors of the projections of the calibration pattern points, +observed by the second camera. +@param cameraMatrix1 Input/output first camera matrix: +\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If +any of CV_CALIB_USE_INTRINSIC_GUESS , CV_CALIB_FIX_ASPECT_RATIO , +CV_CALIB_FIX_INTRINSIC , or CV_CALIB_FIX_FOCAL_LENGTH are specified, some or all of the +matrix components must be initialized. See the flags description for details. +@param distCoeffs1 Input/output vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. The output vector length depends on the flags. +@param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1 +@param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter +is similar to distCoeffs1 . +@param imageSize Size of the image used only to initialize intrinsic camera matrix. +@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. +@param T Output translation vector between the coordinate systems of the cameras. +@param E Output essential matrix. +@param F Output fundamental matrix. +@param flags Different flags that may be zero or a combination of the following values: +- **CV_CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F +matrices are estimated. +- **CV_CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters +according to the specified flags. Initial values are provided by the user. +- **CV_CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization. +- **CV_CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ . +- **CV_CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$ +. +- **CV_CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ . +- **CV_CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to +zeros and fix there. +- **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** Do not change the corresponding radial +distortion coefficient during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, +the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CV_CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward +compatibility, this extra flag should be explicitly specified to make the calibration +function use the rational model and return 8 coefficients. If the flag is not set, the +function computes and returns only 5 distortion coefficients. +- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the thin prism model and return 12 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during +the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the tilted sensor model and return 14 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during +the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +@param criteria Termination criteria for the iterative optimization algorithm. + +The function estimates transformation between two cameras making a stereo pair. If you have a stereo +camera where the relative position and orientation of two cameras is fixed, and if you computed +poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2), +respectively (this can be done with solvePnP ), then those poses definitely relate to each other. +This means that, given ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). You only +need to know the position and orientation of the second camera relative to the first camera. This is +what the described function does. It computes ( \f$R\f$,\f$T\f$ ) so that: + +\f[R_2=R*R_1\f] +\f[T_2=R*T_1 + T,\f] + +Optionally, it computes the essential matrix E: + +\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f] + +where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function +can also compute the fundamental matrix F: + +\f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f] + +Besides the stereo-related information, the function can also perform a full calibration of each of +two cameras. However, due to the high dimensionality of the parameter space and noise in the input +data, the function can diverge from the correct solution. If the intrinsic parameters can be +estimated with high accuracy for each of the cameras individually (for example, using +calibrateCamera ), you are recommended to do so and then pass CV_CALIB_FIX_INTRINSIC flag to the +function along with the computed intrinsic parameters. Otherwise, if all the parameters are +estimated at once, it makes sense to restrict some parameters, for example, pass +CV_CALIB_SAME_FOCAL_LENGTH and CV_CALIB_ZERO_TANGENT_DIST flags, which is usually a +reasonable assumption. + +Similarly to calibrateCamera , the function minimizes the total re-projection error for all the +points in all the available views from both cameras. The function returns the final value of the +re-projection error. + */ +CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, + InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, + Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F, + int flags = CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); + + +/** @brief Computes rectification transforms for each head of a calibrated stereo camera. + +@param cameraMatrix1 First camera matrix. +@param distCoeffs1 First camera distortion parameters. +@param cameraMatrix2 Second camera matrix. +@param distCoeffs2 Second camera distortion parameters. +@param imageSize Size of the image used for stereo calibration. +@param R Rotation matrix between the coordinate systems of the first and the second cameras. +@param T Translation vector between coordinate systems of the cameras. +@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. +@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. +@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first +camera. +@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second +camera. +@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). +@param flags Operation flags that may be zero or CV_CALIB_ZERO_DISPARITY . If the flag is set, +the function makes the principal points of each camera have the same pixel coordinates in the +rectified views. And if the flag is not set, the function may still shift the images in the +horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the +useful image area. +@param alpha Free scaling parameter. If it is -1 or absent, the function performs the default +scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified +images are zoomed and shifted so that only valid pixels are visible (no black areas after +rectification). alpha=1 means that the rectified image is decimated and shifted so that all the +pixels from the original images from the cameras are retained in the rectified images (no source +image pixels are lost). Obviously, any intermediate value yields an intermediate result between +those two extreme cases. +@param newImageSize New image resolution after rectification. The same size should be passed to +initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) +is passed (default), it is set to the original imageSize . Setting it to larger value can help you +preserve details in the original image, especially when there is a big radial distortion. +@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels +are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller +(see the picture below). +@param validPixROI2 Optional output rectangles inside the rectified images where all the pixels +are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller +(see the picture below). + +The function computes the rotation matrices for each camera that (virtually) make both camera image +planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies +the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate +as input. As output, it provides two rotation matrices and also two projection matrices in the new +coordinates. The function distinguishes the following two cases: + +- **Horizontal stereo**: the first and the second camera views are shifted relative to each other + mainly along the x axis (with possible small vertical shift). In the rectified images, the + corresponding epipolar lines in the left and right cameras are horizontal and have the same + y-coordinate. P1 and P2 look like: + + \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] + + \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] + + where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if + CV_CALIB_ZERO_DISPARITY is set. + +- **Vertical stereo**: the first and the second camera views are shifted relative to each other + mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar + lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like: + + \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] + + \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] + + where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB_ZERO_DISPARITY is + set. + +As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera +matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to +initialize the rectification map for each camera. + +See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through +the corresponding image regions. This means that the images are well rectified, which is what most +stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that +their interiors are all valid pixels. + +![image](pics/stereo_undistort.jpg) + */ +CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, + InputArray cameraMatrix2, InputArray distCoeffs2, + Size imageSize, InputArray R, InputArray T, + OutputArray R1, OutputArray R2, + OutputArray P1, OutputArray P2, + OutputArray Q, int flags = CALIB_ZERO_DISPARITY, + double alpha = -1, Size newImageSize = Size(), + CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 ); + +/** @brief Computes a rectification transform for an uncalibrated stereo camera. + +@param points1 Array of feature points in the first image. +@param points2 The corresponding points in the second image. The same formats as in +findFundamentalMat are supported. +@param F Input fundamental matrix. It can be computed from the same set of point pairs using +findFundamentalMat . +@param imgSize Size of the image. +@param H1 Output rectification homography matrix for the first image. +@param H2 Output rectification homography matrix for the second image. +@param threshold Optional threshold used to filter out the outliers. If the parameter is greater +than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points +for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are +rejected prior to computing the homographies. Otherwise,all the points are considered inliers. + +The function computes the rectification transformations without knowing intrinsic parameters of the +cameras and their relative position in the space, which explains the suffix "uncalibrated". Another +related difference from stereoRectify is that the function outputs not the rectification +transformations in the object (3D) space, but the planar perspective transformations encoded by the +homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 . + +@note + While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily + depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, + it would be better to correct it before computing the fundamental matrix and calling this + function. For example, distortion coefficients can be estimated for each head of stereo camera + separately by using calibrateCamera . Then, the images can be corrected using undistort , or + just the point coordinates can be corrected with undistortPoints . + */ +CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2, + InputArray F, Size imgSize, + OutputArray H1, OutputArray H2, + double threshold = 5 ); + +//! computes the rectification transformations for 3-head camera, where all the heads are on the same line. +CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1, + InputArray cameraMatrix2, InputArray distCoeffs2, + InputArray cameraMatrix3, InputArray distCoeffs3, + InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3, + Size imageSize, InputArray R12, InputArray T12, + InputArray R13, InputArray T13, + OutputArray R1, OutputArray R2, OutputArray R3, + OutputArray P1, OutputArray P2, OutputArray P3, + OutputArray Q, double alpha, Size newImgSize, + CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags ); + +/** @brief Returns the new camera matrix based on the free scaling parameter. + +@param cameraMatrix Input camera matrix. +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param imageSize Original image size. +@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are +valid) and 1 (when all the source image pixels are retained in the undistorted image). See +stereoRectify for details. +@param newImgSize Image size after rectification. By default,it is set to imageSize . +@param validPixROI Optional output rectangle that outlines all-good-pixels region in the +undistorted image. See roi1, roi2 description in stereoRectify . +@param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the +principal point should be at the image center or not. By default, the principal point is chosen to +best fit a subset of the source image (determined by alpha) to the corrected image. +@return new_camera_matrix Output new camera matrix. + +The function computes and returns the optimal new camera matrix based on the free scaling parameter. +By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original +image pixels if there is valuable information in the corners alpha=1 , or get something in between. +When alpha\>0 , the undistortion result is likely to have some black pixels corresponding to +"virtual" pixels outside of the captured distorted image. The original camera matrix, distortion +coefficients, the computed new camera matrix, and newImageSize should be passed to +initUndistortRectifyMap to produce the maps for remap . + */ +CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, + Size imageSize, double alpha, Size newImgSize = Size(), + CV_OUT Rect* validPixROI = 0, + bool centerPrincipalPoint = false); + +/** @brief Converts points from Euclidean to homogeneous space. + +@param src Input vector of N-dimensional points. +@param dst Output vector of N+1-dimensional points. + +The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of +point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1). + */ +CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Converts points from homogeneous to Euclidean space. + +@param src Input vector of N-dimensional points. +@param dst Output vector of N-1-dimensional points. + +The function converts points homogeneous to Euclidean space using perspective projection. That is, +each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the +output point coordinates will be (0,0,0,...). + */ +CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Converts points to/from homogeneous coordinates. + +@param src Input array or vector of 2D, 3D, or 4D points. +@param dst Output vector of 2D, 3D, or 4D points. + +The function converts 2D or 3D points from/to homogeneous coordinates by calling either +convertPointsToHomogeneous or convertPointsFromHomogeneous. + +@note The function is obsolete. Use one of the previous two functions instead. + */ +CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Calculates a fundamental matrix from the corresponding points in two images. + +@param points1 Array of N points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param method Method for computing a fundamental matrix. +- **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$ +- **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$ +- **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$ +- **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$ +@param param1 Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param param2 Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level +of confidence (probability) that the estimated matrix is correct. +@param mask + +The epipolar geometry is described by the following equation: + +\f[[p_2; 1]^T F [p_1; 1] = 0\f] + +where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the +second images, respectively. + +The function calculates the fundamental matrix using one of four methods listed above and returns +the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point +algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3 +matrices sequentially). + +The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the +epipolar lines corresponding to the specified points. It can also be passed to +stereoRectifyUncalibrated to compute the rectification transformation. : +@code + // Example. Estimation of fundamental matrix using the RANSAC algorithm + int point_count = 100; + vector points1(point_count); + vector points2(point_count); + + // initialize the points here ... + for( int i = 0; i < point_count; i++ ) + { + points1[i] = ...; + points2[i] = ...; + } + + Mat fundamental_matrix = + findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99); +@endcode + */ +CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, + int method = FM_RANSAC, + double param1 = 3., double param2 = 0.99, + OutputArray mask = noArray() ); + +/** @overload */ +CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2, + OutputArray mask, int method = FM_RANSAC, + double param1 = 3., double param2 = 0.99 ); + +/** @brief Calculates an essential matrix from the corresponding points in two images. + +@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should +be floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param method Method for computing a fundamental matrix. +- **RANSAC** for the RANSAC algorithm. +- **MEDS** for the LMedS algorithm. +@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of +confidence (probability) that the estimated matrix is correct. +@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 +for the other points. The array is computed only in the RANSAC and LMedS methods. + +This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 . +@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation: + +\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f] + +where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the +second images, respectively. The result of this function may be passed further to +decomposeEssentialMat or recoverPose to recover the relative pose between cameras. + */ +CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, + InputArray cameraMatrix, int method = RANSAC, + double prob = 0.999, double threshold = 1.0, + OutputArray mask = noArray() ); + +/** @overload +@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should +be floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param focal focal length of the camera. Note that this function assumes that points1 and points2 +are feature points from cameras with same focal length and principal point. +@param pp principal point of the camera. +@param method Method for computing a fundamental matrix. +- **RANSAC** for the RANSAC algorithm. +- **LMEDS** for the LMedS algorithm. +@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of +confidence (probability) that the estimated matrix is correct. +@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 +for the other points. The array is computed only in the RANSAC and LMedS methods. + +This function differs from the one above that it computes camera matrix from focal length and +principal point: + +\f[K = +\begin{bmatrix} +f & 0 & x_{pp} \\ +0 & f & y_{pp} \\ +0 & 0 & 1 +\end{bmatrix}\f] + */ +CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, + double focal = 1.0, Point2d pp = Point2d(0, 0), + int method = RANSAC, double prob = 0.999, + double threshold = 1.0, OutputArray mask = noArray() ); + +/** @brief Decompose an essential matrix to possible rotations and translation. + +@param E The input essential matrix. +@param R1 One possible rotation matrix. +@param R2 Another possible rotation matrix. +@param t One possible translation. + +This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4 +possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By +decomposing E, you can only get the direction of the translation, so the function returns unit t. + */ +CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t ); + +/** @brief Recover relative camera rotation and translation from an estimated essential matrix and the +corresponding points in two images, using cheirality check. Returns the number of inliers which pass +the check. + +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param R Recovered relative rotation. +@param t Recoverd relative translation. +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. +This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible +pose hypotheses by doing cheirality check. The cheirality check basically means that the +triangulated 3D points should have positive depth. Some details can be found in @cite Nister03 . + +This function can be used to process output E and mask from findEssentialMat. In this scenario, +points1 and points2 are the same input for findEssentialMat. : +@code + // Example. Estimation of fundamental matrix using the RANSAC algorithm + int point_count = 100; + vector points1(point_count); + vector points2(point_count); + + // initialize the points here ... + for( int i = 0; i < point_count; i++ ) + { + points1[i] = ...; + points2[i] = ...; + } + + // cametra matrix with both focal lengths = 1, and principal point = (0, 0) + Mat cameraMatrix = Mat::eye(3, 3, CV_64F); + + Mat E, R, t, mask; + + E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); + recoverPose(E, points1, points2, cameraMatrix, R, t, mask); +@endcode + */ +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + InputArray cameraMatrix, OutputArray R, OutputArray t, + InputOutputArray mask = noArray() ); + +/** @overload +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param R Recovered relative rotation. +@param t Recoverd relative translation. +@param focal Focal length of the camera. Note that this function assumes that points1 and points2 +are feature points from cameras with same focal length and principal point. +@param pp principal point of the camera. +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. + +This function differs from the one above that it computes camera matrix from focal length and +principal point: + +\f[K = +\begin{bmatrix} +f & 0 & x_{pp} \\ +0 & f & y_{pp} \\ +0 & 0 & 1 +\end{bmatrix}\f] + */ +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + OutputArray R, OutputArray t, + double focal = 1.0, Point2d pp = Point2d(0, 0), + InputOutputArray mask = noArray() ); + +/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image. + +@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or +vector\ . +@param whichImage Index of the image (1 or 2) that contains the points . +@param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . +@param lines Output vector of the epipolar lines corresponding to the points in the other image. +Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ . + +For every point in one of the two images of a stereo pair, the function finds the equation of the +corresponding epipolar line in the other image. + +From the fundamental matrix definition (see findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second +image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as: + +\f[l^{(2)}_i = F p^{(1)}_i\f] + +And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as: + +\f[l^{(1)}_i = F^T p^{(2)}_i\f] + +Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ . + */ +CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage, + InputArray F, OutputArray lines ); + +/** @brief Reconstructs points by triangulation. + +@param projMatr1 3x4 projection matrix of the first camera. +@param projMatr2 3x4 projection matrix of the second camera. +@param projPoints1 2xN array of feature points in the first image. In case of c++ version it can +be also a vector of feature points or two-channel matrix of size 1xN or Nx1. +@param projPoints2 2xN array of corresponding points in the second image. In case of c++ version +it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. +@param points4D 4xN array of reconstructed points in homogeneous coordinates. + +The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their +observations with a stereo camera. Projections matrices can be obtained from stereoRectify. + +@note + Keep in mind that all input data should be of float type in order for this function to work. + +@sa + reprojectImageTo3D + */ +CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2, + InputArray projPoints1, InputArray projPoints2, + OutputArray points4D ); + +/** @brief Refines coordinates of corresponding points. + +@param F 3x3 fundamental matrix. +@param points1 1xN array containing the first set of points. +@param points2 1xN array containing the second set of points. +@param newPoints1 The optimized points1. +@param newPoints2 The optimized points2. + +The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). +For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it +computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric +error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the +geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint +\f$newPoints2^T * F * newPoints1 = 0\f$ . + */ +CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2, + OutputArray newPoints1, OutputArray newPoints2 ); + +/** @brief Filters off small noise blobs (speckles) in the disparity map + +@param img The input 16-bit signed disparity image +@param newVal The disparity value used to paint-off the speckles +@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not +affected by the algorithm +@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same +blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point +disparity map, where disparity values are multiplied by 16, this scale factor should be taken into +account when specifying this parameter value. +@param buf The optional temporary buffer to avoid memory allocation within the function. + */ +CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal, + int maxSpeckleSize, double maxDiff, + InputOutputArray buf = noArray() ); + +//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify()) +CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2, + int minDisparity, int numberOfDisparities, + int SADWindowSize ); + +//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm +CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost, + int minDisparity, int numberOfDisparities, + int disp12MaxDisp = 1 ); + +/** @brief Reprojects a disparity image to 3D space. + +@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit +floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no +fractional bits. +@param _3dImage Output 3-channel floating-point image of the same size as disparity . Each +element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity +map. +@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify. +@param handleMissingValues Indicates, whether the function should handle missing values (i.e. +points where the disparity was not computed). If handleMissingValues=true, then pixels with the +minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed +to 3D points with a very large Z value (currently set to 10000). +@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F +depth. ddepth can also be set to CV_16S, CV_32S or CV_32F. + +The function transforms a single-channel disparity map to a 3-channel image representing a 3D +surface. That is, for each pixel (x,y) andthe corresponding disparity d=disparity(x,y) , it +computes: + +\f[\begin{array}{l} [X \; Y \; Z \; W]^T = \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f] + +The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by +stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use +perspectiveTransform . + */ +CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity, + OutputArray _3dImage, InputArray Q, + bool handleMissingValues = false, + int ddepth = -1 ); + +/** @brief Calculates the Sampson Distance between two points. + +The function sampsonDistance calculates and returns the first order approximation of the geometric error as: +\f[sd( \texttt{pt1} , \texttt{pt2} )= \frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2}{(\texttt{F} \cdot \texttt{pt1})(0) + (\texttt{F} \cdot \texttt{pt1})(1) + (\texttt{F}^t \cdot \texttt{pt2})(0) + (\texttt{F}^t \cdot \texttt{pt2})(1)}\f] +The fundamental matrix may be calculated using the cv::findFundamentalMat function. See HZ 11.4.3 for details. +@param pt1 first homogeneous 2d point +@param pt2 second homogeneous 2d point +@param F fundamental matrix +*/ +CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F); + +/** @brief Computes an optimal affine transformation between two 3D point sets. + +@param src First input 3D point set. +@param dst Second input 3D point set. +@param out Output 3D affine transformation matrix \f$3 \times 4\f$ . +@param inliers Output vector indicating which points are inliers. +@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as +an inlier. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. + +The function estimates an optimal 3D affine transformation between two 3D point sets using the +RANSAC algorithm. + */ +CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst, + OutputArray out, OutputArray inliers, + double ransacThreshold = 3, double confidence = 0.99); + +/** @brief Computes an optimal affine transformation between two 2D point sets. + +@param from First input 2D point set. +@param to Second input 2D point set. +@param inliers Output vector indicating which points are inliers. +@param method Robust method used to compute tranformation. The following methods are possible: +- cv::RANSAC - RANSAC-based robust method +- cv::LMEDS - Least-Median robust method +RANSAC is the default method. +@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider +a point as an inlier. Applies only to RANSAC. +@param maxIters The maximum number of robust method iterations, 2000 is the maximum it can be. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. +@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). +Passing 0 will disable refining, so the output matrix will be output of robust method. + +@return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation +could not be estimated. + +The function estimates an optimal 2D affine transformation between two 2D point sets using the +selected robust algorithm. + +The computed transformation is then refined further (using only inliers) with the +Levenberg-Marquardt method to reduce the re-projection error even more. + +@note +The RANSAC method can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. + +@sa estimateAffinePartial2D, getAffineTransform +*/ +CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray from, InputArray to, OutputArray inliers = noArray(), + int method = RANSAC, double ransacReprojThreshold = 3, + size_t maxIters = 2000, double confidence = 0.99, + size_t refineIters = 10); + +/** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between +two 2D point sets. + +@param from First input 2D point set. +@param to Second input 2D point set. +@param inliers Output vector indicating which points are inliers. +@param method Robust method used to compute tranformation. The following methods are possible: +- cv::RANSAC - RANSAC-based robust method +- cv::LMEDS - Least-Median robust method +RANSAC is the default method. +@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider +a point as an inlier. Applies only to RANSAC. +@param maxIters The maximum number of robust method iterations, 2000 is the maximum it can be. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. +@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). +Passing 0 will disable refining, so the output matrix will be output of robust method. + +@return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ or +empty matrix if transformation could not be estimated. + +The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to +combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust +estimation. + +The computed transformation is then refined further (using only inliers) with the +Levenberg-Marquardt method to reduce the re-projection error even more. + +Estimated transformation matrix is: +\f[ \begin{bmatrix} \cos(\theta)s & -\sin(\theta)s & tx \\ + \sin(\theta)s & \cos(\theta)s & ty +\end{bmatrix} \f] +Where \f$ \theta \f$ is the rotation angle, \f$ s \f$ the scaling factor and \f$ tx, ty \f$ are +translations in \f$ x, y \f$ axes respectively. + +@note +The RANSAC method can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. + +@sa estimateAffine2D, getAffineTransform +*/ +CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, OutputArray inliers = noArray(), + int method = RANSAC, double ransacReprojThreshold = 3, + size_t maxIters = 2000, double confidence = 0.99, + size_t refineIters = 10); + +/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). + +@param H The input homography matrix between two images. +@param K The input intrinsic camera calibration matrix. +@param rotations Array of rotation matrices. +@param translations Array of translation matrices. +@param normals Array of plane normal matrices. + +This function extracts relative camera motion between two views observing a planar object from the +homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function +may return up to four mathematical solution sets. At least two of the solutions may further be +invalidated if point correspondences are available by applying positive depth constraint (all points +must be in front of the camera). The decomposition method is described in detail in @cite Malis . + */ +CV_EXPORTS_W int decomposeHomographyMat(InputArray H, + InputArray K, + OutputArrayOfArrays rotations, + OutputArrayOfArrays translations, + OutputArrayOfArrays normals); + +/** @brief The base class for stereo correspondence algorithms. + */ +class CV_EXPORTS_W StereoMatcher : public Algorithm +{ +public: + enum { DISP_SHIFT = 4, + DISP_SCALE = (1 << DISP_SHIFT) + }; + + /** @brief Computes disparity map for the specified stereo pair + + @param left Left 8-bit single-channel image. + @param right Right image of the same size and the same type as the left one. + @param disparity Output disparity map. It has the same size as the input images. Some algorithms, + like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value + has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map. + */ + CV_WRAP virtual void compute( InputArray left, InputArray right, + OutputArray disparity ) = 0; + + CV_WRAP virtual int getMinDisparity() const = 0; + CV_WRAP virtual void setMinDisparity(int minDisparity) = 0; + + CV_WRAP virtual int getNumDisparities() const = 0; + CV_WRAP virtual void setNumDisparities(int numDisparities) = 0; + + CV_WRAP virtual int getBlockSize() const = 0; + CV_WRAP virtual void setBlockSize(int blockSize) = 0; + + CV_WRAP virtual int getSpeckleWindowSize() const = 0; + CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0; + + CV_WRAP virtual int getSpeckleRange() const = 0; + CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0; + + CV_WRAP virtual int getDisp12MaxDiff() const = 0; + CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0; +}; + + +/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and +contributed to OpenCV by K. Konolige. + */ +class CV_EXPORTS_W StereoBM : public StereoMatcher +{ +public: + enum { PREFILTER_NORMALIZED_RESPONSE = 0, + PREFILTER_XSOBEL = 1 + }; + + CV_WRAP virtual int getPreFilterType() const = 0; + CV_WRAP virtual void setPreFilterType(int preFilterType) = 0; + + CV_WRAP virtual int getPreFilterSize() const = 0; + CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0; + + CV_WRAP virtual int getPreFilterCap() const = 0; + CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; + + CV_WRAP virtual int getTextureThreshold() const = 0; + CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0; + + CV_WRAP virtual int getUniquenessRatio() const = 0; + CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; + + CV_WRAP virtual int getSmallerBlockSize() const = 0; + CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0; + + CV_WRAP virtual Rect getROI1() const = 0; + CV_WRAP virtual void setROI1(Rect roi1) = 0; + + CV_WRAP virtual Rect getROI2() const = 0; + CV_WRAP virtual void setROI2(Rect roi2) = 0; + + /** @brief Creates StereoBM object + + @param numDisparities the disparity search range. For each pixel algorithm will find the best + disparity from 0 (default minimum disparity) to numDisparities. The search range can then be + shifted by changing the minimum disparity. + @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd + (as the block is centered at the current pixel). Larger block size implies smoother, though less + accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher + chance for algorithm to find a wrong correspondence. + + The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for + a specific stereo pair. + */ + CV_WRAP static Ptr create(int numDisparities = 0, int blockSize = 21); +}; + +/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original +one as follows: + +- By default, the algorithm is single-pass, which means that you consider only 5 directions +instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the +algorithm but beware that it may consume a lot of memory. +- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the +blocks to single pixels. +- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi +sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well. +- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for +example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness +check, quadratic interpolation and speckle filtering). + +@note + - (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found + at opencv_source_code/samples/python/stereo_match.py + */ +class CV_EXPORTS_W StereoSGBM : public StereoMatcher +{ +public: + enum + { + MODE_SGBM = 0, + MODE_HH = 1, + MODE_SGBM_3WAY = 2 + }; + + CV_WRAP virtual int getPreFilterCap() const = 0; + CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; + + CV_WRAP virtual int getUniquenessRatio() const = 0; + CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; + + CV_WRAP virtual int getP1() const = 0; + CV_WRAP virtual void setP1(int P1) = 0; + + CV_WRAP virtual int getP2() const = 0; + CV_WRAP virtual void setP2(int P2) = 0; + + CV_WRAP virtual int getMode() const = 0; + CV_WRAP virtual void setMode(int mode) = 0; + + /** @brief Creates StereoSGBM object + + @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes + rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. + @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than + zero. In the current implementation, this parameter must be divisible by 16. + @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be + somewhere in the 3..11 range. + @param P1 The first parameter controlling the disparity smoothness. See below. + @param P2 The second parameter controlling the disparity smoothness. The larger the values are, + the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1 + between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor + pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good + P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and + 32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively). + @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right + disparity check. Set it to a non-positive value to disable the check. + @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first + computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval. + The result values are passed to the Birchfield-Tomasi pixel cost function. + @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function + value should "win" the second best value to consider the found match correct. Normally, a value + within the 5-15 range is good enough. + @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles + and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the + 50-200 range. + @param speckleRange Maximum disparity variation within each connected component. If you do speckle + filtering, set the parameter to a positive value, it will be implicitly multiplied by 16. + Normally, 1 or 2 is good enough. + @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming + algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and + huge for HD-size pictures. By default, it is set to false . + + The first constructor initializes StereoSGBM with all the default parameters. So, you only have to + set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter + to a custom value. + */ + CV_WRAP static Ptr create(int minDisparity, int numDisparities, int blockSize, + int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, + int preFilterCap = 0, int uniquenessRatio = 0, + int speckleWindowSize = 0, int speckleRange = 0, + int mode = StereoSGBM::MODE_SGBM); +}; + +//! @} calib3d + +/** @brief The methods in this namespace use a so-called fisheye camera model. + @ingroup calib3d_fisheye +*/ +namespace fisheye +{ +//! @addtogroup calib3d_fisheye +//! @{ + + enum{ + CALIB_USE_INTRINSIC_GUESS = 1 << 0, + CALIB_RECOMPUTE_EXTRINSIC = 1 << 1, + CALIB_CHECK_COND = 1 << 2, + CALIB_FIX_SKEW = 1 << 3, + CALIB_FIX_K1 = 1 << 4, + CALIB_FIX_K2 = 1 << 5, + CALIB_FIX_K3 = 1 << 6, + CALIB_FIX_K4 = 1 << 7, + CALIB_FIX_INTRINSIC = 1 << 8, + CALIB_FIX_PRINCIPAL_POINT = 1 << 9 + }; + + /** @brief Projects points using fisheye model + + @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\ ), where N is + the number of points in the view. + @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or + vector\. + @param affine + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param alpha The skew coefficient. + @param jacobian Optional output 2Nx15 jacobian matrix of derivatives of image points with respect + to components of the focal lengths, coordinates of the principal point, distortion coefficients, + rotation vector, translation vector, and the skew. In the old interface different components of + the jacobian are returned via different output parameters. + + The function computes projections of 3D points to the image plane given intrinsic and extrinsic + camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of + image points coordinates (as functions of all the input parameters) with respect to the particular + parameters, intrinsic and/or extrinsic. + */ + CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine, + InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); + + /** @overload */ + CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, + InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); + + /** @brief Distorts 2D points using fisheye model. + + @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is + the number of points in the view. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param alpha The skew coefficient. + @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . + + Note that the function assumes the camera matrix of the undistorted points to be indentity. + This means if you want to transform back points undistorted with undistortPoints() you have to + multiply them with \f$P^{-1}\f$. + */ + CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0); + + /** @brief Undistorts 2D points using fisheye model + + @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is the + number of points in the view. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . + */ + CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, + InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray()); + + /** @brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero + distortion is used, if R or P is empty identity matrixes are used. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param size Undistorted image size. + @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() + for details. + @param map1 The first output map. + @param map2 The second output map. + */ + CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P, + const cv::Size& size, int m1type, OutputArray map1, OutputArray map2); + + /** @brief Transforms an image to compensate for fisheye lens distortion. + + @param distorted image with fisheye lens distortion. + @param undistorted Output image with compensated fisheye lens distortion. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param Knew Camera matrix of the distorted image. By default, it is the identity matrix but you + may additionally scale and shift the result by using a different matrix. + @param new_size + + The function transforms an image to compensate radial and tangential lens distortion. + + The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap + (with bilinear interpolation). See the former function for details of the transformation being + performed. + + See below the results of undistortImage. + - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, + k_4, k_5, k_6) of distortion were optimized under calibration) + - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, + k_3, k_4) of fisheye distortion were optimized under calibration) + - c\) original image was captured with fisheye lens + + Pictures a) and b) almost the same. But if we consider points of image located far from the center + of image, we can notice that on image a) these points are distorted. + + ![image](pics/fisheye_undistorted.jpg) + */ + CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, + InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size()); + + /** @brief Estimates new camera matrix for undistortion or rectification. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param image_size + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param balance Sets the new focal length in range between the min focal length and the max focal + length. Balance is in range of [0, 1]. + @param new_size + @param fov_scale Divisor for new focal length. + */ + CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R, + OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0); + + /** @brief Performs camera calibaration + + @param objectPoints vector of vectors of calibration pattern points in the calibration pattern + coordinate space. + @param imagePoints vector of vectors of the projections of calibration pattern points. + imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to + objectPoints[i].size() for each i. + @param image_size Size of the image used only to initialize the intrinsic camera matrix. + @param K Output 3x3 floating-point camera matrix + \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If + fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be + initialized before calling the function. + @param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. + That is, each k-th rotation vector together with the corresponding k-th translation vector (see + the next output parameter description) brings the calibration pattern from the model coordinate + space (in which object points are specified) to the world coordinate space, that is, a real + position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). + @param tvecs Output vector of translation vectors estimated for each pattern view. + @param flags Different flags that may be zero or a combination of the following values: + - **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of + fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image + center ( imageSize is used), and focal distances are computed in a least-squares fashion. + - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration + of intrinsic optimization. + - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. + - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. + - **fisheye::CALIB_FIX_K1..fisheye::CALIB_FIX_K4** Selected distortion coefficients + are set to zeros and stay zero. + - **fisheye::CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global +optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. + @param criteria Termination criteria for the iterative optimization algorithm. + */ + CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size, + InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); + + /** @brief Stereo rectification for fisheye camera model + + @param K1 First camera matrix. + @param D1 First camera distortion parameters. + @param K2 Second camera matrix. + @param D2 Second camera distortion parameters. + @param imageSize Size of the image used for stereo calibration. + @param R Rotation matrix between the coordinate systems of the first and the second + cameras. + @param tvec Translation vector between coordinate systems of the cameras. + @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. + @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. + @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first + camera. + @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second + camera. + @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). + @param flags Operation flags that may be zero or CV_CALIB_ZERO_DISPARITY . If the flag is set, + the function makes the principal points of each camera have the same pixel coordinates in the + rectified views. And if the flag is not set, the function may still shift the images in the + horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the + useful image area. + @param newImageSize New image resolution after rectification. The same size should be passed to + initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) + is passed (default), it is set to the original imageSize . Setting it to larger value can help you + preserve details in the original image, especially when there is a big radial distortion. + @param balance Sets the new focal length in range between the min focal length and the max focal + length. Balance is in range of [0, 1]. + @param fov_scale Divisor for new focal length. + */ + CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec, + OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(), + double balance = 0.0, double fov_scale = 1.0); + + /** @brief Performs stereo calibration + + @param objectPoints Vector of vectors of the calibration pattern points. + @param imagePoints1 Vector of vectors of the projections of the calibration pattern points, + observed by the first camera. + @param imagePoints2 Vector of vectors of the projections of the calibration pattern points, + observed by the second camera. + @param K1 Input/output first camera matrix: + \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If + any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CV_CALIB_FIX_INTRINSIC are specified, + some or all of the matrix components must be initialized. + @param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements. + @param K2 Input/output second camera matrix. The parameter is similar to K1 . + @param D2 Input/output lens distortion coefficients for the second camera. The parameter is + similar to D1 . + @param imageSize Size of the image used only to initialize intrinsic camera matrix. + @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. + @param T Output translation vector between the coordinate systems of the cameras. + @param flags Different flags that may be zero or a combination of the following values: + - **fisheye::CV_CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices + are estimated. + - **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of + fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image + center (imageSize is used), and focal distances are computed in a least-squares fashion. + - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration + of intrinsic optimization. + - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. + - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. + - **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay + zero. + @param criteria Termination criteria for the iterative optimization algorithm. + */ + CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, + OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); + +//! @} calib3d_fisheye +} + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/calib3d/calib3d_c.h" +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/calib3d/calib3d.hpp b/thirdparty/linux/include/opencv2/calib3d/calib3d.hpp new file mode 100644 index 0000000..b3da45e --- /dev/null +++ b/thirdparty/linux/include/opencv2/calib3d/calib3d.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/calib3d.hpp" diff --git a/thirdparty/linux/include/opencv2/calib3d/calib3d_c.h b/thirdparty/linux/include/opencv2/calib3d/calib3d_c.h new file mode 100644 index 0000000..1069b58 --- /dev/null +++ b/thirdparty/linux/include/opencv2/calib3d/calib3d_c.h @@ -0,0 +1,426 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CALIB3D_C_H +#define OPENCV_CALIB3D_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup calib3d_c + @{ + */ + +/****************************************************************************************\ +* Camera Calibration, Pose Estimation and Stereo * +\****************************************************************************************/ + +typedef struct CvPOSITObject CvPOSITObject; + +/* Allocates and initializes CvPOSITObject structure before doing cvPOSIT */ +CVAPI(CvPOSITObject*) cvCreatePOSITObject( CvPoint3D32f* points, int point_count ); + + +/* Runs POSIT (POSe from ITeration) algorithm for determining 3d position of + an object given its model and projection in a weak-perspective case */ +CVAPI(void) cvPOSIT( CvPOSITObject* posit_object, CvPoint2D32f* image_points, + double focal_length, CvTermCriteria criteria, + float* rotation_matrix, float* translation_vector); + +/* Releases CvPOSITObject structure */ +CVAPI(void) cvReleasePOSITObject( CvPOSITObject** posit_object ); + +/* updates the number of RANSAC iterations */ +CVAPI(int) cvRANSACUpdateNumIters( double p, double err_prob, + int model_points, int max_iters ); + +CVAPI(void) cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst ); + +/* Calculates fundamental matrix given a set of corresponding points */ +#define CV_FM_7POINT 1 +#define CV_FM_8POINT 2 + +#define CV_LMEDS 4 +#define CV_RANSAC 8 + +#define CV_FM_LMEDS_ONLY CV_LMEDS +#define CV_FM_RANSAC_ONLY CV_RANSAC +#define CV_FM_LMEDS CV_LMEDS +#define CV_FM_RANSAC CV_RANSAC + +enum +{ + CV_ITERATIVE = 0, + CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" + CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem" + CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP" +}; + +CVAPI(int) cvFindFundamentalMat( const CvMat* points1, const CvMat* points2, + CvMat* fundamental_matrix, + int method CV_DEFAULT(CV_FM_RANSAC), + double param1 CV_DEFAULT(3.), double param2 CV_DEFAULT(0.99), + CvMat* status CV_DEFAULT(NULL) ); + +/* For each input point on one of images + computes parameters of the corresponding + epipolar line on the other image */ +CVAPI(void) cvComputeCorrespondEpilines( const CvMat* points, + int which_image, + const CvMat* fundamental_matrix, + CvMat* correspondent_lines ); + +/* Triangulation functions */ + +CVAPI(void) cvTriangulatePoints(CvMat* projMatr1, CvMat* projMatr2, + CvMat* projPoints1, CvMat* projPoints2, + CvMat* points4D); + +CVAPI(void) cvCorrectMatches(CvMat* F, CvMat* points1, CvMat* points2, + CvMat* new_points1, CvMat* new_points2); + + +/* Computes the optimal new camera matrix according to the free scaling parameter alpha: + alpha=0 - only valid pixels will be retained in the undistorted image + alpha=1 - all the source image pixels will be retained in the undistorted image +*/ +CVAPI(void) cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix, + const CvMat* dist_coeffs, + CvSize image_size, double alpha, + CvMat* new_camera_matrix, + CvSize new_imag_size CV_DEFAULT(cvSize(0,0)), + CvRect* valid_pixel_ROI CV_DEFAULT(0), + int center_principal_point CV_DEFAULT(0)); + +/* Converts rotation vector to rotation matrix or vice versa */ +CVAPI(int) cvRodrigues2( const CvMat* src, CvMat* dst, + CvMat* jacobian CV_DEFAULT(0) ); + +/* Finds perspective transformation between the object plane and image (view) plane */ +CVAPI(int) cvFindHomography( const CvMat* src_points, + const CvMat* dst_points, + CvMat* homography, + int method CV_DEFAULT(0), + double ransacReprojThreshold CV_DEFAULT(3), + CvMat* mask CV_DEFAULT(0), + int maxIters CV_DEFAULT(2000), + double confidence CV_DEFAULT(0.995)); + +/* Computes RQ decomposition for 3x3 matrices */ +CVAPI(void) cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ, + CvMat *matrixQx CV_DEFAULT(NULL), + CvMat *matrixQy CV_DEFAULT(NULL), + CvMat *matrixQz CV_DEFAULT(NULL), + CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); + +/* Computes projection matrix decomposition */ +CVAPI(void) cvDecomposeProjectionMatrix( const CvMat *projMatr, CvMat *calibMatr, + CvMat *rotMatr, CvMat *posVect, + CvMat *rotMatrX CV_DEFAULT(NULL), + CvMat *rotMatrY CV_DEFAULT(NULL), + CvMat *rotMatrZ CV_DEFAULT(NULL), + CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); + +/* Computes d(AB)/dA and d(AB)/dB */ +CVAPI(void) cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB ); + +/* Computes r3 = rodrigues(rodrigues(r2)*rodrigues(r1)), + t3 = rodrigues(r2)*t1 + t2 and the respective derivatives */ +CVAPI(void) cvComposeRT( const CvMat* _rvec1, const CvMat* _tvec1, + const CvMat* _rvec2, const CvMat* _tvec2, + CvMat* _rvec3, CvMat* _tvec3, + CvMat* dr3dr1 CV_DEFAULT(0), CvMat* dr3dt1 CV_DEFAULT(0), + CvMat* dr3dr2 CV_DEFAULT(0), CvMat* dr3dt2 CV_DEFAULT(0), + CvMat* dt3dr1 CV_DEFAULT(0), CvMat* dt3dt1 CV_DEFAULT(0), + CvMat* dt3dr2 CV_DEFAULT(0), CvMat* dt3dt2 CV_DEFAULT(0) ); + +/* Projects object points to the view plane using + the specified extrinsic and intrinsic camera parameters */ +CVAPI(void) cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector, + const CvMat* translation_vector, const CvMat* camera_matrix, + const CvMat* distortion_coeffs, CvMat* image_points, + CvMat* dpdrot CV_DEFAULT(NULL), CvMat* dpdt CV_DEFAULT(NULL), + CvMat* dpdf CV_DEFAULT(NULL), CvMat* dpdc CV_DEFAULT(NULL), + CvMat* dpddist CV_DEFAULT(NULL), + double aspect_ratio CV_DEFAULT(0)); + +/* Finds extrinsic camera parameters from + a few known corresponding point pairs and intrinsic parameters */ +CVAPI(void) cvFindExtrinsicCameraParams2( const CvMat* object_points, + const CvMat* image_points, + const CvMat* camera_matrix, + const CvMat* distortion_coeffs, + CvMat* rotation_vector, + CvMat* translation_vector, + int use_extrinsic_guess CV_DEFAULT(0) ); + +/* Computes initial estimate of the intrinsic camera parameters + in case of planar calibration target (e.g. chessboard) */ +CVAPI(void) cvInitIntrinsicParams2D( const CvMat* object_points, + const CvMat* image_points, + const CvMat* npoints, CvSize image_size, + CvMat* camera_matrix, + double aspect_ratio CV_DEFAULT(1.) ); + +#define CV_CALIB_CB_ADAPTIVE_THRESH 1 +#define CV_CALIB_CB_NORMALIZE_IMAGE 2 +#define CV_CALIB_CB_FILTER_QUADS 4 +#define CV_CALIB_CB_FAST_CHECK 8 + +// Performs a fast check if a chessboard is in the input image. This is a workaround to +// a problem of cvFindChessboardCorners being slow on images with no chessboard +// - src: input image +// - size: chessboard size +// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, +// 0 if there is no chessboard, -1 in case of error +CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size); + + /* Detects corners on a chessboard calibration pattern */ +CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size, + CvPoint2D32f* corners, + int* corner_count CV_DEFAULT(NULL), + int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) ); + +/* Draws individual chessboard corners or the whole chessboard detected */ +CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size, + CvPoint2D32f* corners, + int count, int pattern_was_found ); + +#define CV_CALIB_USE_INTRINSIC_GUESS 1 +#define CV_CALIB_FIX_ASPECT_RATIO 2 +#define CV_CALIB_FIX_PRINCIPAL_POINT 4 +#define CV_CALIB_ZERO_TANGENT_DIST 8 +#define CV_CALIB_FIX_FOCAL_LENGTH 16 +#define CV_CALIB_FIX_K1 32 +#define CV_CALIB_FIX_K2 64 +#define CV_CALIB_FIX_K3 128 +#define CV_CALIB_FIX_K4 2048 +#define CV_CALIB_FIX_K5 4096 +#define CV_CALIB_FIX_K6 8192 +#define CV_CALIB_RATIONAL_MODEL 16384 +#define CV_CALIB_THIN_PRISM_MODEL 32768 +#define CV_CALIB_FIX_S1_S2_S3_S4 65536 +#define CV_CALIB_TILTED_MODEL 262144 +#define CV_CALIB_FIX_TAUX_TAUY 524288 + +#define CV_CALIB_NINTRINSIC 18 + +/* Finds intrinsic and extrinsic camera parameters + from a few views of known calibration pattern */ +CVAPI(double) cvCalibrateCamera2( const CvMat* object_points, + const CvMat* image_points, + const CvMat* point_counts, + CvSize image_size, + CvMat* camera_matrix, + CvMat* distortion_coeffs, + CvMat* rotation_vectors CV_DEFAULT(NULL), + CvMat* translation_vectors CV_DEFAULT(NULL), + int flags CV_DEFAULT(0), + CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( + CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON)) ); + +/* Computes various useful characteristics of the camera from the data computed by + cvCalibrateCamera2 */ +CVAPI(void) cvCalibrationMatrixValues( const CvMat *camera_matrix, + CvSize image_size, + double aperture_width CV_DEFAULT(0), + double aperture_height CV_DEFAULT(0), + double *fovx CV_DEFAULT(NULL), + double *fovy CV_DEFAULT(NULL), + double *focal_length CV_DEFAULT(NULL), + CvPoint2D64f *principal_point CV_DEFAULT(NULL), + double *pixel_aspect_ratio CV_DEFAULT(NULL)); + +#define CV_CALIB_FIX_INTRINSIC 256 +#define CV_CALIB_SAME_FOCAL_LENGTH 512 + +/* Computes the transformation from one camera coordinate system to another one + from a few correspondent views of the same calibration target. Optionally, calibrates + both cameras */ +CVAPI(double) cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1, + const CvMat* image_points2, const CvMat* npoints, + CvMat* camera_matrix1, CvMat* dist_coeffs1, + CvMat* camera_matrix2, CvMat* dist_coeffs2, + CvSize image_size, CvMat* R, CvMat* T, + CvMat* E CV_DEFAULT(0), CvMat* F CV_DEFAULT(0), + int flags CV_DEFAULT(CV_CALIB_FIX_INTRINSIC), + CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( + CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6)) ); + +#define CV_CALIB_ZERO_DISPARITY 1024 + +/* Computes 3D rotations (+ optional shift) for each camera coordinate system to make both + views parallel (=> to make all the epipolar lines horizontal or vertical) */ +CVAPI(void) cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2, + const CvMat* dist_coeffs1, const CvMat* dist_coeffs2, + CvSize image_size, const CvMat* R, const CvMat* T, + CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, + CvMat* Q CV_DEFAULT(0), + int flags CV_DEFAULT(CV_CALIB_ZERO_DISPARITY), + double alpha CV_DEFAULT(-1), + CvSize new_image_size CV_DEFAULT(cvSize(0,0)), + CvRect* valid_pix_ROI1 CV_DEFAULT(0), + CvRect* valid_pix_ROI2 CV_DEFAULT(0)); + +/* Computes rectification transformations for uncalibrated pair of images using a set + of point correspondences */ +CVAPI(int) cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2, + const CvMat* F, CvSize img_size, + CvMat* H1, CvMat* H2, + double threshold CV_DEFAULT(5)); + + + +/* stereo correspondence parameters and functions */ + +#define CV_STEREO_BM_NORMALIZED_RESPONSE 0 +#define CV_STEREO_BM_XSOBEL 1 + +/* Block matching algorithm structure */ +typedef struct CvStereoBMState +{ + // pre-filtering (normalization of input images) + int preFilterType; // =CV_STEREO_BM_NORMALIZED_RESPONSE now + int preFilterSize; // averaging window size: ~5x5..21x21 + int preFilterCap; // the output of pre-filtering is clipped by [-preFilterCap,preFilterCap] + + // correspondence using Sum of Absolute Difference (SAD) + int SADWindowSize; // ~5x5..21x21 + int minDisparity; // minimum disparity (can be negative) + int numberOfDisparities; // maximum disparity - minimum disparity (> 0) + + // post-filtering + int textureThreshold; // the disparity is only computed for pixels + // with textured enough neighborhood + int uniquenessRatio; // accept the computed disparity d* only if + // SAD(d) >= SAD(d*)*(1 + uniquenessRatio/100.) + // for any d != d*+/-1 within the search range. + int speckleWindowSize; // disparity variation window + int speckleRange; // acceptable range of variation in window + + int trySmallerWindows; // if 1, the results may be more accurate, + // at the expense of slower processing + CvRect roi1, roi2; + int disp12MaxDiff; + + // temporary buffers + CvMat* preFilteredImg0; + CvMat* preFilteredImg1; + CvMat* slidingSumBuf; + CvMat* cost; + CvMat* disp; +} CvStereoBMState; + +#define CV_STEREO_BM_BASIC 0 +#define CV_STEREO_BM_FISH_EYE 1 +#define CV_STEREO_BM_NARROW 2 + +CVAPI(CvStereoBMState*) cvCreateStereoBMState(int preset CV_DEFAULT(CV_STEREO_BM_BASIC), + int numberOfDisparities CV_DEFAULT(0)); + +CVAPI(void) cvReleaseStereoBMState( CvStereoBMState** state ); + +CVAPI(void) cvFindStereoCorrespondenceBM( const CvArr* left, const CvArr* right, + CvArr* disparity, CvStereoBMState* state ); + +CVAPI(CvRect) cvGetValidDisparityROI( CvRect roi1, CvRect roi2, int minDisparity, + int numberOfDisparities, int SADWindowSize ); + +CVAPI(void) cvValidateDisparity( CvArr* disparity, const CvArr* cost, + int minDisparity, int numberOfDisparities, + int disp12MaxDiff CV_DEFAULT(1) ); + +/* Reprojects the computed disparity image to the 3D space using the specified 4x4 matrix */ +CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage, + CvArr* _3dImage, const CvMat* Q, + int handleMissingValues CV_DEFAULT(0) ); + +/** @} calib3d_c */ + +#ifdef __cplusplus +} // extern "C" + +////////////////////////////////////////////////////////////////////////////////////////// +class CV_EXPORTS CvLevMarq +{ +public: + CvLevMarq(); + CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria= + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), + bool completeSymmFlag=false ); + ~CvLevMarq(); + void init( int nparams, int nerrs, CvTermCriteria criteria= + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), + bool completeSymmFlag=false ); + bool update( const CvMat*& param, CvMat*& J, CvMat*& err ); + bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm ); + + void clear(); + void step(); + enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 }; + + cv::Ptr mask; + cv::Ptr prevParam; + cv::Ptr param; + cv::Ptr J; + cv::Ptr err; + cv::Ptr JtJ; + cv::Ptr JtJN; + cv::Ptr JtErr; + cv::Ptr JtJV; + cv::Ptr JtJW; + double prevErrNorm, errNorm; + int lambdaLg10; + CvTermCriteria criteria; + int state; + int iters; + bool completeSymmFlag; + int solveMethod; +}; + +#endif + +#endif /* OPENCV_CALIB3D_C_H */ diff --git a/thirdparty/linux/include/opencv2/ccalib.hpp b/thirdparty/linux/include/opencv2/ccalib.hpp new file mode 100644 index 0000000..79df598 --- /dev/null +++ b/thirdparty/linux/include/opencv2/ccalib.hpp @@ -0,0 +1,157 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// + // + // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + // + // By downloading, copying, installing or using the software you agree to this license. + // If you do not agree to this license, do not download, install, + // copy or use the software. + // + // + // License Agreement + // For Open Source Computer Vision Library + // + // Copyright (C) 2014, OpenCV Foundation, all rights reserved. + // Third party copyrights are property of their respective owners. + // + // Redistribution and use in source and binary forms, with or without modification, + // are permitted provided that the following conditions are met: + // + // * Redistribution's of source code must retain the above copyright notice, + // this list of conditions and the following disclaimer. + // + // * Redistribution's in binary form must reproduce the above copyright notice, + // this list of conditions and the following disclaimer in the documentation + // and/or other materials provided with the distribution. + // + // * The name of the copyright holders may not be used to endorse or promote products + // derived from this software without specific prior written permission. + // + // This software is provided by the copyright holders and contributors "as is" and + // any express or implied warranties, including, but not limited to, the implied + // warranties of merchantability and fitness for a particular purpose are disclaimed. + // In no event shall the Intel Corporation or contributors be liable for any direct, + // indirect, incidental, special, exemplary, or consequential damages + // (including, but not limited to, procurement of substitute goods or services; + // loss of use, data, or profits; or business interruption) however caused + // and on any theory of liability, whether in contract, strict liability, + // or tort (including negligence or otherwise) arising in any way out of + // the use of this software, even if advised of the possibility of such damage. + // + //M*/ + +#ifndef __OPENCV_CCALIB_HPP__ +#define __OPENCV_CCALIB_HPP__ + +#include +#include +#include +#include + +#include + +/** @defgroup ccalib Custom Calibration Pattern for 3D reconstruction +*/ + +namespace cv{ namespace ccalib{ + +//! @addtogroup ccalib +//! @{ + +class CV_EXPORTS CustomPattern : public Algorithm +{ +public: + CustomPattern(); + virtual ~CustomPattern(); + + bool create(InputArray pattern, const Size2f boardSize, OutputArray output = noArray()); + + bool findPattern(InputArray image, OutputArray matched_features, OutputArray pattern_points, const double ratio = 0.7, + const double proj_error = 8.0, const bool refine_position = false, OutputArray out = noArray(), + OutputArray H = noArray(), OutputArray pattern_corners = noArray()); + + bool isInitialized(); + + void getPatternPoints(OutputArray original_points); + /**< + Returns a vector of the original points. + */ + double getPixelSize(); + /**< + Get the pixel size of the pattern + */ + + bool setFeatureDetector(Ptr featureDetector); + bool setDescriptorExtractor(Ptr extractor); + bool setDescriptorMatcher(Ptr matcher); + + Ptr getFeatureDetector(); + Ptr getDescriptorExtractor(); + Ptr getDescriptorMatcher(); + + double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, + Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON)); + /**< + Calls the calirateCamera function with the same inputs. + */ + + bool findRt(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE); + bool findRt(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE); + /**< + Uses solvePnP to find the rotation and translation of the pattern + with respect to the camera frame. + */ + + bool findRtRANSAC(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, + float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE); + bool findRtRANSAC(InputArray image, InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, + float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE); + /**< + Uses solvePnPRansac() + */ + + void drawOrientation(InputOutputArray image, InputArray tvec, InputArray rvec, InputArray cameraMatrix, + InputArray distCoeffs, double axis_length = 3, int axis_width = 2); + /**< + pattern_corners -> projected over the image position of the edges of the pattern. + */ + +private: + + Mat img_roi; + std::vector obj_corners; + double pxSize; + + bool initialized; + + Ptr detector; + Ptr descriptorExtractor; + Ptr descriptorMatcher; + + std::vector keypoints; + std::vector points3d; + Mat descriptor; + + bool init(Mat& image, const float pixel_size, OutputArray output = noArray()); + bool findPatternPass(const Mat& image, std::vector& matched_features, std::vector& pattern_points, + Mat& H, std::vector& scene_corners, const double pratio, const double proj_error, + const bool refine_position = false, const Mat& mask = Mat(), OutputArray output = noArray()); + void scaleFoundPoints(const double squareSize, const std::vector& corners, std::vector& pts3d); + void check_matches(std::vector& matched, const std::vector& pattern, std::vector& good, std::vector& pattern_3d, const Mat& H); + + void keypoints2points(const std::vector& in, std::vector& out); + void updateKeypointsPos(std::vector& in, const std::vector& new_pos); + void refinePointsPos(const Mat& img, std::vector& p); + void refineKeypointsPos(const Mat& img, std::vector& kp); +}; + +//! @} + +}} // namespace ccalib, cv + +#endif diff --git a/thirdparty/linux/include/opencv2/ccalib/multicalib.hpp b/thirdparty/linux/include/opencv2/ccalib/multicalib.hpp new file mode 100644 index 0000000..686d7a5 --- /dev/null +++ b/thirdparty/linux/include/opencv2/ccalib/multicalib.hpp @@ -0,0 +1,212 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University, +// all rights reserved. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_MULTICAMERACALIBRATION_HPP__ +#define __OPENCV_MULTICAMERACALIBRATION_HPP__ + +#include "opencv2/ccalib/randpattern.hpp" +#include "opencv2/ccalib/omnidir.hpp" +#include +#include + +namespace cv { namespace multicalib { + +//! @addtogroup ccalib +//! @{ + +#define HEAD -1 +#define INVALID -2 + +/** @brief Class for multiple camera calibration that supports pinhole camera and omnidirection camera. +For omnidirectional camera model, please refer to omnidir.hpp in ccalib module. +It first calibrate each camera individually, then a bundle adjustment like optimization is applied to +refine extrinsic parameters. So far, it only support "random" pattern for calibration, +see randomPattern.hpp in ccalib module for details. +Images that are used should be named by "cameraIdx-timestamp.*", several images with the same timestamp +means that they are the same pattern that are photographed. cameraIdx should start from 0. + +For more details, please refer to paper + B. Li, L. Heng, K. Kevin and M. Pollefeys, "A Multiple-Camera System + Calibration Toolbox Using A Feature Descriptor-Based Calibration + Pattern", in IROS 2013. +*/ + +class CV_EXPORTS MultiCameraCalibration +{ +public: + enum { + PINHOLE, + OMNIDIRECTIONAL + //FISHEYE + }; + + // an edge connects a camera and pattern + struct edge + { + int cameraVertex; // vertex index for camera in this edge + int photoVertex; // vertex index for pattern in this edge + int photoIndex; // photo index among photos for this camera + Mat transform; // transform from pattern to camera + + edge(int cv, int pv, int pi, Mat trans) + { + cameraVertex = cv; + photoVertex = pv; + photoIndex = pi; + transform = trans; + } + }; + + struct vertex + { + Mat pose; // relative pose to the first camera. For camera vertex, it is the + // transform from the first camera to this camera, for pattern vertex, + // it is the transform from pattern to the first camera + int timestamp; // timestamp of photo, only available for photo vertex + + vertex(Mat po, int ts) + { + pose = po; + timestamp = ts; + } + + vertex() + { + pose = Mat::eye(4, 4, CV_32F); + timestamp = -1; + } + }; + /* @brief Constructor + @param cameraType camera type, PINHOLE or OMNIDIRECTIONAL + @param nCameras number of cameras + @fileName filename of string list that are used for calibration, the file is generated + by imagelist_creator from OpenCv samples. The first one in the list is the pattern filename. + @patternWidth the physical width of pattern, in user defined unit. + @patternHeight the physical height of pattern, in user defined unit. + @showExtration whether show extracted features and feature filtering. + @nMiniMatches minimal number of matched features for a frame. + @flags Calibration flags + @criteria optimization stopping criteria. + @detector feature detector that detect feature points in pattern and images. + @descriptor feature descriptor. + @matcher feature matcher. + */ + MultiCameraCalibration(int cameraType, int nCameras, const std::string& fileName, float patternWidth, + float patternHeight, int verbose = 0, int showExtration = 0, int nMiniMatches = 20, int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 200, 1e-7), + Ptr detector = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.006f), + Ptr descriptor = AKAZE::create(AKAZE::DESCRIPTOR_MLDB,0, 3, 0.006f), + Ptr matcher = DescriptorMatcher::create("BruteForce-L1")); + + /* @brief load images + */ + void loadImages(); + + /* @brief initialize multiple camera calibration. It calibrates each camera individually. + */ + void initialize(); + + /* @brief optimization extrinsic parameters + */ + double optimizeExtrinsics(); + + /* @brief run multi-camera camera calibration, it runs loadImage(), initialize() and optimizeExtrinsics() + */ + double run(); + + /* @brief write camera parameters to file. + */ + void writeParameters(const std::string& filename); + +private: + std::vector readStringList(); + + int getPhotoVertex(int timestamp); + + void graphTraverse(const Mat& G, int begin, std::vector& order, std::vector& pre); + + void findRowNonZero(const Mat& row, Mat& idx); + + void computeJacobianExtrinsic(const Mat& extrinsicParams, Mat& JTJ_inv, Mat& JTE); + + void computePhotoCameraJacobian(const Mat& rvecPhoto, const Mat& tvecPhoto, const Mat& rvecCamera, + const Mat& tvecCamera, Mat& rvecTran, Mat& tvecTran, const Mat& objectPoints, const Mat& imagePoints, const Mat& K, + const Mat& distort, const Mat& xi, Mat& jacobianPhoto, Mat& jacobianCamera, Mat& E); + + void compose_motion(InputArray _om1, InputArray _T1, InputArray _om2, InputArray _T2, Mat& om3, Mat& T3, Mat& dom3dom1, + Mat& dom3dT1, Mat& dom3dom2, Mat& dom3dT2, Mat& dT3dom1, Mat& dT3dT1, Mat& dT3dom2, Mat& dT3dT2); + + void JRodriguesMatlab(const Mat& src, Mat& dst); + void dAB(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB); + + double computeProjectError(Mat& parameters); + + void vector2parameters(const Mat& parameters, std::vector& rvecVertex, std::vector& tvecVertexs); + void parameters2vector(const std::vector& rvecVertex, const std::vector& tvecVertex, Mat& parameters); + + int _camType; //PINHOLE, FISHEYE or OMNIDIRECTIONAL + int _nCamera; + int _nMiniMatches; + int _flags; + int _verbose; + double _error; + float _patternWidth, _patternHeight; + TermCriteria _criteria; + std::string _filename; + int _showExtraction; + Ptr _detector; + Ptr _descriptor; + Ptr _matcher; + + std::vector _edgeList; + std::vector _vertexList; + std::vector > _objectPointsForEachCamera; + std::vector > _imagePointsForEachCamera; + std::vector _cameraMatrix; + std::vector _distortCoeffs; + std::vector _xi; + std::vector > _omEachCamera, _tEachCamera; +}; + +//! @} + +}} // namespace multicalib, cv +#endif \ No newline at end of file diff --git a/thirdparty/linux/include/opencv2/ccalib/omnidir.hpp b/thirdparty/linux/include/opencv2/ccalib/omnidir.hpp new file mode 100644 index 0000000..9663c18 --- /dev/null +++ b/thirdparty/linux/include/opencv2/ccalib/omnidir.hpp @@ -0,0 +1,312 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University, +// all rights reserved. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#include +#include + +#ifndef __OPENCV_OMNIDIR_HPP__ +#define __OPENCV_OMNIDIR_HPP__ + +namespace cv +{ +namespace omnidir +{ + //! @addtogroup ccalib + //! @{ + + enum { + CALIB_USE_GUESS = 1, + CALIB_FIX_SKEW = 2, + CALIB_FIX_K1 = 4, + CALIB_FIX_K2 = 8, + CALIB_FIX_P1 = 16, + CALIB_FIX_P2 = 32, + CALIB_FIX_XI = 64, + CALIB_FIX_GAMMA = 128, + CALIB_FIX_CENTER = 256 + }; + + enum{ + RECTIFY_PERSPECTIVE = 1, + RECTIFY_CYLINDRICAL = 2, + RECTIFY_LONGLATI = 3, + RECTIFY_STEREOGRAPHIC = 4 + }; + + enum{ + XYZRGB = 1, + XYZ = 2 + }; +/** + * This module was accepted as a GSoC 2015 project for OpenCV, authored by + * Baisheng Lai, mentored by Bo Li. + */ + + /** @brief Projects points for omnidirectional camera using CMei's model + + @param objectPoints Object points in world coordinate, vector of vector of Vec3f or Mat of + 1xN/Nx1 3-channel of type CV_32F and N is the number of points. 64F is also acceptable. + @param imagePoints Output array of image points, vector of vector of Vec2f or + 1xN/Nx1 2-channel of type CV_32F. 64F is also acceptable. + @param rvec vector of rotation between world coordinate and camera coordinate, i.e., om + @param tvec vector of translation between pattern coordinate and camera coordinate + @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$. + @param xi The parameter xi for CMei's model + @param jacobian Optional output 2Nx16 of type CV_64F jacobian matrix, contains the derivatives of + image pixel points wrt parameters including \f$om, T, f_x, f_y, s, c_x, c_y, xi, k_1, k_2, p_1, p_2\f$. + This matrix will be used in calibration by optimization. + + The function projects object 3D points of world coordinate to image pixels, parameter by intrinsic + and extrinsic parameters. Also, it optionally compute a by-product: the jacobian matrix containing + contains the derivatives of image pixel points wrt intrinsic and extrinsic parameters. + */ + CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, + InputArray K, double xi, InputArray D, OutputArray jacobian = noArray()); + + /** @brief Undistort 2D image points for omnidirectional camera using CMei's model + + @param distorted Array of distorted image points, vector of Vec2f + or 1xN/Nx1 2-channel Mat of type CV_32F, 64F depth is also acceptable + @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$. + @param xi The parameter xi for CMei's model + @param R Rotation trainsform between the original and object space : 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param undistorted array of normalized object points, vector of Vec2f/Vec2d or 1xN/Nx1 2-channel Mat with the same + depth of distorted points. + */ + CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray xi, InputArray R); + + /** @brief Computes undistortion and rectification maps for omnidirectional camera image transform by a rotation R. + It output two maps that are used for cv::remap(). If D is empty then zero distortion is used, + if R or P is empty then identity matrices are used. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$, with depth CV_32F or CV_64F + @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$, with depth CV_32F or CV_64F + @param xi The parameter xi for CMei's model + @param R Rotation transform between the original and object space : 3x3 1-channel, or vector: 3x1/1x3, with depth CV_32F or CV_64F + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param size Undistorted image size. + @param mltype Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() + for details. + @param map1 The first output map. + @param map2 The second output map. + @param flags Flags indicates the rectification type, RECTIFY_PERSPECTIVE, RECTIFY_CYLINDRICAL, RECTIFY_LONGLATI and RECTIFY_STEREOGRAPHIC + are supported. + */ + CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray xi, InputArray R, InputArray P, const cv::Size& size, + int mltype, OutputArray map1, OutputArray map2, int flags); + + /** @brief Undistort omnidirectional images to perspective images + + @param distorted The input omnidirectional image. + @param undistorted The output undistorted image. + @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$. + @param xi The parameter xi for CMei's model. + @param flags Flags indicates the rectification type, RECTIFY_PERSPECTIVE, RECTIFY_CYLINDRICAL, RECTIFY_LONGLATI and RECTIFY_STEREOGRAPHIC + @param Knew Camera matrix of the distorted image. If it is not assigned, it is just K. + @param new_size The new image size. By default, it is the size of distorted. + @param R Rotation matrix between the input and output images. By default, it is identity matrix. + */ + CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray xi, int flags, + InputArray Knew = cv::noArray(), const Size& new_size = Size(), InputArray R = Mat::eye(3, 3, CV_64F)); + + /** @brief Perform omnidirectional camera calibration, the default depth of outputs is CV_64F. + + @param objectPoints Vector of vector of Vec3f object points in world (pattern) coordinate. + It also can be vector of Mat with size 1xN/Nx1 and type CV_32FC3. Data with depth of 64_F is also acceptable. + @param imagePoints Vector of vector of Vec2f corresponding image points of objectPoints. It must be the same + size and the same type with objectPoints. + @param size Image size of calibration images. + @param K Output calibrated camera matrix. + @param xi Output parameter xi for CMei's model + @param D Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ + @param rvecs Output rotations for each calibration images + @param tvecs Output translation for each calibration images + @param flags The flags that control calibrate + @param criteria Termination criteria for optimization + @param idx Indices of images that pass initialization, which are really used in calibration. So the size of rvecs is the + same as idx.total(). + */ + CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size size, + InputOutputArray K, InputOutputArray xi, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + int flags, TermCriteria criteria, OutputArray idx=noArray()); + + /** @brief Stereo calibration for omnidirectional camera model. It computes the intrinsic parameters for two + cameras and the extrinsic parameters between two cameras. The default depth of outputs is CV_64F. + + @param objectPoints Object points in world (pattern) coordinate. Its type is vector >. + It also can be vector of Mat with size 1xN/Nx1 and type CV_32FC3. Data with depth of 64_F is also acceptable. + @param imagePoints1 The corresponding image points of the first camera, with type vector >. + It must be the same size and the same type as objectPoints. + @param imagePoints2 The corresponding image points of the second camera, with type vector >. + It must be the same size and the same type as objectPoints. + @param imageSize1 Image size of calibration images of the first camera. + @param imageSize2 Image size of calibration images of the second camera. + @param K1 Output camera matrix for the first camera. + @param xi1 Output parameter xi of Mei's model for the first camera + @param D1 Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the first camera + @param K2 Output camera matrix for the first camera. + @param xi2 Output parameter xi of CMei's model for the second camera + @param D2 Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the second camera + @param rvec Output rotation between the first and second camera + @param tvec Output translation between the first and second camera + @param rvecsL Output rotation for each image of the first camera + @param tvecsL Output translation for each image of the first camera + @param flags The flags that control stereoCalibrate + @param criteria Termination criteria for optimization + @param idx Indices of image pairs that pass initialization, which are really used in calibration. So the size of rvecs is the + same as idx.total(). + @ + */ + CV_EXPORTS_W double stereoCalibrate(InputOutputArrayOfArrays objectPoints, InputOutputArrayOfArrays imagePoints1, InputOutputArrayOfArrays imagePoints2, + const Size& imageSize1, const Size& imageSize2, InputOutputArray K1, InputOutputArray xi1, InputOutputArray D1, InputOutputArray K2, InputOutputArray xi2, + InputOutputArray D2, OutputArray rvec, OutputArray tvec, OutputArrayOfArrays rvecsL, OutputArrayOfArrays tvecsL, int flags, TermCriteria criteria, OutputArray idx=noArray()); + + /** @brief Stereo rectification for omnidirectional camera model. It computes the rectification rotations for two cameras + + @param R Rotation between the first and second camera + @param T Translation between the first and second camera + @param R1 Output 3x3 rotation matrix for the first camera + @param R2 Output 3x3 rotation matrix for the second camera + */ + CV_EXPORTS_W void stereoRectify(InputArray R, InputArray T, OutputArray R1, OutputArray R2); + + /** @brief Stereo 3D reconstruction from a pair of images + + @param image1 The first input image + @param image2 The second input image + @param K1 Input camera matrix of the first camera + @param D1 Input distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the first camera + @param xi1 Input parameter xi for the first camera for CMei's model + @param K2 Input camera matrix of the second camera + @param D2 Input distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the second camera + @param xi2 Input parameter xi for the second camera for CMei's model + @param R Rotation between the first and second camera + @param T Translation between the first and second camera + @param flag Flag of rectification type, RECTIFY_PERSPECTIVE or RECTIFY_LONGLATI + @param numDisparities The parameter 'numDisparities' in StereoSGBM, see StereoSGBM for details. + @param SADWindowSize The parameter 'SADWindowSize' in StereoSGBM, see StereoSGBM for details. + @param disparity Disparity map generated by stereo matching + @param image1Rec Rectified image of the first image + @param image2Rec rectified image of the second image + @param newSize Image size of rectified image, see omnidir::undistortImage + @param Knew New camera matrix of rectified image, see omnidir::undistortImage + @param pointCloud Point cloud of 3D reconstruction, with type CV_64FC3 + @param pointType Point cloud type, it can be XYZRGB or XYZ + */ + CV_EXPORTS_W void stereoReconstruct(InputArray image1, InputArray image2, InputArray K1, InputArray D1, InputArray xi1, + InputArray K2, InputArray D2, InputArray xi2, InputArray R, InputArray T, int flag, int numDisparities, int SADWindowSize, + OutputArray disparity, OutputArray image1Rec, OutputArray image2Rec, const Size& newSize = Size(), InputArray Knew = cv::noArray(), + OutputArray pointCloud = cv::noArray(), int pointType = XYZRGB); + +namespace internal +{ + void initializeCalibration(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size size, OutputArrayOfArrays omAll, + OutputArrayOfArrays tAll, OutputArray K, double& xi, OutputArray idx = noArray()); + + void initializeStereoCalibration(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + const Size& size1, const Size& size2, OutputArray om, OutputArray T, OutputArrayOfArrays omL, OutputArrayOfArrays tL, OutputArray K1, OutputArray D1, OutputArray K2, OutputArray D2, + double &xi1, double &xi2, int flags, OutputArray idx); + + void computeJacobian(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray parameters, Mat& JTJ_inv, Mat& JTE, int flags, + double epsilon); + + void computeJacobianStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputArray parameters, Mat& JTJ_inv, Mat& JTE, int flags, double epsilon); + + void encodeParameters(InputArray K, InputArrayOfArrays omAll, InputArrayOfArrays tAll, InputArray distoaration, double xi, OutputArray parameters); + + void encodeParametersStereo(InputArray K1, InputArray K2, InputArray om, InputArray T, InputArrayOfArrays omL, InputArrayOfArrays tL, + InputArray D1, InputArray D2, double xi1, double xi2, OutputArray parameters); + + void decodeParameters(InputArray paramsters, OutputArray K, OutputArrayOfArrays omAll, OutputArrayOfArrays tAll, OutputArray distoration, double& xi); + + void decodeParametersStereo(InputArray parameters, OutputArray K1, OutputArray K2, OutputArray om, OutputArray T, OutputArrayOfArrays omL, + OutputArrayOfArrays tL, OutputArray D1, OutputArray D2, double& xi1, double& xi2); + + void estimateUncertainties(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray parameters, Mat& errors, Vec2d& std_error, double& rms, int flags); + + void estimateUncertaintiesStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputArray parameters, Mat& errors, + Vec2d& std_error, double& rms, int flags); + + double computeMeanReproErr(InputArrayOfArrays imagePoints, InputArrayOfArrays proImagePoints); + + double computeMeanReproErr(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray K, InputArray D, double xi, InputArrayOfArrays omAll, + InputArrayOfArrays tAll); + + double computeMeanReproErrStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputArray K1, InputArray K2, + InputArray D1, InputArray D2, double xi1, double xi2, InputArray om, InputArray T, InputArrayOfArrays omL, InputArrayOfArrays TL); + + void checkFixed(Mat &G, int flags, int n); + + void subMatrix(const Mat& src, Mat& dst, const std::vector& cols, const std::vector& rows); + + void flags2idx(int flags, std::vector& idx, int n); + + void flags2idxStereo(int flags, std::vector& idx, int n); + + void fillFixed(Mat&G, int flags, int n); + + void fillFixedStereo(Mat& G, int flags, int n); + + double findMedian(const Mat& row); + + Vec3d findMedian3(InputArray mat); + + void getInterset(InputArray idx1, InputArray idx2, OutputArray inter1, OutputArray inter2, OutputArray inter_ori); + + void compose_motion(InputArray _om1, InputArray _T1, InputArray _om2, InputArray _T2, Mat& om3, Mat& T3, Mat& dom3dom1, + Mat& dom3dT1, Mat& dom3dom2, Mat& dom3dT2, Mat& dT3dom1, Mat& dT3dT1, Mat& dT3dom2, Mat& dT3dT2); + + //void JRodriguesMatlab(const Mat& src, Mat& dst); + + //void dAB(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB); +} // internal + +//! @} + +} // omnidir + +} //cv +#endif \ No newline at end of file diff --git a/thirdparty/linux/include/opencv2/ccalib/randpattern.hpp b/thirdparty/linux/include/opencv2/ccalib/randpattern.hpp new file mode 100644 index 0000000..9fc08f8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/ccalib/randpattern.hpp @@ -0,0 +1,177 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University, +// all rights reserved. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_RANDOMPATTERN_HPP__ +#define __OPENCV_RANDOMPATTERN_HPP__ + +#include "opencv2/features2d.hpp" +#include "opencv2/highgui.hpp" + +namespace cv { namespace randpattern { + + +//! @addtogroup ccalib +//! @{ + +/** @brief Class for finding features points and corresponding 3D in world coordinate of +a "random" pattern, which can be to be used in calibration. It is useful when pattern is +partly occluded or only a part of pattern can be observed in multiple cameras calibration. +The pattern can be generated by RandomPatternGenerator class described in this file. + +Please refer to paper + B. Li, L. Heng, K. Kevin and M. Pollefeys, "A Multiple-Camera System + Calibration Toolbox Using A Feature Descriptor-Based Calibration + Pattern", in IROS 2013. +*/ + +class CV_EXPORTS RandomPatternCornerFinder +{ +public: + + /* @brief Construct RandomPatternCornerFinder object + + @param patternWidth the real width of "random" pattern in a user defined unit. + @param patternHeight the real height of "random" pattern in a user defined unit. + @param nMiniMatch number of minimal matches, otherwise that image is abandoned + @depth depth of output objectPoints and imagePoints, set it to be CV_32F or CV_64F. + @showExtraction whether show feature extraction, 0 for no and 1 for yes. + @detector feature detector to detect feature points in pattern and images. + @descriptor feature descriptor. + @matcher feature matcher. + */ + RandomPatternCornerFinder(float patternWidth, float patternHeight, + int nminiMatch = 20, int depth = CV_32F, int verbose = 0, int showExtraction = 0, + Ptr detector = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.005f), + Ptr descriptor = AKAZE::create(AKAZE::DESCRIPTOR_MLDB,0, 3, 0.005f), + Ptr matcher = DescriptorMatcher::create("BruteForce-L1")); + + /* @brief Load pattern image and compute features for pattern + @param patternImage image for "random" pattern generated by RandomPatternGenerator, run it first. + */ + void loadPattern(cv::Mat patternImage); + + /* @brief Compute matched object points and image points which are used for calibration + The objectPoints (3D) and imagePoints (2D) are stored inside the class. Run getObjectPoints() + and getImagePoints() to get them. + + @param inputImages vector of 8-bit grayscale images containing "random" pattern + that are used for calibration. + */ + void computeObjectImagePoints(std::vector inputImages); + + //void computeObjectImagePoints2(std::vector inputImages); + + /* @brief Compute object and image points for a single image. It returns a vector that + the first element stores the imagePoints and the second one stores the objectPoints. + + @param inputImage single input image for calibration + */ + std::vector computeObjectImagePointsForSingle(cv::Mat inputImage); + + /* @brief Get object(3D) points + */ + std::vector getObjectPoints(); + + /* @brief and image(2D) points + */ + std::vector getImagePoints(); + +private: + + std::vector _objectPonits, _imagePoints; + float _patternWidth, _patternHeight; + cv::Size _patternImageSize; + int _nminiMatch; + int _depth; + int _verbose; + + Ptr _detector; + Ptr _descriptor; + Ptr _matcher; + Mat _descriptorPattern; + std::vector _keypointsPattern; + Mat _patternImage; + int _showExtraction; + + void keyPoints2MatchedLocation(const std::vector& imageKeypoints, + const std::vector& patternKeypoints, const std::vector matchces, + cv::Mat& matchedImagelocation, cv::Mat& matchedPatternLocation); + void getFilteredLocation(cv::Mat& imageKeypoints, cv::Mat& patternKeypoints, const cv::Mat mask); + void getObjectImagePoints(const cv::Mat& imageKeypoints, const cv::Mat& patternKeypoints); + void crossCheckMatching( cv::Ptr& descriptorMatcher, + const Mat& descriptors1, const Mat& descriptors2, + std::vector& filteredMatches12, int knn=1 ); + void drawCorrespondence(const Mat& image1, const std::vector keypoint1, + const Mat& image2, const std::vector keypoint2, const std::vector matchces, + const Mat& mask1, const Mat& mask2, const int step); +}; + +/* @brief Class to generate "random" pattern image that are used for RandomPatternCornerFinder +Please refer to paper +B. Li, L. Heng, K. Kevin and M. Pollefeys, "A Multiple-Camera System +Calibration Toolbox Using A Feature Descriptor-Based Calibration +Pattern", in IROS 2013. +*/ +class CV_EXPORTS RandomPatternGenerator +{ +public: + /* @brief Construct RandomPatternGenerator + + @param imageWidth image width of the generated pattern image + @param imageHeight image height of the generated pattern image + */ + RandomPatternGenerator(int imageWidth, int imageHeight); + + /* @brief Generate pattern + */ + void generatePattern(); + /* @brief Get pattern + */ + cv::Mat getPattern(); +private: + cv::Mat _pattern; + int _imageWidth, _imageHeight; +}; + +//! @} + +}} //namespace randpattern, cv +#endif \ No newline at end of file diff --git a/thirdparty/linux/include/opencv2/core.hpp b/thirdparty/linux/include/opencv2/core.hpp new file mode 100644 index 0000000..6b89c42 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core.hpp @@ -0,0 +1,3220 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Copyright (C) 2015, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_HPP +#define OPENCV_CORE_HPP + +#ifndef __cplusplus +# error core.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/version.hpp" +#include "opencv2/core/base.hpp" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/traits.hpp" +#include "opencv2/core/matx.hpp" +#include "opencv2/core/types.hpp" +#include "opencv2/core/mat.hpp" +#include "opencv2/core/persistence.hpp" + +/** +@defgroup core Core functionality +@{ + @defgroup core_basic Basic structures + @defgroup core_c C structures and operations + @{ + @defgroup core_c_glue Connections with C++ + @} + @defgroup core_array Operations on arrays + @defgroup core_xml XML/YAML Persistence + @defgroup core_cluster Clustering + @defgroup core_utils Utility and system functions and macros + @{ + @defgroup core_utils_sse SSE utilities + @defgroup core_utils_neon NEON utilities + @} + @defgroup core_opengl OpenGL interoperability + @defgroup core_ipp Intel IPP Asynchronous C/C++ Converters + @defgroup core_optim Optimization Algorithms + @defgroup core_directx DirectX interoperability + @defgroup core_eigen Eigen support + @defgroup core_opencl OpenCL support + @defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability + @defgroup core_hal Hardware Acceleration Layer + @{ + @defgroup core_hal_functions Functions + @defgroup core_hal_interface Interface + @defgroup core_hal_intrin Universal intrinsics + @{ + @defgroup core_hal_intrin_impl Private implementation helpers + @} + @} +@} + */ + +namespace cv { + +//! @addtogroup core_utils +//! @{ + +/*! @brief Class passed to an error. + +This class encapsulates all or almost all necessary +information about the error happened in the program. The exception is +usually constructed and thrown implicitly via CV_Error and CV_Error_ macros. +@see error + */ +class CV_EXPORTS Exception : public std::exception +{ +public: + /*! + Default constructor + */ + Exception(); + /*! + Full constructor. Normally the constuctor is not called explicitly. + Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used. + */ + Exception(int _code, const String& _err, const String& _func, const String& _file, int _line); + virtual ~Exception() throw(); + + /*! + \return the error description and the context as a text string. + */ + virtual const char *what() const throw(); + void formatMessage(); + + String msg; ///< the formatted error message + + int code; ///< error code @see CVStatus + String err; ///< error description + String func; ///< function name. Available only when the compiler supports getting it + String file; ///< source file name where the error has occured + int line; ///< line number in the source file where the error has occured +}; + +/*! @brief Signals an error and raises the exception. + +By default the function prints information about the error to stderr, +then it either stops if cv::setBreakOnError() had been called before or raises the exception. +It is possible to alternate error processing by using cv::redirectError(). +@param exc the exception raisen. +@deprecated drop this version + */ +CV_EXPORTS void error( const Exception& exc ); + +enum SortFlags { SORT_EVERY_ROW = 0, //!< each matrix row is sorted independently + SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted + //!< independently; this flag and the previous one are + //!< mutually exclusive. + SORT_ASCENDING = 0, //!< each matrix row is sorted in the ascending + //!< order. + SORT_DESCENDING = 16 //!< each matrix row is sorted in the + //!< descending order; this flag and the previous one are also + //!< mutually exclusive. + }; + +//! @} core_utils + +//! @addtogroup core +//! @{ + +//! Covariation flags +enum CovarFlags { + /** The output covariance matrix is calculated as: + \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\f] + The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used + for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for + face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true + covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of + the "scrambled" covariance matrix. */ + COVAR_SCRAMBLED = 0, + /**The output covariance matrix is calculated as: + \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\f] + covar will be a square matrix of the same size as the total number of elements in each input + vector. One and only one of COVAR_SCRAMBLED and COVAR_NORMAL must be specified.*/ + COVAR_NORMAL = 1, + /** If the flag is specified, the function does not calculate mean from + the input vectors but, instead, uses the passed mean vector. This is useful if mean has been + pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In + this case, mean is not a mean vector of the input sub-set of vectors but rather the mean + vector of the whole set.*/ + COVAR_USE_AVG = 2, + /** If the flag is specified, the covariance matrix is scaled. In the + "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the + total number of elements in each input vector. By default (if the flag is not specified), the + covariance matrix is not scaled ( scale=1 ).*/ + COVAR_SCALE = 4, + /** If the flag is + specified, all the input vectors are stored as rows of the samples matrix. mean should be a + single-row vector in this case.*/ + COVAR_ROWS = 8, + /** If the flag is + specified, all the input vectors are stored as columns of the samples matrix. mean should be a + single-column vector in this case.*/ + COVAR_COLS = 16 +}; + +//! k-Means flags +enum KmeansFlags { + /** Select random initial centers in each attempt.*/ + KMEANS_RANDOM_CENTERS = 0, + /** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/ + KMEANS_PP_CENTERS = 2, + /** During the first (and possibly the only) attempt, use the + user-supplied labels instead of computing them from the initial centers. For the second and + further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag + to specify the exact method.*/ + KMEANS_USE_INITIAL_LABELS = 1 +}; + +//! type of line +enum LineTypes { + FILLED = -1, + LINE_4 = 4, //!< 4-connected line + LINE_8 = 8, //!< 8-connected line + LINE_AA = 16 //!< antialiased line +}; + +//! Only a subset of Hershey fonts +//! are supported +enum HersheyFonts { + FONT_HERSHEY_SIMPLEX = 0, //!< normal size sans-serif font + FONT_HERSHEY_PLAIN = 1, //!< small size sans-serif font + FONT_HERSHEY_DUPLEX = 2, //!< normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) + FONT_HERSHEY_COMPLEX = 3, //!< normal size serif font + FONT_HERSHEY_TRIPLEX = 4, //!< normal size serif font (more complex than FONT_HERSHEY_COMPLEX) + FONT_HERSHEY_COMPLEX_SMALL = 5, //!< smaller version of FONT_HERSHEY_COMPLEX + FONT_HERSHEY_SCRIPT_SIMPLEX = 6, //!< hand-writing style font + FONT_HERSHEY_SCRIPT_COMPLEX = 7, //!< more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX + FONT_ITALIC = 16 //!< flag for italic font +}; + +enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix. + REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix. + REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix. + REDUCE_MIN = 3 //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix. + }; + + +/** @brief Swaps two matrices +*/ +CV_EXPORTS void swap(Mat& a, Mat& b); +/** @overload */ +CV_EXPORTS void swap( UMat& a, UMat& b ); + +//! @} core + +//! @addtogroup core_array +//! @{ + +/** @brief Computes the source location of an extrapolated pixel. + +The function computes and returns the coordinate of a donor pixel corresponding to the specified +extrapolated pixel when using the specified extrapolation border mode. For example, if you use +cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and +want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it +looks like: +@code{.cpp} + float val = img.at(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), + borderInterpolate(-5, img.cols, cv::BORDER_WRAP)); +@endcode +Normally, the function is not called directly. It is used inside filtering functions and also in +copyMakeBorder. +@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len +@param len Length of the array along the corresponding axis. +@param borderType Border type, one of the cv::BorderTypes, except for cv::BORDER_TRANSPARENT and +cv::BORDER_ISOLATED . When borderType==cv::BORDER_CONSTANT , the function always returns -1, regardless +of p and len. + +@sa copyMakeBorder +*/ +CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType); + +/** @brief Forms a border around an image. + +The function copies the source image into the middle of the destination image. The areas to the +left, to the right, above and below the copied source image will be filled with extrapolated +pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but +what other more complex functions, including your own, may do to simplify image boundary handling. + +The function supports the mode when src is already in the middle of dst . In this case, the +function does not copy src itself but simply constructs the border, for example: + +@code{.cpp} + // let border be the same in all directions + int border=2; + // constructs a larger image to fit both the image and the border + Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); + // select the middle part of it w/o copying data + Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); + // convert image from RGB to grayscale + cvtColor(rgb, gray, COLOR_RGB2GRAY); + // form a border in-place + copyMakeBorder(gray, gray_buf, border, border, + border, border, BORDER_REPLICATE); + // now do some custom filtering ... + ... +@endcode +@note When the source image is a part (ROI) of a bigger image, the function will try to use the +pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as +if src was not a ROI, use borderType | BORDER_ISOLATED. + +@param src Source image. +@param dst Destination image of the same type as src and the size Size(src.cols+left+right, +src.rows+top+bottom) . +@param top +@param bottom +@param left +@param right Parameter specifying how many pixels in each direction from the source image rectangle +to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs +to be built. +@param borderType Border type. See borderInterpolate for details. +@param value Border value if borderType==BORDER_CONSTANT . + +@sa borderInterpolate +*/ +CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst, + int top, int bottom, int left, int right, + int borderType, const Scalar& value = Scalar() ); + +/** @brief Calculates the per-element sum of two arrays or an array and a scalar. + +The function add calculates: +- Sum of two arrays when both input arrays have the same size and the same number of channels: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] +- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of +elements as `src1.channels()`: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] +- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of +elements as `src2.channels()`: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] +where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. + +The first function in the list above can be replaced with matrix expressions: +@code{.cpp} + dst = src1 + src2; + dst += src1; // equivalent to add(dst, src1, dst); +@endcode +The input arrays and the output array can all have the same or different depths. For example, you +can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit +floating-point array. Depth of the output array is determined by the dtype parameter. In the second +and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can +be set to the default -1. In this case, the output array will have the same depth as the input +array, be it src1, src2 or both. +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and number of channels as the input array(s); the +depth is defined by dtype or src1/src2. +@param mask optional operation mask - 8-bit single channel array, that specifies elements of the +output array to be changed. +@param dtype optional depth of the output array (see the discussion below). +@sa subtract, addWeighted, scaleAdd, Mat::convertTo +*/ +CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst, + InputArray mask = noArray(), int dtype = -1); + +/** @brief Calculates the per-element difference between two arrays or array and a scalar. + +The function subtract calculates: +- Difference between two arrays, when both input arrays have the same size and the same number of +channels: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] +- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same +number of elements as `src1.channels()`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] +- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same +number of elements as `src2.channels()`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] +- The reverse difference between a scalar and an array in the case of `SubRS`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f] +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. + +The first function in the list above can be replaced with matrix expressions: +@code{.cpp} + dst = src1 - src2; + dst -= src1; // equivalent to subtract(dst, src1, dst); +@endcode +The input arrays and the output array can all have the same or different depths. For example, you +can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of +the output array is determined by dtype parameter. In the second and third cases above, as well as +in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this +case the output array will have the same depth as the input array, be it src1, src2 or both. +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array of the same size and the same number of channels as the input array. +@param mask optional operation mask; this is an 8-bit single channel array that specifies elements +of the output array to be changed. +@param dtype optional depth of the output array +@sa add, addWeighted, scaleAdd, Mat::convertTo + */ +CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst, + InputArray mask = noArray(), int dtype = -1); + + +/** @brief Calculates the per-element scaled product of two arrays. + +The function multiply calculates the per-element product of two arrays: + +\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\f] + +There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul . + +For a not-per-element matrix product, see gemm . + +@note Saturation is not applied when the output array has the depth +CV_32S. You may even get result of an incorrect sign in the case of +overflow. +@param src1 first input array. +@param src2 second input array of the same size and the same type as src1. +@param dst output array of the same size and type as src1. +@param scale optional scale factor. +@param dtype optional depth of the output array +@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, +Mat::convertTo +*/ +CV_EXPORTS_W void multiply(InputArray src1, InputArray src2, + OutputArray dst, double scale = 1, int dtype = -1); + +/** @brief Performs per-element division of two arrays or a scalar by an array. + +The function cv::divide divides one array by another: +\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f] +or a scalar by an array when there is no src1 : +\f[\texttt{dst(I) = saturate(scale/src2(I))}\f] + +When src2(I) is zero, dst(I) will also be zero. Different channels of +multi-channel arrays are processed independently. + +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array. +@param src2 second input array of the same size and type as src1. +@param scale scalar factor. +@param dst output array of the same size and type as src2. +@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in +case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). +@sa multiply, add, subtract +*/ +CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst, + double scale = 1, int dtype = -1); + +/** @overload */ +CV_EXPORTS_W void divide(double scale, InputArray src2, + OutputArray dst, int dtype = -1); + +/** @brief Calculates the sum of a scaled array and another array. + +The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY +or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates +the sum of a scaled array and another array: +\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\f] +The function can also be emulated with a matrix expression, for example: +@code{.cpp} + Mat A(3, 3, CV_64F); + ... + A.row(0) = A.row(1)*2 + A.row(2); +@endcode +@param src1 first input array. +@param alpha scale factor for the first array. +@param src2 second input array of the same size and type as src1. +@param dst output array of the same size and type as src1. +@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo +*/ +CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst); + +/** @brief Calculates the weighted sum of two arrays. + +The function addWeighted calculates the weighted sum of two arrays as follows: +\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f] +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. +The function can be replaced with a matrix expression: +@code{.cpp} + dst = src1*alpha + src2*beta + gamma; +@endcode +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array. +@param alpha weight of the first array elements. +@param src2 second input array of the same size and channel number as src1. +@param beta weight of the second array elements. +@param gamma scalar added to each sum. +@param dst output array that has the same size and number of channels as the input arrays. +@param dtype optional depth of the output array; when both input arrays have the same depth, dtype +can be set to -1, which will be equivalent to src1.depth(). +@sa add, subtract, scaleAdd, Mat::convertTo +*/ +CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2, + double beta, double gamma, OutputArray dst, int dtype = -1); + +/** @brief Scales, calculates absolute values, and converts the result to 8-bit. + +On each element of the input array, the function convertScaleAbs +performs three operations sequentially: scaling, taking an absolute +value, conversion to an unsigned 8-bit type: +\f[\texttt{dst} (I)= \texttt{saturate\_cast} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f] +In case of multi-channel arrays, the function processes each channel +independently. When the output is not 8-bit, the operation can be +emulated by calling the Mat::convertTo method (or by using matrix +expressions) and then by calculating an absolute value of the result. +For example: +@code{.cpp} + Mat_ A(30,30); + randu(A, Scalar(-100), Scalar(100)); + Mat_ B = A*5 + 3; + B = abs(B); + // Mat_ B = abs(A*5+3) will also do the job, + // but it will allocate a temporary matrix +@endcode +@param src input array. +@param dst output array. +@param alpha optional scale factor. +@param beta optional delta added to the scaled values. +@sa Mat::convertTo, cv::abs(const Mat&) +*/ +CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst, + double alpha = 1, double beta = 0); + +/** @brief Converts an array to half precision floating number. + +This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). The input array has to have type of CV_32F or +CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. +The format of half precision floating point is defined in IEEE 754-2008. + +@param src input array. +@param dst output array. +*/ +CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst); + +/** @brief Performs a look-up table transform of an array. + +The function LUT fills the output array with values from the look-up table. Indices of the entries +are taken from the input array. That is, the function processes each element of src as follows: +\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f] +where +\f[d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f] +@param src input array of 8-bit elements. +@param lut look-up table of 256 elements; in case of multi-channel input array, the table should +either have a single channel (in this case the same table is used for all channels) or the same +number of channels as in the input array. +@param dst output array of the same size and number of channels as src, and the same depth as lut. +@sa convertScaleAbs, Mat::convertTo +*/ +CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst); + +/** @brief Calculates the sum of array elements. + +The function cv::sum calculates and returns the sum of array elements, +independently for each channel. +@param src input array that must have from 1 to 4 channels. +@sa countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce +*/ +CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src); + +/** @brief Counts non-zero array elements. + +The function returns the number of non-zero elements in src : +\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f] +@param src single-channel array. +@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix +*/ +CV_EXPORTS_W int countNonZero( InputArray src ); + +/** @brief Returns the list of locations of non-zero pixels + +Given a binary matrix (likely returned from an operation such +as threshold(), compare(), >, ==, etc, return all of +the non-zero indices as a cv::Mat or std::vector (x,y) +For example: +@code{.cpp} + cv::Mat binaryImage; // input, binary image + cv::Mat locations; // output, locations of non-zero pixels + cv::findNonZero(binaryImage, locations); + + // access pixel coordinates + Point pnt = locations.at(i); +@endcode +or +@code{.cpp} + cv::Mat binaryImage; // input, binary image + vector locations; // output, locations of non-zero pixels + cv::findNonZero(binaryImage, locations); + + // access pixel coordinates + Point pnt = locations[i]; +@endcode +@param src single-channel array (type CV_8UC1) +@param idx the output array, type of cv::Mat or std::vector, corresponding to non-zero indices in the input +*/ +CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx ); + +/** @brief Calculates an average (mean) of array elements. + +The function cv::mean calculates the mean value M of array elements, +independently for each channel, and return it: +\f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f] +When all the mask elements are 0's, the function returns Scalar::all(0) +@param src input array that should have from 1 to 4 channels so that the result can be stored in +Scalar_ . +@param mask optional operation mask. +@sa countNonZero, meanStdDev, norm, minMaxLoc +*/ +CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray()); + +/** Calculates a mean and standard deviation of array elements. + +The function cv::meanStdDev calculates the mean and the standard deviation M +of array elements independently for each channel and returns it via the +output parameters: +\f[\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\f] +When all the mask elements are 0's, the function returns +mean=stddev=Scalar::all(0). +@note The calculated standard deviation is only the diagonal of the +complete normalized covariance matrix. If the full matrix is needed, you +can reshape the multi-channel array M x N to the single-channel array +M\*N x mtx.channels() (only possible when the matrix is continuous) and +then pass the matrix to calcCovarMatrix . +@param src input array that should have from 1 to 4 channels so that the results can be stored in +Scalar_ 's. +@param mean output parameter: calculated mean value. +@param stddev output parameter: calculateded standard deviation. +@param mask optional operation mask. +@sa countNonZero, mean, norm, minMaxLoc, calcCovarMatrix +*/ +CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev, + InputArray mask=noArray()); + +/** @brief Calculates an absolute array norm, an absolute difference norm, or a +relative difference norm. + +The function cv::norm calculates an absolute norm of src1 (when there is no +src2 ): + +\f[norm = \forkthree{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } +{ \| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } +{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }\f] + +or an absolute or relative difference norm if src2 is there: + +\f[norm = \forkthree{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } +{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } +{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }\f] + +or + +\f[norm = \forkthree{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE_INF}\) } +{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE_L1}\) } +{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE_L2}\) }\f] + +The function cv::norm returns the calculated norm. + +When the mask parameter is specified and it is not empty, the norm is +calculated only over the region specified by the mask. + +A multi-channel input arrays are treated as a single-channel, that is, +the results for all channels are combined. + +@param src1 first input array. +@param normType type of the norm (see cv::NormTypes). +@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. +*/ +CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray()); + +/** @overload +@param src1 first input array. +@param src2 second input array of the same size and the same type as src1. +@param normType type of the norm (cv::NormTypes). +@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. +*/ +CV_EXPORTS_W double norm(InputArray src1, InputArray src2, + int normType = NORM_L2, InputArray mask = noArray()); +/** @overload +@param src first input array. +@param normType type of the norm (see cv::NormTypes). +*/ +CV_EXPORTS double norm( const SparseMat& src, int normType ); + +/** @brief computes PSNR image/video quality metric + +see http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio for details +@todo document + */ +CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2); + +/** @brief naive nearest neighbor finder + +see http://en.wikipedia.org/wiki/Nearest_neighbor_search +@todo document + */ +CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2, + OutputArray dist, int dtype, OutputArray nidx, + int normType = NORM_L2, int K = 0, + InputArray mask = noArray(), int update = 0, + bool crosscheck = false); + +/** @brief Normalizes the norm or value range of an array. + +The function cv::normalize normalizes scale and shift the input array elements so that +\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f] +(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that +\f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f] + +when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be +normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this +sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or +min-max but modify the whole array, you can use norm and Mat::convertTo. + +In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, +the range transformation for sparse matrices is not allowed since it can shift the zero level. + +Possible usage with some positive example data: +@code{.cpp} + vector positiveData = { 2.0, 8.0, 10.0 }; + vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; + + // Norm to probability (total count) + // sum(numbers) = 20.0 + // 2.0 0.1 (2.0/20.0) + // 8.0 0.4 (8.0/20.0) + // 10.0 0.5 (10.0/20.0) + normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); + + // Norm to unit vector: ||positiveData|| = 1.0 + // 2.0 0.15 + // 8.0 0.62 + // 10.0 0.77 + normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); + + // Norm to max element + // 2.0 0.2 (2.0/10.0) + // 8.0 0.8 (8.0/10.0) + // 10.0 1.0 (10.0/10.0) + normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); + + // Norm to range [0.0;1.0] + // 2.0 0.0 (shift to left border) + // 8.0 0.75 (6.0/8.0) + // 10.0 1.0 (shift to right border) + normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); +@endcode + +@param src input array. +@param dst output array of the same size as src . +@param alpha norm value to normalize to or the lower range boundary in case of the range +normalization. +@param beta upper range boundary in case of the range normalization; it is not used for the norm +normalization. +@param norm_type normalization type (see cv::NormTypes). +@param dtype when negative, the output array has the same type as src; otherwise, it has the same +number of channels as src and the depth =CV_MAT_DEPTH(dtype). +@param mask optional operation mask. +@sa norm, Mat::convertTo, SparseMat::convertTo +*/ +CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0, + int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray()); + +/** @overload +@param src input array. +@param dst output array of the same size as src . +@param alpha norm value to normalize to or the lower range boundary in case of the range +normalization. +@param normType normalization type (see cv::NormTypes). +*/ +CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType ); + +/** @brief Finds the global minimum and maximum in an array. + +The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The +extremums are searched across the whole array or, if mask is not an empty array, in the specified +array region. + +The function do not work with multi-channel arrays. If you need to find minimum or maximum +elements across all the channels, use Mat::reshape first to reinterpret the array as +single-channel. Or you may extract the particular channel using either extractImageCOI , or +mixChannels , or split . +@param src input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required. +@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required. +@param mask optional mask used to select a sub-array. +@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape +*/ +CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal, + CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0, + CV_OUT Point* maxLoc = 0, InputArray mask = noArray()); + + +/** @brief Finds the global minimum and maximum in an array + +The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The +extremums are searched across the whole array or, if mask is not an empty array, in the specified +array region. The function does not work with multi-channel arrays. If you need to find minimum or +maximum elements across all the channels, use Mat::reshape first to reinterpret the array as +single-channel. Or you may extract the particular channel using either extractImageCOI , or +mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements +only. +@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is +a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 +dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be +(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be +(0,j1)/(0,j2)). +@param src input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; +Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element +in each dimension are stored there sequentially. +@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. +@param mask specified array region +*/ +CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0, + int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray()); + +/** @overload +@param a input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; +Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element +in each dimension are stored there sequentially. +@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. +*/ +CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal, + double* maxVal, int* minIdx = 0, int* maxIdx = 0); + +/** @brief Reduces a matrix to a vector. + +The function cv::reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of +1D vectors and performing the specified operation on the vectors until a single row/column is +obtained. For example, the function can be used to compute horizontal and vertical projections of a +raster image. In case of REDUCE_MAX and REDUCE_MIN , the output image should have the same type as the source one. +In case of REDUCE_SUM and REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. +And multi-channel arrays are also supported in these two reduction modes. +@param src input 2D matrix. +@param dst output vector. Its size and type is defined by dim and dtype parameters. +@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to +a single row. 1 means that the matrix is reduced to a single column. +@param rtype reduction operation that could be one of cv::ReduceTypes +@param dtype when negative, the output vector will have the same type as the input matrix, +otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). +@sa repeat +*/ +CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1); + +/** @brief Creates one multi-channel array out of several single-channel ones. + +The function cv::merge merges several arrays to make a single multi-channel array. That is, each +element of the output array will be a concatenation of the elements of the input arrays, where +elements of i-th input array are treated as mv[i].channels()-element vectors. + +The function cv::split does the reverse operation. If you need to shuffle channels in some other +advanced way, use cv::mixChannels. +@param mv input array of matrices to be merged; all the matrices in mv must have the same +size and the same depth. +@param count number of input matrices when mv is a plain C array; it must be greater than zero. +@param dst output array of the same size and the same depth as mv[0]; The number of channels will +be equal to the parameter count. +@sa mixChannels, split, Mat::reshape +*/ +CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst); + +/** @overload +@param mv input vector of matrices to be merged; all the matrices in mv must have the same +size and the same depth. +@param dst output array of the same size and the same depth as mv[0]; The number of channels will +be the total number of channels in the matrix array. + */ +CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst); + +/** @brief Divides a multi-channel array into several single-channel arrays. + +The function cv::split splits a multi-channel array into separate single-channel arrays: +\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f] +If you need to extract a single channel or do some other sophisticated channel permutation, use +mixChannels . +@param src input multi-channel array. +@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are +reallocated, if needed. +@sa merge, mixChannels, cvtColor +*/ +CV_EXPORTS void split(const Mat& src, Mat* mvbegin); + +/** @overload +@param m input multi-channel array. +@param mv output vector of arrays; the arrays themselves are reallocated, if needed. +*/ +CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv); + +/** @brief Copies specified channels from input arrays to the specified channels of +output arrays. + +The function cv::mixChannels provides an advanced mechanism for shuffling image channels. + +cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels. + +In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R +channels swapped) and a separate alpha-channel image: +@code{.cpp} + Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); + Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); + Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); + + // forming an array of matrices is a quite efficient operation, + // because the matrix data is not copied, only the headers + Mat out[] = { bgr, alpha }; + // bgra[0] -> bgr[2], bgra[1] -> bgr[1], + // bgra[2] -> bgr[0], bgra[3] -> alpha[0] + int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; + mixChannels( &bgra, 1, out, 2, from_to, 4 ); +@endcode +@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and +Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the +function. +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param nsrcs number of matrices in `src`. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in `src[0]`. +@param ndsts number of matrices in `dst`. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +@param npairs number of index pairs in `fromTo`. +@sa split, merge, extractChannel, insertChannel, cvtColor +*/ +CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, + const int* fromTo, size_t npairs); + +/** @overload +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in src[0]. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +@param npairs number of index pairs in fromTo. +*/ +CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, + const int* fromTo, size_t npairs); + +/** @overload +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in src[0]. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +*/ +CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, + const std::vector& fromTo); + +/** @brief Extracts a single channel from src (coi is 0-based index) +@param src input array +@param dst output array +@param coi index of channel to extract +@sa mixChannels, split +*/ +CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi); + +/** @brief Inserts a single channel to dst (coi is 0-based index) +@param src input array +@param dst output array +@param coi index of channel for insertion +@sa mixChannels, merge +*/ +CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi); + +/** @brief Flips a 2D array around vertical, horizontal, or both axes. + +The function cv::flip flips the array in one of three different ways (row +and column indices are 0-based): +\f[\texttt{dst} _{ij} = +\left\{ +\begin{array}{l l} +\texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ +\texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ +\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ +\end{array} +\right.\f] +The example scenarios of using the function are the following: +* Vertical flipping of the image (flipCode == 0) to switch between + top-left and bottom-left image origin. This is a typical operation + in video processing on Microsoft Windows\* OS. +* Horizontal flipping of the image with the subsequent horizontal + shift and absolute difference calculation to check for a + vertical-axis symmetry (flipCode \> 0). +* Simultaneous horizontal and vertical flipping of the image with + the subsequent shift and absolute difference calculation to check + for a central symmetry (flipCode \< 0). +* Reversing the order of point arrays (flipCode \> 0 or + flipCode == 0). +@param src input array. +@param dst output array of the same size and type as src. +@param flipCode a flag to specify how to flip the array; 0 means +flipping around the x-axis and positive value (for example, 1) means +flipping around y-axis. Negative value (for example, -1) means flipping +around both axes. +@sa transpose , repeat , completeSymm +*/ +CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode); + +enum RotateFlags { + ROTATE_90_CLOCKWISE = 0, //Rotate 90 degrees clockwise + ROTATE_180 = 1, //Rotate 180 degrees clockwise + ROTATE_90_COUNTERCLOCKWISE = 2, //Rotate 270 degrees clockwise +}; +/** @brief Rotates a 2D array in multiples of 90 degrees. +The function rotate rotates the array in one of three different ways: +* Rotate by 90 degrees clockwise (rotateCode = ROTATE_90). +* Rotate by 180 degrees clockwise (rotateCode = ROTATE_180). +* Rotate by 270 degrees clockwise (rotateCode = ROTATE_270). +@param src input array. +@param dst output array of the same type as src. The size is the same with ROTATE_180, +and the rows and cols are switched for ROTATE_90 and ROTATE_270. +@param rotateCode an enum to specify how to rotate the array; see the enum RotateFlags +@sa transpose , repeat , completeSymm, flip, RotateFlags +*/ +CV_EXPORTS_W void rotate(InputArray src, OutputArray dst, int rotateCode); + +/** @brief Fills the output array with repeated copies of the input array. + +The function cv::repeat duplicates the input array one or more times along each of the two axes: +\f[\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\f] +The second variant of the function is more convenient to use with @ref MatrixExpressions. +@param src input array to replicate. +@param ny Flag to specify how many times the `src` is repeated along the +vertical axis. +@param nx Flag to specify how many times the `src` is repeated along the +horizontal axis. +@param dst output array of the same type as `src`. +@sa cv::reduce +*/ +CV_EXPORTS_W void repeat(InputArray src, int ny, int nx, OutputArray dst); + +/** @overload +@param src input array to replicate. +@param ny Flag to specify how many times the `src` is repeated along the +vertical axis. +@param nx Flag to specify how many times the `src` is repeated along the +horizontal axis. + */ +CV_EXPORTS Mat repeat(const Mat& src, int ny, int nx); + +/** @brief Applies horizontal concatenation to given matrices. + +The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows). +@code{.cpp} + cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::hconcat( matArray, 3, out ); + //out: + //[1, 2, 3; + // 1, 2, 3; + // 1, 2, 3; + // 1, 2, 3] +@endcode +@param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. +@param nsrc number of matrices in src. +@param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. +@sa cv::vconcat(const Mat*, size_t, OutputArray), @sa cv::vconcat(InputArrayOfArrays, OutputArray) and @sa cv::vconcat(InputArray, InputArray, OutputArray) +*/ +CV_EXPORTS void hconcat(const Mat* src, size_t nsrc, OutputArray dst); +/** @overload + @code{.cpp} + cv::Mat_ A = (cv::Mat_(3, 2) << 1, 4, + 2, 5, + 3, 6); + cv::Mat_ B = (cv::Mat_(3, 2) << 7, 10, + 8, 11, + 9, 12); + + cv::Mat C; + cv::hconcat(A, B, C); + //C: + //[1, 4, 7, 10; + // 2, 5, 8, 11; + // 3, 6, 9, 12] + @endcode + @param src1 first input array to be considered for horizontal concatenation. + @param src2 second input array to be considered for horizontal concatenation. + @param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2. + */ +CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst); +/** @overload + @code{.cpp} + std::vector matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::hconcat( matrices, out ); + //out: + //[1, 2, 3; + // 1, 2, 3; + // 1, 2, 3; + // 1, 2, 3] + @endcode + @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. + @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. +same depth. + */ +CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst); + +/** @brief Applies vertical concatenation to given matrices. + +The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). +@code{.cpp} + cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::vconcat( matArray, 3, out ); + //out: + //[1, 1, 1, 1; + // 2, 2, 2, 2; + // 3, 3, 3, 3] +@endcode +@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. +@param nsrc number of matrices in src. +@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. +@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray) +*/ +CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst); +/** @overload + @code{.cpp} + cv::Mat_ A = (cv::Mat_(3, 2) << 1, 7, + 2, 8, + 3, 9); + cv::Mat_ B = (cv::Mat_(3, 2) << 4, 10, + 5, 11, + 6, 12); + + cv::Mat C; + cv::vconcat(A, B, C); + //C: + //[1, 7; + // 2, 8; + // 3, 9; + // 4, 10; + // 5, 11; + // 6, 12] + @endcode + @param src1 first input array to be considered for vertical concatenation. + @param src2 second input array to be considered for vertical concatenation. + @param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2. + */ +CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst); +/** @overload + @code{.cpp} + std::vector matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::vconcat( matrices, out ); + //out: + //[1, 1, 1, 1; + // 2, 2, 2, 2; + // 3, 3, 3, 3] + @endcode + @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth + @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. +same depth. + */ +CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst); + +/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2) +Calculates the per-element bit-wise conjunction of two arrays or an +array and a scalar. + +The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the second and third cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Calculates the per-element bit-wise disjunction of two arrays or an +array and a scalar. + +The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the second and third cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Calculates the per-element bit-wise "exclusive or" operation on two +arrays or an array and a scalar. + +The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" +operation for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the 2nd and 3rd cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Inverts every bit of an array. + +The function cv::bitwise_not calculates per-element bit-wise inversion of the input +array: +\f[\texttt{dst} (I) = \neg \texttt{src} (I)\f] +In case of a floating-point input array, its machine-specific bit +representation (usually IEEE754-compliant) is used for the operation. In +case of multi-channel arrays, each channel is processed independently. +@param src input array. +@param dst output array that has the same size and type as the input +array. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst, + InputArray mask = noArray()); + +/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar. + +The function cv::absdiff calculates: +* Absolute difference between two arrays when they have the same + size and type: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\f] +* Absolute difference between an array and a scalar when the second + array is constructed from Scalar or has as many elements as the + number of channels in `src1`: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\f] +* Absolute difference between a scalar and an array when the first + array is constructed from Scalar or has as many elements as the + number of channels in `src2`: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\f] + where I is a multi-dimensional index of array elements. In case of + multi-channel arrays, each channel is processed independently. +@note Saturation is not applied when the arrays have the depth CV_32S. +You may even get a negative value in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as input arrays. +@sa cv::abs(const Mat&) +*/ +CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst); + +/** @brief Checks if array elements lie between the elements of two other arrays. + +The function checks the range as follows: +- For every element of a single-channel input array: + \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\f] +- For two-channel arrays: + \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\f] +- and so forth. + +That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the +specified 1D, 2D, 3D, ... box and 0 otherwise. + +When the lower and/or upper boundary parameters are scalars, the indexes +(I) at lowerb and upperb in the above formulas should be omitted. +@param src first input array. +@param lowerb inclusive lower boundary array or a scalar. +@param upperb inclusive upper boundary array or a scalar. +@param dst output array of the same size as src and CV_8U type. +*/ +CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb, + InputArray upperb, OutputArray dst); + +/** @brief Performs the per-element comparison of two arrays or an array and scalar value. + +The function compares: +* Elements of two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\f] +* Elements of src1 with a scalar src2 when src2 is constructed from + Scalar or has a single element: + \f[\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\f] +* src1 with elements of src2 when src1 is constructed from Scalar or + has a single element: + \f[\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\f] +When the comparison result is true, the corresponding element of output +array is set to 255. The comparison operations can be replaced with the +equivalent matrix expressions: +@code{.cpp} + Mat dst1 = src1 >= src2; + Mat dst2 = src1 < 8; + ... +@endcode +@param src1 first input array or a scalar; when it is an array, it must have a single channel. +@param src2 second input array or a scalar; when it is an array, it must have a single channel. +@param dst output array of type ref CV_8U that has the same size and the same number of channels as + the input arrays. +@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes) +@sa checkRange, min, max, threshold +*/ +CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop); + +/** @brief Calculates per-element minimum of two arrays or an array and a scalar. + +The function cv::min calculates the per-element minimum of two arrays: +\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f] +or array and a scalar: +\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f] +@param src1 first input array. +@param src2 second input array of the same size and type as src1. +@param dst output array of the same size and type as src1. +@sa max, compare, inRange, minMaxLoc +*/ +CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst); + +/** @brief Calculates per-element maximum of two arrays or an array and a scalar. + +The function cv::max calculates the per-element maximum of two arrays: +\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f] +or array and a scalar: +\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f] +@param src1 first input array. +@param src2 second input array of the same size and type as src1 . +@param dst output array of the same size and type as src1. +@sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions +*/ +CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst); + +/** @brief Calculates a square root of array elements. + +The function cv::sqrt calculates a square root of each input array element. +In case of multi-channel arrays, each channel is processed +independently. The accuracy is approximately the same as of the built-in +std::sqrt . +@param src input floating-point array. +@param dst output array of the same size and type as src. +*/ +CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst); + +/** @brief Raises every array element to a power. + +The function cv::pow raises every element of the input array to power : +\f[\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f] + +So, for a non-integer power exponent, the absolute values of input array +elements are used. However, it is possible to get true values for +negative values using some extra operations. In the example below, +computing the 5th root of array src shows: +@code{.cpp} + Mat mask = src < 0; + pow(src, 1./5, dst); + subtract(Scalar::all(0), dst, dst, mask); +@endcode +For some values of power, such as integer values, 0.5 and -0.5, +specialized faster algorithms are used. + +Special values (NaN, Inf) are not handled. +@param src input array. +@param power exponent of power. +@param dst output array of the same size and type as src. +@sa sqrt, exp, log, cartToPolar, polarToCart +*/ +CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst); + +/** @brief Calculates the exponent of every array element. + +The function cv::exp calculates the exponent of every element of the input +array: +\f[\texttt{dst} [I] = e^{ src(I) }\f] + +The maximum relative error is about 7e-6 for single-precision input and +less than 1e-10 for double-precision input. Currently, the function +converts denormalized values to zeros on output. Special values (NaN, +Inf) are not handled. +@param src input array. +@param dst output array of the same size and type as src. +@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude +*/ +CV_EXPORTS_W void exp(InputArray src, OutputArray dst); + +/** @brief Calculates the natural logarithm of every array element. + +The function cv::log calculates the natural logarithm of every element of the input array: +\f[\texttt{dst} (I) = \log (\texttt{src}(I)) \f] + +Output on zero, negative and special (NaN, Inf) values is undefined. + +@param src input array. +@param dst output array of the same size and type as src . +@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude +*/ +CV_EXPORTS_W void log(InputArray src, OutputArray dst); + +/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle. + +The function cv::polarToCart calculates the Cartesian coordinates of each 2D +vector represented by the corresponding elements of magnitude and angle: +\f[\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f] + +The relative accuracy of the estimated coordinates is about 1e-6. +@param magnitude input floating-point array of magnitudes of 2D vectors; +it can be an empty matrix (=Mat()), in this case, the function assumes +that all the magnitudes are =1; if it is not empty, it must have the +same size and type as angle. +@param angle input floating-point array of angles of 2D vectors. +@param x output array of x-coordinates of 2D vectors; it has the same +size and type as angle. +@param y output array of y-coordinates of 2D vectors; it has the same +size and type as angle. +@param angleInDegrees when true, the input angles are measured in +degrees, otherwise, they are measured in radians. +@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt +*/ +CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle, + OutputArray x, OutputArray y, bool angleInDegrees = false); + +/** @brief Calculates the magnitude and angle of 2D vectors. + +The function cv::cartToPolar calculates either the magnitude, angle, or both +for every 2D vector (x(I),y(I)): +\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f] + +The angles are calculated with accuracy about 0.3 degrees. For the point +(0,0), the angle is set to 0. +@param x array of x-coordinates; this must be a single-precision or +double-precision floating-point array. +@param y array of y-coordinates, that must have the same size and same type as x. +@param magnitude output array of magnitudes of the same size and type as x. +@param angle output array of angles that has the same size and type as +x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). +@param angleInDegrees a flag, indicating whether the angles are measured +in radians (which is by default), or in degrees. +@sa Sobel, Scharr +*/ +CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y, + OutputArray magnitude, OutputArray angle, + bool angleInDegrees = false); + +/** @brief Calculates the rotation angle of 2D vectors. + +The function cv::phase calculates the rotation angle of each 2D vector that +is formed from the corresponding elements of x and y : +\f[\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f] + +The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , +the corresponding angle(I) is set to 0. +@param x input floating-point array of x-coordinates of 2D vectors. +@param y input array of y-coordinates of 2D vectors; it must have the +same size and the same type as x. +@param angle output array of vector angles; it has the same size and +same type as x . +@param angleInDegrees when true, the function calculates the angle in +degrees, otherwise, they are measured in radians. +*/ +CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle, + bool angleInDegrees = false); + +/** @brief Calculates the magnitude of 2D vectors. + +The function cv::magnitude calculates the magnitude of 2D vectors formed +from the corresponding elements of x and y arrays: +\f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f] +@param x floating-point array of x-coordinates of the vectors. +@param y floating-point array of y-coordinates of the vectors; it must +have the same size as x. +@param magnitude output array of the same size and type as x. +@sa cartToPolar, polarToCart, phase, sqrt +*/ +CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude); + +/** @brief Checks every element of an input array for invalid values. + +The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \> +-DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and +maxVal. In case of multi-channel arrays, each channel is processed independently. If some values +are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the +function either returns false (when quiet=true) or throws an exception. +@param a input array. +@param quiet a flag, indicating whether the functions quietly return false when the array elements +are out of range or they throw an exception. +@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims +elements. +@param minVal inclusive lower boundary of valid values range. +@param maxVal exclusive upper boundary of valid values range. +*/ +CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0, + double minVal = -DBL_MAX, double maxVal = DBL_MAX); + +/** @brief converts NaN's to the given number +*/ +CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0); + +/** @brief Performs generalized matrix multiplication. + +The function cv::gemm performs generalized matrix multiplication similar to the +gemm functions in BLAS level 3. For example, +`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` +corresponds to +\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f] + +In case of complex (two-channel) data, performed a complex matrix +multiplication. + +The function can be replaced with a matrix expression. For example, the +above call can be replaced with: +@code{.cpp} + dst = alpha*src1.t()*src2 + beta*src3.t(); +@endcode +@param src1 first multiplied input matrix that could be real(CV_32FC1, +CV_64FC1) or complex(CV_32FC2, CV_64FC2). +@param src2 second multiplied input matrix of the same type as src1. +@param alpha weight of the matrix product. +@param src3 third optional delta matrix added to the matrix product; it +should have the same type as src1 and src2. +@param beta weight of src3. +@param dst output matrix; it has the proper size and the same type as +input matrices. +@param flags operation flags (cv::GemmFlags) +@sa mulTransposed , transform +*/ +CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha, + InputArray src3, double beta, OutputArray dst, int flags = 0); + +/** @brief Calculates the product of a matrix and its transposition. + +The function cv::mulTransposed calculates the product of src and its +transposition: +\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f] +if aTa=true , and +\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f] +otherwise. The function is used to calculate the covariance matrix. With +zero delta, it can be used as a faster substitute for general matrix +product A\*B when B=A' +@param src input single-channel matrix. Note that unlike gemm, the +function can multiply not only floating-point matrices. +@param dst output square matrix. +@param aTa Flag specifying the multiplication ordering. See the +description below. +@param delta Optional delta matrix subtracted from src before the +multiplication. When the matrix is empty ( delta=noArray() ), it is +assumed to be zero, that is, nothing is subtracted. If it has the same +size as src , it is simply subtracted. Otherwise, it is "repeated" (see +repeat ) to cover the full src and then subtracted. Type of the delta +matrix, when it is not empty, must be the same as the type of created +output matrix. See the dtype parameter description below. +@param scale Optional scale factor for the matrix product. +@param dtype Optional type of the output matrix. When it is negative, +the output matrix will have the same type as src . Otherwise, it will be +type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . +@sa calcCovarMatrix, gemm, repeat, reduce +*/ +CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa, + InputArray delta = noArray(), + double scale = 1, int dtype = -1 ); + +/** @brief Transposes a matrix. + +The function cv::transpose transposes the matrix src : +\f[\texttt{dst} (i,j) = \texttt{src} (j,i)\f] +@note No complex conjugation is done in case of a complex matrix. It it +should be done separately if needed. +@param src input array. +@param dst output array of the same type as src. +*/ +CV_EXPORTS_W void transpose(InputArray src, OutputArray dst); + +/** @brief Performs the matrix transformation of every array element. + +The function cv::transform performs the matrix transformation of every +element of the array src and stores the results in dst : +\f[\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\f] +(when m.cols=src.channels() ), or +\f[\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\f] +(when m.cols=src.channels()+1 ) + +Every element of the N -channel array src is interpreted as N -element +vector that is transformed using the M x N or M x (N+1) matrix m to +M-element vector - the corresponding element of the output array dst . + +The function may be used for geometrical transformation of +N -dimensional points, arbitrary linear color space transformation (such +as various kinds of RGB to YUV transforms), shuffling the image +channels, and so forth. +@param src input array that must have as many channels (1 to 4) as +m.cols or m.cols-1. +@param dst output array of the same size and depth as src; it has as +many channels as m.rows. +@param m transformation 2x2 or 2x3 floating-point matrix. +@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective +*/ +CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m ); + +/** @brief Performs the perspective matrix transformation of vectors. + +The function cv::perspectiveTransform transforms every element of src by +treating it as a 2D or 3D vector, in the following way: +\f[(x, y, z) \rightarrow (x'/w, y'/w, z'/w)\f] +where +\f[(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\f] +and +\f[w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f] + +Here a 3D vector transformation is shown. In case of a 2D vector +transformation, the z component is omitted. + +@note The function transforms a sparse set of 2D or 3D vectors. If you +want to transform an image using perspective transformation, use +warpPerspective . If you have an inverse problem, that is, you want to +compute the most probable perspective transformation out of several +pairs of corresponding points, you can use getPerspectiveTransform or +findHomography . +@param src input two-channel or three-channel floating-point array; each +element is a 2D/3D vector to be transformed. +@param dst output array of the same size and type as src. +@param m 3x3 or 4x4 floating-point transformation matrix. +@sa transform, warpPerspective, getPerspectiveTransform, findHomography +*/ +CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m ); + +/** @brief Copies the lower or the upper half of a square matrix to another half. + +The function cv::completeSymm copies the lower half of a square matrix to +its another half. The matrix diagonal remains unchanged: +* \f$\texttt{mtx}_{ij}=\texttt{mtx}_{ji}\f$ for \f$i > j\f$ if + lowerToUpper=false +* \f$\texttt{mtx}_{ij}=\texttt{mtx}_{ji}\f$ for \f$i < j\f$ if + lowerToUpper=true +@param mtx input-output floating-point square matrix. +@param lowerToUpper operation flag; if true, the lower half is copied to +the upper half. Otherwise, the upper half is copied to the lower half. +@sa flip, transpose +*/ +CV_EXPORTS_W void completeSymm(InputOutputArray mtx, bool lowerToUpper = false); + +/** @brief Initializes a scaled identity matrix. + +The function cv::setIdentity initializes a scaled identity matrix: +\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f] + +The function can also be emulated using the matrix initializers and the +matrix expressions: +@code + Mat A = Mat::eye(4, 3, CV_32F)*5; + // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] +@endcode +@param mtx matrix to initialize (not necessarily square). +@param s value to assign to diagonal elements. +@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= +*/ +CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1)); + +/** @brief Returns the determinant of a square floating-point matrix. + +The function cv::determinant calculates and returns the determinant of the +specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the +direct method is used. For larger matrices, the function uses LU +factorization with partial pivoting. + +For symmetric positively-determined matrices, it is also possible to use +eigen decomposition to calculate the determinant. +@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and +square size. +@sa trace, invert, solve, eigen, @ref MatrixExpressions +*/ +CV_EXPORTS_W double determinant(InputArray mtx); + +/** @brief Returns the trace of a matrix. + +The function cv::trace returns the sum of the diagonal elements of the +matrix mtx . +\f[\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\f] +@param mtx input matrix. +*/ +CV_EXPORTS_W Scalar trace(InputArray mtx); + +/** @brief Finds the inverse or pseudo-inverse of a matrix. + +The function cv::invert inverts the matrix src and stores the result in dst +. When the matrix src is singular or non-square, the function calculates +the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is +minimal, where I is an identity matrix. + +In case of the DECOMP_LU method, the function returns non-zero value if +the inverse has been successfully calculated and 0 if src is singular. + +In case of the DECOMP_SVD method, the function returns the inverse +condition number of src (the ratio of the smallest singular value to the +largest singular value) and 0 if src is singular. The SVD method +calculates a pseudo-inverse matrix if src is singular. + +Similarly to DECOMP_LU, the method DECOMP_CHOLESKY works only with +non-singular square matrices that should also be symmetrical and +positively defined. In this case, the function stores the inverted +matrix in dst and returns non-zero. Otherwise, it returns 0. + +@param src input floating-point M x N matrix. +@param dst output matrix of N x M size and the same type as src. +@param flags inversion method (cv::DecompTypes) +@sa solve, SVD +*/ +CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU); + +/** @brief Solves one or more linear systems or least-squares problems. + +The function cv::solve solves a linear system or least-squares problem (the +latter is possible with SVD or QR methods, or by specifying the flag +DECOMP_NORMAL ): +\f[\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\f] + +If DECOMP_LU or DECOMP_CHOLESKY method is used, the function returns 1 +if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise, +it returns 0. In the latter case, dst is not valid. Other methods find a +pseudo-solution in case of a singular left-hand side part. + +@note If you want to find a unity-norm solution of an under-defined +singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve +will not do the work. Use SVD::solveZ instead. + +@param src1 input matrix on the left-hand side of the system. +@param src2 input matrix on the right-hand side of the system. +@param dst output solution. +@param flags solution (matrix inversion) method (cv::DecompTypes) +@sa invert, SVD, eigen +*/ +CV_EXPORTS_W bool solve(InputArray src1, InputArray src2, + OutputArray dst, int flags = DECOMP_LU); + +/** @brief Sorts each row or each column of a matrix. + +The function cv::sort sorts each matrix row or each matrix column in +ascending or descending order. So you should pass two operation flags to +get desired behaviour. If you want to sort matrix rows or columns +lexicographically, you can use STL std::sort generic function with the +proper comparison predicate. + +@param src input single-channel array. +@param dst output array of the same size and type as src. +@param flags operation flags, a combination of cv::SortFlags +@sa sortIdx, randShuffle +*/ +CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags); + +/** @brief Sorts each row or each column of a matrix. + +The function cv::sortIdx sorts each matrix row or each matrix column in the +ascending or descending order. So you should pass two operation flags to +get desired behaviour. Instead of reordering the elements themselves, it +stores the indices of sorted elements in the output array. For example: +@code + Mat A = Mat::eye(3,3,CV_32F), B; + sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); + // B will probably contain + // (because of equal elements in A some permutations are possible): + // [[1, 2, 0], [0, 2, 1], [0, 1, 2]] +@endcode +@param src input single-channel array. +@param dst output integer array of the same size as src. +@param flags operation flags that could be a combination of cv::SortFlags +@sa sort, randShuffle +*/ +CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags); + +/** @brief Finds the real roots of a cubic equation. + +The function solveCubic finds the real roots of a cubic equation: +- if coeffs is a 4-element vector: +\f[\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\f] +- if coeffs is a 3-element vector: +\f[x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\f] + +The roots are stored in the roots array. +@param coeffs equation coefficients, an array of 3 or 4 elements. +@param roots output array of real roots that has 1 or 3 elements. +*/ +CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots); + +/** @brief Finds the real or complex roots of a polynomial equation. + +The function cv::solvePoly finds real and complex roots of a polynomial equation: +\f[\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\f] +@param coeffs array of polynomial coefficients. +@param roots output (complex) array of roots. +@param maxIters maximum number of iterations the algorithm does. +*/ +CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300); + +/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix. + +The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric +matrix src: +@code + src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() +@endcode +@note in the new and the old interfaces different ordering of eigenvalues and eigenvectors +parameters is used. +@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical +(src ^T^ == src). +@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored +in the descending order. +@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the +eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding +eigenvalues. +@sa completeSymm , PCA +*/ +CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues, + OutputArray eigenvectors = noArray()); + +/** @brief Calculates the covariance matrix of a set of vectors. + +The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of +the set of input vectors. +@param samples samples stored as separate matrices +@param nsamples number of samples +@param covar output covariance matrix of the type ctype and square size. +@param mean input or output (depending on the flags) array as the average value of the input vectors. +@param flags operation flags as a combination of cv::CovarFlags +@param ctype type of the matrixl; it equals 'CV_64F' by default. +@sa PCA, mulTransposed, Mahalanobis +@todo InputArrayOfArrays +*/ +CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean, + int flags, int ctype = CV_64F); + +/** @overload +@note use cv::COVAR_ROWS or cv::COVAR_COLS flag +@param samples samples stored as rows/columns of a single matrix. +@param covar output covariance matrix of the type ctype and square size. +@param mean input or output (depending on the flags) array as the average value of the input vectors. +@param flags operation flags as a combination of cv::CovarFlags +@param ctype type of the matrixl; it equals 'CV_64F' by default. +*/ +CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar, + InputOutputArray mean, int flags, int ctype = CV_64F); + +/** wrap PCA::operator() */ +CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, int maxComponents = 0); + +/** wrap PCA::operator() */ +CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, double retainedVariance); + +/** wrap PCA::project */ +CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean, + InputArray eigenvectors, OutputArray result); + +/** wrap PCA::backProject */ +CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean, + InputArray eigenvectors, OutputArray result); + +/** wrap SVD::compute */ +CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 ); + +/** wrap SVD::backSubst */ +CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt, + InputArray rhs, OutputArray dst ); + +/** @brief Calculates the Mahalanobis distance between two vectors. + +The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: +\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f] +The covariance matrix may be calculated using the cv::calcCovarMatrix function and then inverted using +the invert function (preferably using the cv::DECOMP_SVD method, as the most accurate). +@param v1 first 1D input vector. +@param v2 second 1D input vector. +@param icovar inverse covariance matrix. +*/ +CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar); + +/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. + +The function cv::dft performs one of the following: +- Forward the Fourier transform of a 1D vector of N elements: + \f[Y = F^{(N)} \cdot X,\f] + where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$ +- Inverse the Fourier transform of a 1D vector of N elements: + \f[\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\f] + where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$ +- Forward the 2D Fourier transform of a M x N matrix: + \f[Y = F^{(M)} \cdot X \cdot F^{(N)}\f] +- Inverse the 2D Fourier transform of a M x N matrix: + \f[\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\f] + +In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input +spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* +(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here +is how 2D *CCS* spectrum looks: +\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f] + +In case of 1D transform of a real vector, the output looks like the first row of the matrix above. + +So, the function chooses an operation mode depending on the flags and size of the input array: +- If DFT_ROWS is set or the input array has a single row or single column, the function + performs a 1D forward or inverse transform of each row of a matrix when DFT_ROWS is set. + Otherwise, it performs a 2D transform. +- If the input array is real and DFT_INVERSE is not set, the function performs a forward 1D or + 2D transform: + - When DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as + input. + - When DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as + input. In case of 2D transform, it uses the packed format as shown above. In case of a + single 1D transform, it looks like the first row of the matrix above. In case of + multiple 1D transforms (when using the DFT_ROWS flag), each row of the output matrix + looks like the first row of the matrix above. +- If the input array is complex and either DFT_INVERSE or DFT_REAL_OUTPUT are not set, the + output is a complex array of the same size as input. The function performs a forward or + inverse 1D or 2D transform of the whole input array or each row of the input array + independently, depending on the flags DFT_INVERSE and DFT_ROWS. +- When DFT_INVERSE is set and the input array is real, or it is complex but DFT_REAL_OUTPUT + is set, the output is a real array of the same size as input. The function performs a 1D or 2D + inverse transformation of the whole input array or each individual row, depending on the flags + DFT_INVERSE and DFT_ROWS. + +If DFT_SCALE is set, the scaling is done after the transformation. + +Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed +efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the +current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize +method. + +The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: +@code + void convolveDFT(InputArray A, InputArray B, OutputArray C) + { + // reallocate the output array if needed + C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); + Size dftSize; + // calculate the size of DFT transform + dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); + dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); + + // allocate temporary buffers and initialize them with 0's + Mat tempA(dftSize, A.type(), Scalar::all(0)); + Mat tempB(dftSize, B.type(), Scalar::all(0)); + + // copy A and B to the top-left corners of tempA and tempB, respectively + Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); + A.copyTo(roiA); + Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); + B.copyTo(roiB); + + // now transform the padded A & B in-place; + // use "nonzeroRows" hint for faster processing + dft(tempA, tempA, 0, A.rows); + dft(tempB, tempB, 0, B.rows); + + // multiply the spectrums; + // the function handles packed spectrum representations well + mulSpectrums(tempA, tempB, tempA); + + // transform the product back from the frequency domain. + // Even though all the result rows will be non-zero, + // you need only the first C.rows of them, and thus you + // pass nonzeroRows == C.rows + dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); + + // now copy the result back to C. + tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); + + // all the temporary buffers will be deallocated automatically + } +@endcode +To optimize this sample, consider the following approaches: +- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to + the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole + tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) + rightmost columns of the matrices. +- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B + is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. + To do this, you need to split the output array C into multiple tiles. For each tile, estimate + which parts of A and B are required to calculate convolution in this tile. If the tiles in C are + too small, the speed will decrease a lot because of repeated work. In the ultimate case, when + each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution + algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and + there is also a slowdown because of bad cache locality. So, there is an optimal tile size + somewhere in the middle. +- If different tiles in C can be calculated in parallel and, thus, the convolution is done by + parts, the loop can be threaded. + +All of the above improvements have been implemented in matchTemplate and filter2D . Therefore, by +using them, you can get the performance even better than with the above theoretically optimal +implementation. Though, those two functions actually calculate cross-correlation, not convolution, +so you need to "flip" the second convolution operand B vertically and horizontally using flip . +@note +- An example using the discrete fourier transform can be found at + opencv_source_code/samples/cpp/dft.cpp +- (Python) An example using the dft functionality to perform Wiener deconvolution can be found + at opencv_source/samples/python/deconvolution.py +- (Python) An example rearranging the quadrants of a Fourier image can be found at + opencv_source/samples/python/dft.py +@param src input array that could be real or complex. +@param dst output array whose size and type depends on the flags . +@param flags transformation flags, representing a combination of the cv::DftFlags +@param nonzeroRows when the parameter is not zero, the function assumes that only the first +nonzeroRows rows of the input array (DFT_INVERSE is not set) or only the first nonzeroRows of the +output array (DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the +rows more efficiently and save some time; this technique is very useful for calculating array +cross-correlation or convolution using DFT. +@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , +magnitude , phase +*/ +CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); + +/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. + +idft(src, dst, flags) is equivalent to dft(src, dst, flags | DFT_INVERSE) . +@note None of dft and idft scales the result by default. So, you should pass DFT_SCALE to one of +dft or idft explicitly to make these transforms mutually inverse. +@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize +@param src input floating-point real or complex array. +@param dst output array whose size and type depend on the flags. +@param flags operation flags (see dft and cv::DftFlags). +@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see +the convolution sample in dft description. +*/ +CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); + +/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array. + +The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D +floating-point array: +- Forward Cosine transform of a 1D vector of N elements: + \f[Y = C^{(N)} \cdot X\f] + where + \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f] + and + \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*. +- Inverse Cosine transform of a 1D vector of N elements: + \f[X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\f] + (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ ) +- Forward 2D Cosine transform of M x N matrix: + \f[Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\f] +- Inverse 2D Cosine transform of M x N matrix: + \f[X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\f] + +The function chooses the mode of operation by looking at the flags and size of the input array: +- If (flags & DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it + is an inverse 1D or 2D transform. +- If (flags & DCT_ROWS) != 0 , the function performs a 1D transform of each row. +- If the array is a single column or a single row, the function performs a 1D transform. +- If none of the above is true, the function performs a 2D transform. + +@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you +can pad the array when necessary. +Also, the function performance depends very much, and not monotonically, on the array size (see +getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT +of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: +@code + size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } + N1 = getOptimalDCTSize(N); +@endcode +@param src input floating-point array. +@param dst output array of the same size and type as src . +@param flags transformation flags as a combination of cv::DftFlags (DCT_*) +@sa dft , getOptimalDFTSize , idct +*/ +CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0); + +/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. + +idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). +@param src input floating-point single-channel array. +@param dst output array of the same size and type as src. +@param flags operation flags. +@sa dct, dft, idft, getOptimalDFTSize +*/ +CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0); + +/** @brief Performs the per-element multiplication of two Fourier spectrums. + +The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex +matrices that are results of a real or complex Fourier transform. + +The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) +or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are +simply multiplied (per element) with an optional conjugation of the second-array elements. When the +arrays are real, they are assumed to be CCS-packed (see dft for details). +@param a first input array. +@param b second input array of the same size and type as src1 . +@param c output array of the same size and type as src1 . +@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that +each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. +@param conjB optional flag that conjugates the second input array before the multiplication (true) +or not (false). +*/ +CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c, + int flags, bool conjB = false); + +/** @brief Returns the optimal DFT size for a given vector size. + +DFT performance is not a monotonic function of a vector size. Therefore, when you calculate +convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to +pad the input data with zeros to get a bit larger array that can be transformed much faster than the +original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. +Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) +are also processed quite efficiently. + +The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize +so that the DFT of a vector of size N can be processed efficiently. In the current implementation N += 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. + +The function returns a negative number if vecsize is too large (very close to INT_MAX ). + +While the function cannot be used directly to estimate the optimal vector size for DCT transform +(since the current DCT implementation supports only even-size vectors), it can be easily processed +as getOptimalDFTSize((vecsize+1)/2)\*2. +@param vecsize vector size. +@sa dft , dct , idft , idct , mulSpectrums +*/ +CV_EXPORTS_W int getOptimalDFTSize(int vecsize); + +/** @brief Returns the default random number generator. + +The function cv::theRNG returns the default random number generator. For each thread, there is a +separate random number generator, so you can use the function safely in multi-thread environments. +If you just need to get a single random number using this generator or initialize an array, you can +use randu or randn instead. But if you are going to generate many random numbers inside a loop, it +is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() . +@sa RNG, randu, randn +*/ +CV_EXPORTS RNG& theRNG(); + +/** @brief Sets state of default random number generator. + +The function cv::setRNGSeed sets state of default random number generator to custom value. +@param seed new state for default random number generator +@sa RNG, randu, randn +*/ +CV_EXPORTS_W void setRNGSeed(int seed); + +/** @brief Generates a single uniformly-distributed random number or an array of random numbers. + +Non-template variant of the function fills the matrix dst with uniformly-distributed +random numbers from the specified range: +\f[\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\f] +@param dst output array of random numbers; the array must be pre-allocated. +@param low inclusive lower boundary of the generated random numbers. +@param high exclusive upper boundary of the generated random numbers. +@sa RNG, randn, theRNG +*/ +CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high); + +/** @brief Fills the array with normally distributed random numbers. + +The function cv::randn fills the matrix dst with normally distributed random numbers with the specified +mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the +value range of the output array data type. +@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels. +@param mean mean value (expectation) of the generated random numbers. +@param stddev standard deviation of the generated random numbers; it can be either a vector (in +which case a diagonal standard deviation matrix is assumed) or a square matrix. +@sa RNG, randu +*/ +CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev); + +/** @brief Shuffles the array elements randomly. + +The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and +swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor . +@param dst input/output numerical 1D array. +@param iterFactor scale factor that determines the number of random swap operations (see the details +below). +@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used +instead. +@sa RNG, sort +*/ +CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0); + +/** @brief Principal Component Analysis + +The class is used to calculate a special basis for a set of vectors. The +basis will consist of eigenvectors of the covariance matrix calculated +from the input set of vectors. The class %PCA can also transform +vectors to/from the new coordinate space defined by the basis. Usually, +in this new coordinate system, each vector from the original set (and +any linear combination of such vectors) can be quite accurately +approximated by taking its first few components, corresponding to the +eigenvectors of the largest eigenvalues of the covariance matrix. +Geometrically it means that you calculate a projection of the vector to +a subspace formed by a few eigenvectors corresponding to the dominant +eigenvalues of the covariance matrix. And usually such a projection is +very close to the original vector. So, you can represent the original +vector from a high-dimensional space with a much shorter vector +consisting of the projected vector's coordinates in the subspace. Such a +transformation is also known as Karhunen-Loeve Transform, or KLT. +See http://en.wikipedia.org/wiki/Principal_component_analysis + +The sample below is the function that takes two matrices. The first +function stores a set of vectors (a row per vector) that is used to +calculate PCA. The second function stores another "test" set of vectors +(a row per vector). First, these vectors are compressed with PCA, then +reconstructed back, and then the reconstruction error norm is computed +and printed for each vector. : + +@code{.cpp} +using namespace cv; + +PCA compressPCA(const Mat& pcaset, int maxComponents, + const Mat& testset, Mat& compressed) +{ + PCA pca(pcaset, // pass the data + Mat(), // we do not have a pre-computed mean vector, + // so let the PCA engine to compute it + PCA::DATA_AS_ROW, // indicate that the vectors + // are stored as matrix rows + // (use PCA::DATA_AS_COL if the vectors are + // the matrix columns) + maxComponents // specify, how many principal components to retain + ); + // if there is no test data, just return the computed basis, ready-to-use + if( !testset.data ) + return pca; + CV_Assert( testset.cols == pcaset.cols ); + + compressed.create(testset.rows, maxComponents, testset.type()); + + Mat reconstructed; + for( int i = 0; i < testset.rows; i++ ) + { + Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; + // compress the vector, the result will be stored + // in the i-th row of the output matrix + pca.project(vec, coeffs); + // and then reconstruct it + pca.backProject(coeffs, reconstructed); + // and measure the error + printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); + } + return pca; +} +@endcode +@sa calcCovarMatrix, mulTransposed, SVD, dft, dct +*/ +class CV_EXPORTS PCA +{ +public: + enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows + DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns + USE_AVG = 2 //! + }; + + /** @brief default constructor + + The default constructor initializes an empty %PCA structure. The other + constructors initialize the structure and call PCA::operator()(). + */ + PCA(); + + /** @overload + @param data input samples stored as matrix rows or matrix columns. + @param mean optional mean value; if the matrix is empty (@c noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout (PCA::Flags) + @param maxComponents maximum number of components that %PCA should + retain; by default, all the components are retained. + */ + PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0); + + /** @overload + @param data input samples stored as matrix rows or matrix columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout (PCA::Flags) + @param retainedVariance Percentage of variance that PCA should retain. + Using this parameter will let the PCA decided how many components to + retain but it will always keep at least 2. + */ + PCA(InputArray data, InputArray mean, int flags, double retainedVariance); + + /** @brief performs %PCA + + The operator performs %PCA of the supplied dataset. It is safe to reuse + the same PCA structure for multiple datasets. That is, if the structure + has been previously used with another dataset, the existing internal + data is reclaimed and the new @ref eigenvalues, @ref eigenvectors and @ref + mean are allocated and computed. + + The computed @ref eigenvalues are sorted from the largest to the smallest and + the corresponding @ref eigenvectors are stored as eigenvectors rows. + + @param data input samples stored as the matrix rows or as the matrix + columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout. (Flags) + @param maxComponents maximum number of components that PCA should + retain; by default, all the components are retained. + */ + PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0); + + /** @overload + @param data input samples stored as the matrix rows or as the matrix + columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout. (PCA::Flags) + @param retainedVariance Percentage of variance that %PCA should retain. + Using this parameter will let the %PCA decided how many components to + retain but it will always keep at least 2. + */ + PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance); + + /** @brief Projects vector(s) to the principal component subspace. + + The methods project one or more vectors to the principal component + subspace, where each vector projection is represented by coefficients in + the principal component basis. The first form of the method returns the + matrix that the second form writes to the result. So the first form can + be used as a part of expression while the second form can be more + efficient in a processing loop. + @param vec input vector(s); must have the same dimensionality and the + same layout as the input data used at %PCA phase, that is, if + DATA_AS_ROW are specified, then `vec.cols==data.cols` + (vector dimensionality) and `vec.rows` is the number of vectors to + project, and the same is true for the PCA::DATA_AS_COL case. + */ + Mat project(InputArray vec) const; + + /** @overload + @param vec input vector(s); must have the same dimensionality and the + same layout as the input data used at PCA phase, that is, if + DATA_AS_ROW are specified, then `vec.cols==data.cols` + (vector dimensionality) and `vec.rows` is the number of vectors to + project, and the same is true for the PCA::DATA_AS_COL case. + @param result output vectors; in case of PCA::DATA_AS_COL, the + output matrix has as many columns as the number of input vectors, this + means that `result.cols==vec.cols` and the number of rows match the + number of principal components (for example, `maxComponents` parameter + passed to the constructor). + */ + void project(InputArray vec, OutputArray result) const; + + /** @brief Reconstructs vectors from their PC projections. + + The methods are inverse operations to PCA::project. They take PC + coordinates of projected vectors and reconstruct the original vectors. + Unless all the principal components have been retained, the + reconstructed vectors are different from the originals. But typically, + the difference is small if the number of components is large enough (but + still much smaller than the original vector dimensionality). As a + result, PCA is used. + @param vec coordinates of the vectors in the principal component + subspace, the layout and size are the same as of PCA::project output + vectors. + */ + Mat backProject(InputArray vec) const; + + /** @overload + @param vec coordinates of the vectors in the principal component + subspace, the layout and size are the same as of PCA::project output + vectors. + @param result reconstructed vectors; the layout and size are the same as + of PCA::project input vectors. + */ + void backProject(InputArray vec, OutputArray result) const; + + /** @brief write PCA objects + + Writes @ref eigenvalues @ref eigenvectors and @ref mean to specified FileStorage + */ + void write(FileStorage& fs) const; + + /** @brief load PCA objects + + Loads @ref eigenvalues @ref eigenvectors and @ref mean from specified FileNode + */ + void read(const FileNode& fn); + + Mat eigenvectors; //!< eigenvectors of the covariation matrix + Mat eigenvalues; //!< eigenvalues of the covariation matrix + Mat mean; //!< mean value subtracted before the projection and added after the back projection +}; + +/** @example pca.cpp + An example using %PCA for dimensionality reduction while maintaining an amount of variance + */ + +/** + @brief Linear Discriminant Analysis + @todo document this class + */ +class CV_EXPORTS LDA +{ +public: + /** @brief constructor + Initializes a LDA with num_components (default 0). + */ + explicit LDA(int num_components = 0); + + /** Initializes and performs a Discriminant Analysis with Fisher's + Optimization Criterion on given data in src and corresponding labels + in labels. If 0 (or less) number of components are given, they are + automatically determined for given data in computation. + */ + LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0); + + /** Serializes this object to a given filename. + */ + void save(const String& filename) const; + + /** Deserializes this object from a given filename. + */ + void load(const String& filename); + + /** Serializes this object to a given cv::FileStorage. + */ + void save(FileStorage& fs) const; + + /** Deserializes this object from a given cv::FileStorage. + */ + void load(const FileStorage& node); + + /** destructor + */ + ~LDA(); + + /** Compute the discriminants for data in src (row aligned) and labels. + */ + void compute(InputArrayOfArrays src, InputArray labels); + + /** Projects samples into the LDA subspace. + src may be one or more row aligned samples. + */ + Mat project(InputArray src); + + /** Reconstructs projections from the LDA subspace. + src may be one or more row aligned projections. + */ + Mat reconstruct(InputArray src); + + /** Returns the eigenvectors of this LDA. + */ + Mat eigenvectors() const { return _eigenvectors; } + + /** Returns the eigenvalues of this LDA. + */ + Mat eigenvalues() const { return _eigenvalues; } + + static Mat subspaceProject(InputArray W, InputArray mean, InputArray src); + static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src); + +protected: + bool _dataAsRow; // unused, but needed for 3.0 ABI compatibility. + int _num_components; + Mat _eigenvectors; + Mat _eigenvalues; + void lda(InputArrayOfArrays src, InputArray labels); +}; + +/** @brief Singular Value Decomposition + +Class for computing Singular Value Decomposition of a floating-point +matrix. The Singular Value Decomposition is used to solve least-square +problems, under-determined linear systems, invert matrices, compute +condition numbers, and so on. + +If you want to compute a condition number of a matrix or an absolute value of +its determinant, you do not need `u` and `vt`. You can pass +flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u +and vt must be computed, which is not necessary most of the time. + +@sa invert, solve, eigen, determinant +*/ +class CV_EXPORTS SVD +{ +public: + enum Flags { + /** allow the algorithm to modify the decomposed matrix; it can save space and speed up + processing. currently ignored. */ + MODIFY_A = 1, + /** indicates that only a vector of singular values `w` is to be processed, while u and vt + will be set to empty matrices */ + NO_UV = 2, + /** when the matrix is not square, by default the algorithm produces u and vt matrices of + sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is + specified, u and vt will be full-size square orthogonal matrices.*/ + FULL_UV = 4 + }; + + /** @brief the default constructor + + initializes an empty SVD structure + */ + SVD(); + + /** @overload + initializes an empty SVD structure and then calls SVD::operator() + @param src decomposed matrix. + @param flags operation flags (SVD::Flags) + */ + SVD( InputArray src, int flags = 0 ); + + /** @brief the operator that performs SVD. The previously allocated u, w and vt are released. + + The operator performs the singular value decomposition of the supplied + matrix. The u,`vt` , and the vector of singular values w are stored in + the structure. The same SVD structure can be reused many times with + different matrices. Each time, if needed, the previous u,`vt` , and w + are reclaimed and the new matrices are created, which is all handled by + Mat::create. + @param src decomposed matrix. + @param flags operation flags (SVD::Flags) + */ + SVD& operator ()( InputArray src, int flags = 0 ); + + /** @brief decomposes matrix and stores the results to user-provided matrices + + The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor + and SVD::operator(), they store the results to the user-provided + matrices: + + @code{.cpp} + Mat A, w, u, vt; + SVD::compute(A, w, u, vt); + @endcode + + @param src decomposed matrix + @param w calculated singular values + @param u calculated left singular vectors + @param vt transposed matrix of right singular values + @param flags operation flags - see SVD::SVD. + */ + static void compute( InputArray src, OutputArray w, + OutputArray u, OutputArray vt, int flags = 0 ); + + /** @overload + computes singular values of a matrix + @param src decomposed matrix + @param w calculated singular values + @param flags operation flags - see SVD::Flags. + */ + static void compute( InputArray src, OutputArray w, int flags = 0 ); + + /** @brief performs back substitution + */ + static void backSubst( InputArray w, InputArray u, + InputArray vt, InputArray rhs, + OutputArray dst ); + + /** @brief solves an under-determined singular linear system + + The method finds a unit-length solution x of a singular linear system + A\*x = 0. Depending on the rank of A, there can be no solutions, a + single solution or an infinite number of solutions. In general, the + algorithm solves the following problem: + \f[dst = \arg \min _{x: \| x \| =1} \| src \cdot x \|\f] + @param src left-hand-side matrix. + @param dst found solution. + */ + static void solveZ( InputArray src, OutputArray dst ); + + /** @brief performs a singular value back substitution. + + The method calculates a back substitution for the specified right-hand + side: + + \f[\texttt{x} = \texttt{vt} ^T \cdot diag( \texttt{w} )^{-1} \cdot \texttt{u} ^T \cdot \texttt{rhs} \sim \texttt{A} ^{-1} \cdot \texttt{rhs}\f] + + Using this technique you can either get a very accurate solution of the + convenient linear system, or the best (in the least-squares terms) + pseudo-solution of an overdetermined linear system. + + @param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to + be solved, where A has been previously decomposed. + + @param dst found solution of the system. + + @note Explicit SVD with the further back substitution only makes sense + if you need to solve many linear systems with the same left-hand side + (for example, src ). If all you need is to solve a single system + (possibly with multiple rhs immediately available), simply call solve + add pass DECOMP_SVD there. It does absolutely the same thing. + */ + void backSubst( InputArray rhs, OutputArray dst ) const; + + /** @todo document */ + template static + void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ); + + /** @todo document */ + template static + void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ); + + /** @todo document */ + template static + void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst ); + + Mat u, w, vt; +}; + +/** @brief Random Number Generator + +Random number generator. It encapsulates the state (currently, a 64-bit +integer) and has methods to return scalar random values and to fill +arrays with random values. Currently it supports uniform and Gaussian +(normal) distributions. The generator uses Multiply-With-Carry +algorithm, introduced by G. Marsaglia ( + ). +Gaussian-distribution random numbers are generated using the Ziggurat +algorithm ( ), +introduced by G. Marsaglia and W. W. Tsang. +*/ +class CV_EXPORTS RNG +{ +public: + enum { UNIFORM = 0, + NORMAL = 1 + }; + + /** @brief constructor + + These are the RNG constructors. The first form sets the state to some + pre-defined value, equal to 2\*\*32-1 in the current implementation. The + second form sets the state to the specified value. If you passed state=0 + , the constructor uses the above default value instead to avoid the + singular random number sequence, consisting of all zeros. + */ + RNG(); + /** @overload + @param state 64-bit value used to initialize the RNG. + */ + RNG(uint64 state); + /**The method updates the state using the MWC algorithm and returns the + next 32-bit random number.*/ + unsigned next(); + + /**Each of the methods updates the state using the MWC algorithm and + returns the next random number of the specified type. In case of integer + types, the returned number is from the available value range for the + specified type. In case of floating-point types, the returned value is + from [0,1) range. + */ + operator uchar(); + /** @overload */ + operator schar(); + /** @overload */ + operator ushort(); + /** @overload */ + operator short(); + /** @overload */ + operator unsigned(); + /** @overload */ + operator int(); + /** @overload */ + operator float(); + /** @overload */ + operator double(); + + /** @brief returns a random integer sampled uniformly from [0, N). + + The methods transform the state using the MWC algorithm and return the + next random number. The first form is equivalent to RNG::next . The + second form returns the random number modulo N , which means that the + result is in the range [0, N) . + */ + unsigned operator ()(); + /** @overload + @param N upper non-inclusive boundary of the returned random number. + */ + unsigned operator ()(unsigned N); + + /** @brief returns uniformly distributed integer random number from [a,b) range + + The methods transform the state using the MWC algorithm and return the + next uniformly-distributed random number of the specified type, deduced + from the input parameter type, from the range [a, b) . There is a nuance + illustrated by the following sample: + + @code{.cpp} + RNG rng; + + // always produces 0 + double a = rng.uniform(0, 1); + + // produces double from [0, 1) + double a1 = rng.uniform((double)0, (double)1); + + // produces float from [0, 1) + double b = rng.uniform(0.f, 1.f); + + // produces double from [0, 1) + double c = rng.uniform(0., 1.); + + // may cause compiler error because of ambiguity: + // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? + double d = rng.uniform(0, 0.999999); + @endcode + + The compiler does not take into account the type of the variable to + which you assign the result of RNG::uniform . The only thing that + matters to the compiler is the type of a and b parameters. So, if you + want a floating-point random number, but the range boundaries are + integer numbers, either put dots in the end, if they are constants, or + use explicit type cast operators, as in the a1 initialization above. + @param a lower inclusive boundary of the returned random numbers. + @param b upper non-inclusive boundary of the returned random numbers. + */ + int uniform(int a, int b); + /** @overload */ + float uniform(float a, float b); + /** @overload */ + double uniform(double a, double b); + + /** @brief Fills arrays with random numbers. + + @param mat 2D or N-dimensional matrix; currently matrices with more than + 4 channels are not supported by the methods, use Mat::reshape as a + possible workaround. + @param distType distribution type, RNG::UNIFORM or RNG::NORMAL. + @param a first distribution parameter; in case of the uniform + distribution, this is an inclusive lower boundary, in case of the normal + distribution, this is a mean value. + @param b second distribution parameter; in case of the uniform + distribution, this is a non-inclusive upper boundary, in case of the + normal distribution, this is a standard deviation (diagonal of the + standard deviation matrix or the full standard deviation matrix). + @param saturateRange pre-saturation flag; for uniform distribution only; + if true, the method will first convert a and b to the acceptable value + range (according to the mat datatype) and then will generate uniformly + distributed random numbers within the range [saturate(a), saturate(b)), + if saturateRange=false, the method will generate uniformly distributed + random numbers in the original range [a, b) and then will saturate them, + it means, for example, that + theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely + produce array mostly filled with 0's and 255's, since the range (0, 255) + is significantly smaller than [-DBL_MAX, DBL_MAX). + + Each of the methods fills the matrix with the random values from the + specified distribution. As the new numbers are generated, the RNG state + is updated accordingly. In case of multiple-channel images, every + channel is filled independently, which means that RNG cannot generate + samples from the multi-dimensional Gaussian distribution with + non-diagonal covariance matrix directly. To do that, the method + generates samples from multi-dimensional standard Gaussian distribution + with zero mean and identity covariation matrix, and then transforms them + using transform to get samples from the specified Gaussian distribution. + */ + void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false ); + + /** @brief Returns the next random number sampled from the Gaussian distribution + @param sigma standard deviation of the distribution. + + The method transforms the state using the MWC algorithm and returns the + next random number from the Gaussian distribution N(0,sigma) . That is, + the mean value of the returned random numbers is zero and the standard + deviation is the specified sigma . + */ + double gaussian(double sigma); + + uint64 state; +}; + +/** @brief Mersenne Twister random number generator + +Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c +@todo document + */ +class CV_EXPORTS RNG_MT19937 +{ +public: + RNG_MT19937(); + RNG_MT19937(unsigned s); + void seed(unsigned s); + + unsigned next(); + + operator int(); + operator unsigned(); + operator float(); + operator double(); + + unsigned operator ()(unsigned N); + unsigned operator ()(); + + /** @brief returns uniformly distributed integer random number from [a,b) range + +*/ + int uniform(int a, int b); + /** @brief returns uniformly distributed floating-point random number from [a,b) range + +*/ + float uniform(float a, float b); + /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range + +*/ + double uniform(double a, double b); + +private: + enum PeriodParameters {N = 624, M = 397}; + unsigned state[N]; + int mti; +}; + +//! @} core_array + +//! @addtogroup core_cluster +//! @{ + +/** @example kmeans.cpp + An example on K-means clustering +*/ + +/** @brief Finds centers of clusters and groups input samples around the clusters. + +The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters +and groups the input samples around the clusters. As an output, \f$\texttt{labels}_i\f$ contains a +0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix. + +@note +- (Python) An example on K-means clustering can be found at + opencv_source_code/samples/python/kmeans.py +@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed. +Examples of this array can be: +- Mat points(count, 2, CV_32F); +- Mat points(count, 1, CV_32FC2); +- Mat points(1, count, CV_32FC2); +- std::vector\ points(sampleCount); +@param K Number of clusters to split the set by. +@param bestLabels Input/output integer array that stores the cluster indices for every sample. +@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or +the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster +centers moves by less than criteria.epsilon on some iteration, the algorithm stops. +@param attempts Flag to specify the number of times the algorithm is executed using different +initial labellings. The algorithm returns the labels that yield the best compactness (see the last +function parameter). +@param flags Flag that can take values of cv::KmeansFlags +@param centers Output matrix of the cluster centers, one row per each cluster center. +@return The function returns the compactness measure that is computed as +\f[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\f] +after every attempt. The best (minimum) value is chosen and the corresponding labels and the +compactness value are returned by the function. Basically, you can use only the core of the +function, set the number of attempts to 1, initialize labels each time using a custom algorithm, +pass them with the ( flags = KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best +(most-compact) clustering. +*/ +CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels, + TermCriteria criteria, int attempts, + int flags, OutputArray centers = noArray() ); + +//! @} core_cluster + +//! @addtogroup core_basic +//! @{ + +/////////////////////////////// Formatted output of cv::Mat /////////////////////////// + +/** @todo document */ +class CV_EXPORTS Formatted +{ +public: + virtual const char* next() = 0; + virtual void reset() = 0; + virtual ~Formatted(); +}; + +/** @todo document */ +class CV_EXPORTS Formatter +{ +public: + enum { FMT_DEFAULT = 0, + FMT_MATLAB = 1, + FMT_CSV = 2, + FMT_PYTHON = 3, + FMT_NUMPY = 4, + FMT_C = 5 + }; + + virtual ~Formatter(); + + virtual Ptr format(const Mat& mtx) const = 0; + + virtual void set32fPrecision(int p = 8) = 0; + virtual void set64fPrecision(int p = 16) = 0; + virtual void setMultiline(bool ml = true) = 0; + + static Ptr get(int fmt = FMT_DEFAULT); + +}; + +static inline +String& operator << (String& out, Ptr fmtd) +{ + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + out += cv::String(str); + return out; +} + +static inline +String& operator << (String& out, const Mat& mtx) +{ + return out << Formatter::get()->format(mtx); +} + +//////////////////////////////////////// Algorithm //////////////////////////////////// + +class CV_EXPORTS Algorithm; + +template struct ParamType {}; + + +/** @brief This is a base class for all more or less complex algorithms in OpenCV + +especially for classes of algorithms, for which there can be multiple implementations. The examples +are stereo correspondence (for which there are algorithms like block matching, semi-global block +matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians +models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck +etc.). + +Here is example of SIFT use in your application via Algorithm interface: +@code + #include "opencv2/opencv.hpp" + #include "opencv2/xfeatures2d.hpp" + using namespace cv::xfeatures2d; + + Ptr sift = SIFT::create(); + FileStorage fs("sift_params.xml", FileStorage::READ); + if( fs.isOpened() ) // if we have file with parameters, read them + { + sift->read(fs["sift_params"]); + fs.release(); + } + else // else modify the parameters and store them; user can later edit the file to use different parameters + { + sift->setContrastThreshold(0.01f); // lower the contrast threshold, compared to the default value + { + WriteStructContext ws(fs, "sift_params", CV_NODE_MAP); + sift->write(fs); + } + } + Mat image = imread("myimage.png", 0), descriptors; + vector keypoints; + sift->detectAndCompute(image, noArray(), keypoints, descriptors); +@endcode + */ +class CV_EXPORTS_W Algorithm +{ +public: + Algorithm(); + virtual ~Algorithm(); + + /** @brief Clears the algorithm state + */ + CV_WRAP virtual void clear() {} + + /** @brief Stores algorithm parameters in a file storage + */ + virtual void write(FileStorage& fs) const { (void)fs; } + + /** @brief Reads algorithm parameters from a file storage + */ + virtual void read(const FileNode& fn) { (void)fn; } + + /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read + */ + virtual bool empty() const { return false; } + + /** @brief Reads algorithm from the file node + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + cv::FileStorage fsRead("example.xml", FileStorage::READ); + Ptr svm = Algorithm::read(fsRead.root()); + @endcode + In order to make this method work, the derived class must overwrite Algorithm::read(const + FileNode& fn) and also have static create() method without parameters + (or with all the optional parameters) + */ + template static Ptr<_Tp> read(const FileNode& fn) + { + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** @brief Loads algorithm from the file + + @param filename Name of the file to read. + @param objname The optional name of the node to read (if empty, the first top-level node will be used) + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + Ptr svm = Algorithm::load("my_svm_model.xml"); + @endcode + In order to make this method work, the derived class must overwrite Algorithm::read(const + FileNode& fn). + */ + template static Ptr<_Tp> load(const String& filename, const String& objname=String()) + { + FileStorage fs(filename, FileStorage::READ); + FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; + if (fn.empty()) return Ptr<_Tp>(); + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** @brief Loads algorithm from a String + + @param strModel The string variable containing the model you want to load. + @param objname The optional name of the node to read (if empty, the first top-level node will be used) + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + Ptr svm = Algorithm::loadFromString(myStringModel); + @endcode + */ + template static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String()) + { + FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY); + FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** Saves the algorithm to a file. + In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */ + CV_WRAP virtual void save(const String& filename) const; + + /** Returns the algorithm string identifier. + This string is used as top level xml/yml node tag when the object is saved to a file or string. */ + CV_WRAP virtual String getDefaultName() const; + +protected: + void writeFormat(FileStorage& fs) const; +}; + +struct Param { + enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7, + UNSIGNED_INT=8, UINT64=9, UCHAR=11 }; +}; + + + +template<> struct ParamType +{ + typedef bool const_param_type; + typedef bool member_type; + + enum { type = Param::BOOLEAN }; +}; + +template<> struct ParamType +{ + typedef int const_param_type; + typedef int member_type; + + enum { type = Param::INT }; +}; + +template<> struct ParamType +{ + typedef double const_param_type; + typedef double member_type; + + enum { type = Param::REAL }; +}; + +template<> struct ParamType +{ + typedef const String& const_param_type; + typedef String member_type; + + enum { type = Param::STRING }; +}; + +template<> struct ParamType +{ + typedef const Mat& const_param_type; + typedef Mat member_type; + + enum { type = Param::MAT }; +}; + +template<> struct ParamType > +{ + typedef const std::vector& const_param_type; + typedef std::vector member_type; + + enum { type = Param::MAT_VECTOR }; +}; + +template<> struct ParamType +{ + typedef const Ptr& const_param_type; + typedef Ptr member_type; + + enum { type = Param::ALGORITHM }; +}; + +template<> struct ParamType +{ + typedef float const_param_type; + typedef float member_type; + + enum { type = Param::FLOAT }; +}; + +template<> struct ParamType +{ + typedef unsigned const_param_type; + typedef unsigned member_type; + + enum { type = Param::UNSIGNED_INT }; +}; + +template<> struct ParamType +{ + typedef uint64 const_param_type; + typedef uint64 member_type; + + enum { type = Param::UINT64 }; +}; + +template<> struct ParamType +{ + typedef uchar const_param_type; + typedef uchar member_type; + + enum { type = Param::UCHAR }; +}; + +//! @} core_basic + +} //namespace cv + +#include "opencv2/core/operations.hpp" +#include "opencv2/core/cvstd.inl.hpp" +#include "opencv2/core/utility.hpp" +#include "opencv2/core/optim.hpp" +#include "opencv2/core/ovx.hpp" + +#endif /*OPENCV_CORE_HPP*/ diff --git a/thirdparty/linux/include/opencv2/core/affine.hpp b/thirdparty/linux/include/opencv2/core/affine.hpp new file mode 100644 index 0000000..311ff62 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/affine.hpp @@ -0,0 +1,517 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_AFFINE3_HPP +#define OPENCV_CORE_AFFINE3_HPP + +#ifdef __cplusplus + +#include + +namespace cv +{ + +//! @addtogroup core +//! @{ + + /** @brief Affine transform + @todo document + */ + template + class Affine3 + { + public: + typedef T float_type; + typedef Matx Mat3; + typedef Matx Mat4; + typedef Vec Vec3; + + Affine3(); + + //! Augmented affine matrix + Affine3(const Mat4& affine); + + //! Rotation matrix + Affine3(const Mat3& R, const Vec3& t = Vec3::all(0)); + + //! Rodrigues vector + Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0)); + + //! Combines all contructors above. Supports 4x4, 4x3, 3x3, 1x3, 3x1 sizes of data matrix + explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0)); + + //! From 16th element array + explicit Affine3(const float_type* vals); + + //! Create identity transform + static Affine3 Identity(); + + //! Rotation matrix + void rotation(const Mat3& R); + + //! Rodrigues vector + void rotation(const Vec3& rvec); + + //! Combines rotation methods above. Suports 3x3, 1x3, 3x1 sizes of data matrix; + void rotation(const Mat& data); + + void linear(const Mat3& L); + void translation(const Vec3& t); + + Mat3 rotation() const; + Mat3 linear() const; + Vec3 translation() const; + + //! Rodrigues vector + Vec3 rvec() const; + + Affine3 inv(int method = cv::DECOMP_SVD) const; + + //! a.rotate(R) is equivalent to Affine(R, 0) * a; + Affine3 rotate(const Mat3& R) const; + + //! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a; + Affine3 rotate(const Vec3& rvec) const; + + //! a.translate(t) is equivalent to Affine(E, t) * a; + Affine3 translate(const Vec3& t) const; + + //! a.concatenate(affine) is equivalent to affine * a; + Affine3 concatenate(const Affine3& affine) const; + + template operator Affine3() const; + + template Affine3 cast() const; + + Mat4 matrix; + +#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H + Affine3(const Eigen::Transform& affine); + Affine3(const Eigen::Transform& affine); + operator Eigen::Transform() const; + operator Eigen::Transform() const; +#endif + }; + + template static + Affine3 operator*(const Affine3& affine1, const Affine3& affine2); + + template static + V operator*(const Affine3& affine, const V& vector); + + typedef Affine3 Affine3f; + typedef Affine3 Affine3d; + + static Vec3f operator*(const Affine3f& affine, const Vec3f& vector); + static Vec3d operator*(const Affine3d& affine, const Vec3d& vector); + + template class DataType< Affine3<_Tp> > + { + public: + typedef Affine3<_Tp> value_type; + typedef Affine3::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 16, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; + }; + +//! @} core + +} + +//! @cond IGNORED + +/////////////////////////////////////////////////////////////////////////////////// +// Implementaiton + +template inline +cv::Affine3::Affine3() + : matrix(Mat4::eye()) +{} + +template inline +cv::Affine3::Affine3(const Mat4& affine) + : matrix(affine) +{} + +template inline +cv::Affine3::Affine3(const Mat3& R, const Vec3& t) +{ + rotation(R); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const Vec3& _rvec, const Vec3& t) +{ + rotation(_rvec); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const cv::Mat& data, const Vec3& t) +{ + CV_Assert(data.type() == cv::DataType::type); + + if (data.cols == 4 && data.rows == 4) + { + data.copyTo(matrix); + return; + } + else if (data.cols == 4 && data.rows == 3) + { + rotation(data(Rect(0, 0, 3, 3))); + translation(data(Rect(3, 0, 1, 3))); + return; + } + + rotation(data); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const float_type* vals) : matrix(vals) +{} + +template inline +cv::Affine3 cv::Affine3::Identity() +{ + return Affine3(cv::Affine3::Mat4::eye()); +} + +template inline +void cv::Affine3::rotation(const Mat3& R) +{ + linear(R); +} + +template inline +void cv::Affine3::rotation(const Vec3& _rvec) +{ + double theta = norm(_rvec); + + if (theta < DBL_EPSILON) + rotation(Mat3::eye()); + else + { + double c = std::cos(theta); + double s = std::sin(theta); + double c1 = 1. - c; + double itheta = (theta != 0) ? 1./theta : 0.; + + Point3_ r = _rvec*itheta; + + Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z ); + Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 ); + + // R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x] + // where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0] + Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x; + + rotation(R); + } +} + +//Combines rotation methods above. Suports 3x3, 1x3, 3x1 sizes of data matrix; +template inline +void cv::Affine3::rotation(const cv::Mat& data) +{ + CV_Assert(data.type() == cv::DataType::type); + + if (data.cols == 3 && data.rows == 3) + { + Mat3 R; + data.copyTo(R); + rotation(R); + } + else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3)) + { + Vec3 _rvec; + data.reshape(1, 3).copyTo(_rvec); + rotation(_rvec); + } + else + CV_Assert(!"Input marix can be 3x3, 1x3 or 3x1"); +} + +template inline +void cv::Affine3::linear(const Mat3& L) +{ + matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1]; matrix.val[ 2] = L.val[2]; + matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4]; matrix.val[ 6] = L.val[5]; + matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7]; matrix.val[10] = L.val[8]; +} + +template inline +void cv::Affine3::translation(const Vec3& t) +{ + matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2]; +} + +template inline +typename cv::Affine3::Mat3 cv::Affine3::rotation() const +{ + return linear(); +} + +template inline +typename cv::Affine3::Mat3 cv::Affine3::linear() const +{ + typename cv::Affine3::Mat3 R; + R.val[0] = matrix.val[0]; R.val[1] = matrix.val[1]; R.val[2] = matrix.val[ 2]; + R.val[3] = matrix.val[4]; R.val[4] = matrix.val[5]; R.val[5] = matrix.val[ 6]; + R.val[6] = matrix.val[8]; R.val[7] = matrix.val[9]; R.val[8] = matrix.val[10]; + return R; +} + +template inline +typename cv::Affine3::Vec3 cv::Affine3::translation() const +{ + return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]); +} + +template inline +typename cv::Affine3::Vec3 cv::Affine3::rvec() const +{ + cv::Vec3d w; + cv::Matx33d u, vt, R = rotation(); + cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A); + R = u * vt; + + double rx = R.val[7] - R.val[5]; + double ry = R.val[2] - R.val[6]; + double rz = R.val[3] - R.val[1]; + + double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25); + double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5; + c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c; + double theta = acos(c); + + if( s < 1e-5 ) + { + if( c > 0 ) + rx = ry = rz = 0; + else + { + double t; + t = (R.val[0] + 1) * 0.5; + rx = std::sqrt(std::max(t, 0.0)); + t = (R.val[4] + 1) * 0.5; + ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0); + t = (R.val[8] + 1) * 0.5; + rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0); + + if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) ) + rz = -rz; + theta /= std::sqrt(rx*rx + ry*ry + rz*rz); + rx *= theta; + ry *= theta; + rz *= theta; + } + } + else + { + double vth = 1/(2*s); + vth *= theta; + rx *= vth; ry *= vth; rz *= vth; + } + + return cv::Vec3d(rx, ry, rz); +} + +template inline +cv::Affine3 cv::Affine3::inv(int method) const +{ + return matrix.inv(method); +} + +template inline +cv::Affine3 cv::Affine3::rotate(const Mat3& R) const +{ + Mat3 Lc = linear(); + Vec3 tc = translation(); + Mat4 result; + result.val[12] = result.val[13] = result.val[14] = 0; + result.val[15] = 1; + + for(int j = 0; j < 3; ++j) + { + for(int i = 0; i < 3; ++i) + { + float_type value = 0; + for(int k = 0; k < 3; ++k) + value += R(j, k) * Lc(k, i); + result(j, i) = value; + } + + result(j, 3) = R.row(j).dot(tc.t()); + } + return result; +} + +template inline +cv::Affine3 cv::Affine3::rotate(const Vec3& _rvec) const +{ + return rotate(Affine3f(_rvec).rotation()); +} + +template inline +cv::Affine3 cv::Affine3::translate(const Vec3& t) const +{ + Mat4 m = matrix; + m.val[ 3] += t[0]; + m.val[ 7] += t[1]; + m.val[11] += t[2]; + return m; +} + +template inline +cv::Affine3 cv::Affine3::concatenate(const Affine3& affine) const +{ + return (*this).rotate(affine.rotation()).translate(affine.translation()); +} + +template template inline +cv::Affine3::operator Affine3() const +{ + return Affine3(matrix); +} + +template template inline +cv::Affine3 cv::Affine3::cast() const +{ + return Affine3(matrix); +} + +template inline +cv::Affine3 cv::operator*(const cv::Affine3& affine1, const cv::Affine3& affine2) +{ + return affine2.concatenate(affine1); +} + +template inline +V cv::operator*(const cv::Affine3& affine, const V& v) +{ + const typename Affine3::Mat4& m = affine.matrix; + + V r; + r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3]; + r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7]; + r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11]; + return r; +} + +static inline +cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v) +{ + const cv::Matx44f& m = affine.matrix; + cv::Vec3f r; + r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; + r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; + r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; + return r; +} + +static inline +cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v) +{ + const cv::Matx44d& m = affine.matrix; + cv::Vec3d r; + r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; + r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; + r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; + return r; +} + + + +#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H + +template inline +cv::Affine3::Affine3(const Eigen::Transform& affine) +{ + cv::Mat(4, 4, cv::DataType::type, affine.matrix().data()).copyTo(matrix); +} + +template inline +cv::Affine3::Affine3(const Eigen::Transform& affine) +{ + Eigen::Transform a = affine; + cv::Mat(4, 4, cv::DataType::type, a.matrix().data()).copyTo(matrix); +} + +template inline +cv::Affine3::operator Eigen::Transform() const +{ + Eigen::Transform r; + cv::Mat hdr(4, 4, cv::DataType::type, r.matrix().data()); + cv::Mat(matrix, false).copyTo(hdr); + return r; +} + +template inline +cv::Affine3::operator Eigen::Transform() const +{ + return this->operator Eigen::Transform(); +} + +#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */ + +//! @endcond + +#endif /* __cplusplus */ + +#endif /* OPENCV_CORE_AFFINE3_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/base.hpp b/thirdparty/linux/include/opencv2/core/base.hpp new file mode 100644 index 0000000..017b484 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/base.hpp @@ -0,0 +1,691 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_BASE_HPP +#define OPENCV_CORE_BASE_HPP + +#ifndef __cplusplus +# error base.hpp header must be compiled as C++ +#endif + +#include "opencv2/opencv_modules.hpp" + +#include +#include + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" + +namespace cv +{ + +//! @addtogroup core_utils +//! @{ + +namespace Error { +//! error codes +enum Code { + StsOk= 0, //!< everithing is ok + StsBackTrace= -1, //!< pseudo error for back trace + StsError= -2, //!< unknown /unspecified error + StsInternal= -3, //!< internal error (bad state) + StsNoMem= -4, //!< insufficient memory + StsBadArg= -5, //!< function arg/param is bad + StsBadFunc= -6, //!< unsupported function + StsNoConv= -7, //!< iter. didn't converge + StsAutoTrace= -8, //!< tracing + HeaderIsNull= -9, //!< image header is NULL + BadImageSize= -10, //!< image size is invalid + BadOffset= -11, //!< offset is invalid + BadDataPtr= -12, //!< + BadStep= -13, //!< + BadModelOrChSeq= -14, //!< + BadNumChannels= -15, //!< + BadNumChannel1U= -16, //!< + BadDepth= -17, //!< + BadAlphaChannel= -18, //!< + BadOrder= -19, //!< + BadOrigin= -20, //!< + BadAlign= -21, //!< + BadCallBack= -22, //!< + BadTileSize= -23, //!< + BadCOI= -24, //!< + BadROISize= -25, //!< + MaskIsTiled= -26, //!< + StsNullPtr= -27, //!< null pointer + StsVecLengthErr= -28, //!< incorrect vector length + StsFilterStructContentErr= -29, //!< incorr. filter structure content + StsKernelStructContentErr= -30, //!< incorr. transform kernel content + StsFilterOffsetErr= -31, //!< incorrect filter ofset value + StsBadSize= -201, //!< the input/output structure size is incorrect + StsDivByZero= -202, //!< division by zero + StsInplaceNotSupported= -203, //!< in-place operation is not supported + StsObjectNotFound= -204, //!< request can't be completed + StsUnmatchedFormats= -205, //!< formats of input/output arrays differ + StsBadFlag= -206, //!< flag is wrong or not supported + StsBadPoint= -207, //!< bad CvPoint + StsBadMask= -208, //!< bad format of mask (neither 8uC1 nor 8sC1) + StsUnmatchedSizes= -209, //!< sizes of input/output structures do not match + StsUnsupportedFormat= -210, //!< the data format/type is not supported by the function + StsOutOfRange= -211, //!< some of parameters are out of range + StsParseError= -212, //!< invalid syntax/structure of the parsed file + StsNotImplemented= -213, //!< the requested function/feature is not implemented + StsBadMemBlock= -214, //!< an allocated block has been corrupted + StsAssert= -215, //!< assertion failed + GpuNotSupported= -216, + GpuApiCallError= -217, + OpenGlNotSupported= -218, + OpenGlApiCallError= -219, + OpenCLApiCallError= -220, + OpenCLDoubleNotSupported= -221, + OpenCLInitError= -222, + OpenCLNoAMDBlasFft= -223 +}; +} //Error + +//! @} core_utils + +//! @addtogroup core_array +//! @{ + +//! matrix decomposition types +enum DecompTypes { + /** Gaussian elimination with the optimal pivot element chosen. */ + DECOMP_LU = 0, + /** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix + src1 can be singular */ + DECOMP_SVD = 1, + /** eigenvalue decomposition; the matrix src1 must be symmetrical */ + DECOMP_EIG = 2, + /** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively + defined */ + DECOMP_CHOLESKY = 3, + /** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */ + DECOMP_QR = 4, + /** while all the previous flags are mutually exclusive, this flag can be used together with + any of the previous; it means that the normal equations + \f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are + solved instead of the original system + \f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */ + DECOMP_NORMAL = 16 +}; + +/** norm types +- For one array: +\f[norm = \forkthree{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } +{ \| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } +{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }\f] + +- Absolute norm for two arrays +\f[norm = \forkthree{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } +{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } +{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }\f] + +- Relative norm for two arrays +\f[norm = \forkthree{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE_INF}\) } +{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE_L1}\) } +{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE_L2}\) }\f] + +As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$. +The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$ +is calculated as follows +\f{align*} + \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ + \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ + \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 +\f} +and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is +\f{align*} + \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ + \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ + \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. +\f} +The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$. +It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$. +![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) + */ +enum NormTypes { NORM_INF = 1, + NORM_L1 = 2, + NORM_L2 = 4, + NORM_L2SQR = 5, + NORM_HAMMING = 6, + NORM_HAMMING2 = 7, + NORM_TYPE_MASK = 7, + NORM_RELATIVE = 8, //!< flag + NORM_MINMAX = 32 //!< flag + }; + +//! comparison types +enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2. + CMP_GT = 1, //!< src1 is greater than src2. + CMP_GE = 2, //!< src1 is greater than or equal to src2. + CMP_LT = 3, //!< src1 is less than src2. + CMP_LE = 4, //!< src1 is less than or equal to src2. + CMP_NE = 5 //!< src1 is unequal to src2. + }; + +//! generalized matrix multiplication flags +enum GemmFlags { GEMM_1_T = 1, //!< transposes src1 + GEMM_2_T = 2, //!< transposes src2 + GEMM_3_T = 4 //!< transposes src3 + }; + +enum DftFlags { + /** performs an inverse 1D or 2D transform instead of the default forward + transform. */ + DFT_INVERSE = 1, + /** scales the result: divide it by the number of array elements. Normally, it is + combined with DFT_INVERSE. */ + DFT_SCALE = 2, + /** performs a forward or inverse transform of every individual row of the input + matrix; this flag enables you to transform multiple vectors simultaneously and can be used to + decrease the overhead (which is sometimes several times larger than the processing itself) to + perform 3D and higher-dimensional transformations and so forth.*/ + DFT_ROWS = 4, + /** performs a forward transformation of 1D or 2D real array; the result, + though being a complex array, has complex-conjugate symmetry (*CCS*, see the function + description below for details), and such an array can be packed into a real array of the same + size as input, which is the fastest option and which is what the function does by default; + however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) - + pass the flag to enable the function to produce a full-size complex output array. */ + DFT_COMPLEX_OUTPUT = 16, + /** performs an inverse transformation of a 1D or 2D complex array; the + result is normally a complex array of the same size, however, if the input array has + conjugate-complex symmetry (for example, it is a result of forward transformation with + DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not + check whether the input is symmetrical or not, you can pass the flag and then the function + will assume the symmetry and produce the real output array (note that when the input is packed + into a real array and inverse transformation is executed, the function treats the input as a + packed complex-conjugate symmetrical array, and the output will also be a real array). */ + DFT_REAL_OUTPUT = 32, + /** performs an inverse 1D or 2D transform instead of the default forward transform. */ + DCT_INVERSE = DFT_INVERSE, + /** performs a forward or inverse transform of every individual row of the input + matrix. This flag enables you to transform multiple vectors simultaneously and can be used to + decrease the overhead (which is sometimes several times larger than the processing itself) to + perform 3D and higher-dimensional transforms and so forth.*/ + DCT_ROWS = DFT_ROWS +}; + +//! Various border types, image boundaries are denoted with `|` +//! @see borderInterpolate, copyMakeBorder +enum BorderTypes { + BORDER_CONSTANT = 0, //!< `iiiiii|abcdefgh|iiiiiii` with some specified `i` + BORDER_REPLICATE = 1, //!< `aaaaaa|abcdefgh|hhhhhhh` + BORDER_REFLECT = 2, //!< `fedcba|abcdefgh|hgfedcb` + BORDER_WRAP = 3, //!< `cdefgh|abcdefgh|abcdefg` + BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba` + BORDER_TRANSPARENT = 5, //!< `uvwxyz|absdefgh|ijklmno` + + BORDER_REFLECT101 = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 + BORDER_DEFAULT = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 + BORDER_ISOLATED = 16 //!< do not look outside of ROI +}; + +//! @} core_array + +//! @addtogroup core_utils +//! @{ + +//! @cond IGNORED + +//////////////// static assert ///////////////// +#define CVAUX_CONCAT_EXP(a, b) a##b +#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b) + +#if defined(__clang__) +# ifndef __has_extension +# define __has_extension __has_feature /* compatibility, for older versions of clang */ +# endif +# if __has_extension(cxx_static_assert) +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# elif __has_extension(c_static_assert) +# define CV_StaticAssert(condition, reason) _Static_assert((condition), reason " " #condition) +# endif +#elif defined(__GNUC__) +# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L) +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# endif +#elif defined(_MSC_VER) +# if _MSC_VER >= 1600 /* MSVC 10 */ +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# endif +#endif +#ifndef CV_StaticAssert +# if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302) +# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); }) +# else + template struct CV_StaticAssert_failed; + template <> struct CV_StaticAssert_failed { enum { val = 1 }; }; + template struct CV_StaticAssert_test {}; +# define CV_StaticAssert(condition, reason)\ + typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__) +# endif +#endif + +// Suppress warning "-Wdeprecated-declarations" / C4996 +#if defined(_MSC_VER) + #define CV_DO_PRAGMA(x) __pragma(x) +#elif defined(__GNUC__) + #define CV_DO_PRAGMA(x) _Pragma (#x) +#else + #define CV_DO_PRAGMA(x) +#endif + +#ifdef _MSC_VER +#define CV_SUPPRESS_DEPRECATED_START \ + CV_DO_PRAGMA(warning(push)) \ + CV_DO_PRAGMA(warning(disable: 4996)) +#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop)) +#elif defined (__clang__) || ((__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 405)) +#define CV_SUPPRESS_DEPRECATED_START \ + CV_DO_PRAGMA(GCC diagnostic push) \ + CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations") +#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop) +#else +#define CV_SUPPRESS_DEPRECATED_START +#define CV_SUPPRESS_DEPRECATED_END +#endif +#define CV_UNUSED(name) (void)name +//! @endcond + +/*! @brief Signals an error and raises the exception. + +By default the function prints information about the error to stderr, +then it either stops if setBreakOnError() had been called before or raises the exception. +It is possible to alternate error processing by using redirectError(). +@param _code - error code (Error::Code) +@param _err - error description +@param _func - function name. Available only when the compiler supports getting it +@param _file - source file name where the error has occured +@param _line - line number in the source file where the error has occured +@see CV_Error, CV_Error_, CV_ErrorNoReturn, CV_ErrorNoReturn_, CV_Assert, CV_DbgAssert + */ +CV_EXPORTS void error(int _code, const String& _err, const char* _func, const char* _file, int _line); + +#ifdef __GNUC__ +# if defined __clang__ || defined __APPLE__ +# pragma GCC diagnostic push +# pragma GCC diagnostic ignored "-Winvalid-noreturn" +# endif +#endif + +/** same as cv::error, but does not return */ +CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const char* _func, const char* _file, int _line) +{ + error(_code, _err, _func, _file, _line); +#ifdef __GNUC__ +# if !defined __clang__ && !defined __APPLE__ + // this suppresses this warning: "noreturn" function does return [enabled by default] + __builtin_trap(); + // or use infinite loop: for (;;) {} +# endif +#endif +} +#ifdef __GNUC__ +# if defined __clang__ || defined __APPLE__ +# pragma GCC diagnostic pop +# endif +#endif + +#if defined __GNUC__ +#define CV_Func __func__ +#elif defined _MSC_VER +#define CV_Func __FUNCTION__ +#else +#define CV_Func "" +#endif + +/** @brief Call the error handler. + +Currently, the error handler prints the error code and the error message to the standard +error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that +the execution stack and all the parameters can be analyzed by the debugger. In the Release +configuration, the exception is thrown. + +@param code one of Error::Code +@param msg error message +*/ +#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ ) + +/** @brief Call the error handler. + +This macro can be used to construct an error message on-fly to include some dynamic information, +for example: +@code + // note the extra parentheses around the formatted text message + CV_Error_( CV_StsOutOfRange, + ("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue)); +@endcode +@param code one of Error::Code +@param args printf-like formatted error message in parentheses +*/ +#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ ) + +/** @brief Checks a condition at runtime and throws exception if it fails + +The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros +raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release +configurations while CV_DbgAssert is only retained in the Debug configuration. +*/ +#define CV_Assert( expr ) if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ) + +/** same as CV_Error(code,msg), but does not return */ +#define CV_ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ ) + +/** same as CV_Error_(code,args), but does not return */ +#define CV_ErrorNoReturn_( code, args ) cv::errorNoReturn( code, cv::format args, CV_Func, __FILE__, __LINE__ ) + +/** replaced with CV_Assert(expr) in Debug configuration */ +#ifdef _DEBUG +# define CV_DbgAssert(expr) CV_Assert(expr) +#else +# define CV_DbgAssert(expr) +#endif + +/* + * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor + * bit count of A exclusive XOR'ed with B + */ +struct CV_EXPORTS Hamming +{ + enum { normType = NORM_HAMMING }; + typedef unsigned char ValueType; + typedef int ResultType; + + /** this will count the bits in a ^ b + */ + ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const; +}; + +typedef Hamming HammingLUT; + +/////////////////////////////////// inline norms //////////////////////////////////// + +template inline _Tp cv_abs(_Tp x) { return std::abs(x); } +inline int cv_abs(uchar x) { return x; } +inline int cv_abs(schar x) { return std::abs(x); } +inline int cv_abs(ushort x) { return x; } +inline int cv_abs(short x) { return std::abs(x); } + +template static inline +_AccTp normL2Sqr(const _Tp* a, int n) +{ + _AccTp s = 0; + int i=0; +#if CV_ENABLE_UNROLLED + for( ; i <= n - 4; i += 4 ) + { + _AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3]; + s += v0*v0 + v1*v1 + v2*v2 + v3*v3; + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = a[i]; + s += v*v; + } + return s; +} + +template static inline +_AccTp normL1(const _Tp* a, int n) +{ + _AccTp s = 0; + int i = 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) + + (_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]); + } +#endif + for( ; i < n; i++ ) + s += cv_abs(a[i]); + return s; +} + +template static inline +_AccTp normInf(const _Tp* a, int n) +{ + _AccTp s = 0; + for( int i = 0; i < n; i++ ) + s = std::max(s, (_AccTp)cv_abs(a[i])); + return s; +} + +template static inline +_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + int i= 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); + s += v0*v0 + v1*v1 + v2*v2 + v3*v3; + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = _AccTp(a[i] - b[i]); + s += v*v; + } + return s; +} + +static inline float normL2Sqr(const float* a, const float* b, int n) +{ + float s = 0.f; + for( int i = 0; i < n; i++ ) + { + float v = a[i] - b[i]; + s += v*v; + } + return s; +} + +template static inline +_AccTp normL1(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + int i= 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); + s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3); + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = _AccTp(a[i] - b[i]); + s += std::abs(v); + } + return s; +} + +inline float normL1(const float* a, const float* b, int n) +{ + float s = 0.f; + for( int i = 0; i < n; i++ ) + { + s += std::abs(a[i] - b[i]); + } + return s; +} + +inline int normL1(const uchar* a, const uchar* b, int n) +{ + int s = 0; + for( int i = 0; i < n; i++ ) + { + s += std::abs(a[i] - b[i]); + } + return s; +} + +template static inline +_AccTp normInf(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + for( int i = 0; i < n; i++ ) + { + _AccTp v0 = a[i] - b[i]; + s = std::max(s, std::abs(v0)); + } + return s; +} + +/** @brief Computes the cube root of an argument. + + The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly. + NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for + single-precision data. + @param val A function argument. + */ +CV_EXPORTS_W float cubeRoot(float val); + +/** @brief Calculates the angle of a 2D vector in degrees. + + The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured + in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. + @param x x-coordinate of the vector. + @param y y-coordinate of the vector. + */ +CV_EXPORTS_W float fastAtan2(float y, float x); + +/** proxy for hal::LU */ +CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); +/** proxy for hal::LU */ +CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); +/** proxy for hal::Cholesky */ +CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); +/** proxy for hal::Cholesky */ +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); + +////////////////// forward declarations for important OpenCV types ////////////////// + +//! @cond IGNORED + +template class Vec; +template class Matx; + +template class Complex; +template class Point_; +template class Point3_; +template class Size_; +template class Rect_; +template class Scalar_; + +class CV_EXPORTS RotatedRect; +class CV_EXPORTS Range; +class CV_EXPORTS TermCriteria; +class CV_EXPORTS KeyPoint; +class CV_EXPORTS DMatch; +class CV_EXPORTS RNG; + +class CV_EXPORTS Mat; +class CV_EXPORTS MatExpr; + +class CV_EXPORTS UMat; + +class CV_EXPORTS SparseMat; +typedef Mat MatND; + +template class Mat_; +template class SparseMat_; + +class CV_EXPORTS MatConstIterator; +class CV_EXPORTS SparseMatIterator; +class CV_EXPORTS SparseMatConstIterator; +template class MatIterator_; +template class MatConstIterator_; +template class SparseMatIterator_; +template class SparseMatConstIterator_; + +namespace ogl +{ + class CV_EXPORTS Buffer; + class CV_EXPORTS Texture2D; + class CV_EXPORTS Arrays; +} + +namespace cuda +{ + class CV_EXPORTS GpuMat; + class CV_EXPORTS HostMem; + class CV_EXPORTS Stream; + class CV_EXPORTS Event; +} + +namespace cudev +{ + template class GpuMat_; +} + +namespace ipp +{ +CV_EXPORTS int getIppFeatures(); +CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL, + int line = 0); +CV_EXPORTS int getIppStatus(); +CV_EXPORTS String getIppErrorLocation(); +CV_EXPORTS bool useIPP(); +CV_EXPORTS void setUseIPP(bool flag); + +} // ipp + +//! @endcond + +//! @} core_utils + + + + +} // cv + +#include "opencv2/core/neon_utils.hpp" + +#endif //OPENCV_CORE_BASE_HPP diff --git a/thirdparty/linux/include/opencv2/core/bufferpool.hpp b/thirdparty/linux/include/opencv2/core/bufferpool.hpp new file mode 100644 index 0000000..9e7b7c2 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/bufferpool.hpp @@ -0,0 +1,31 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. + +#ifndef OPENCV_CORE_BUFFER_POOL_HPP +#define OPENCV_CORE_BUFFER_POOL_HPP + +namespace cv +{ + +//! @addtogroup core +//! @{ + +class BufferPoolController +{ +protected: + ~BufferPoolController() { } +public: + virtual size_t getReservedSize() const = 0; + virtual size_t getMaxReservedSize() const = 0; + virtual void setMaxReservedSize(size_t size) = 0; + virtual void freeAllReservedBuffers() = 0; +}; + +//! @} + +} + +#endif // OPENCV_CORE_BUFFER_POOL_HPP diff --git a/thirdparty/linux/include/opencv2/core/core.hpp b/thirdparty/linux/include/opencv2/core/core.hpp new file mode 100644 index 0000000..4389183 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/core.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/core.hpp" diff --git a/thirdparty/linux/include/opencv2/core/core_c.h b/thirdparty/linux/include/opencv2/core/core_c.h new file mode 100644 index 0000000..e12f79d --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/core_c.h @@ -0,0 +1,3184 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_CORE_C_H +#define OPENCV_CORE_C_H + +#include "opencv2/core/types_c.h" + +#ifdef __cplusplus +# ifdef _MSC_VER +/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename' + which is incompatible with C + + It is OK to disable it because we only extend few plain structures with + C++ construrtors for simpler interoperability with C++ API of the library +*/ +# pragma warning(disable:4190) +# elif defined __clang__ && __clang_major__ >= 3 +# pragma GCC diagnostic ignored "-Wreturn-type-c-linkage" +# endif +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup core_c + @{ +*/ + +/****************************************************************************************\ +* Array allocation, deallocation, initialization and access to elements * +\****************************************************************************************/ + +/** `malloc` wrapper. + If there is no enough memory, the function + (as well as other OpenCV functions that call cvAlloc) + raises an error. */ +CVAPI(void*) cvAlloc( size_t size ); + +/** `free` wrapper. + Here and further all the memory releasing functions + (that all call cvFree) take double pointer in order to + to clear pointer to the data after releasing it. + Passing pointer to NULL pointer is Ok: nothing happens in this case +*/ +CVAPI(void) cvFree_( void* ptr ); +#define cvFree(ptr) (cvFree_(*(ptr)), *(ptr)=0) + +/** @brief Creates an image header but does not allocate the image data. + +@param size Image width and height +@param depth Image depth (see cvCreateImage ) +@param channels Number of channels (see cvCreateImage ) + */ +CVAPI(IplImage*) cvCreateImageHeader( CvSize size, int depth, int channels ); + +/** @brief Initializes an image header that was previously allocated. + +The returned IplImage\* points to the initialized header. +@param image Image header to initialize +@param size Image width and height +@param depth Image depth (see cvCreateImage ) +@param channels Number of channels (see cvCreateImage ) +@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL +@param align Alignment for image rows, typically 4 or 8 bytes + */ +CVAPI(IplImage*) cvInitImageHeader( IplImage* image, CvSize size, int depth, + int channels, int origin CV_DEFAULT(0), + int align CV_DEFAULT(4)); + +/** @brief Creates an image header and allocates the image data. + +This function call is equivalent to the following code: +@code + header = cvCreateImageHeader(size, depth, channels); + cvCreateData(header); +@endcode +@param size Image width and height +@param depth Bit depth of image elements. See IplImage for valid depths. +@param channels Number of channels per pixel. See IplImage for details. This function only creates +images with interleaved channels. + */ +CVAPI(IplImage*) cvCreateImage( CvSize size, int depth, int channels ); + +/** @brief Deallocates an image header. + +This call is an analogue of : +@code + if(image ) + { + iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI); + *image = 0; + } +@endcode +but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro). +@param image Double pointer to the image header + */ +CVAPI(void) cvReleaseImageHeader( IplImage** image ); + +/** @brief Deallocates the image header and the image data. + +This call is a shortened form of : +@code + if(*image ) + { + cvReleaseData(*image); + cvReleaseImageHeader(image); + } +@endcode +@param image Double pointer to the image header +*/ +CVAPI(void) cvReleaseImage( IplImage** image ); + +/** Creates a copy of IPL image (widthStep may differ) */ +CVAPI(IplImage*) cvCloneImage( const IplImage* image ); + +/** @brief Sets the channel of interest in an IplImage. + +If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do +*not* support the COI setting, so to process an individual image/matrix channel one may copy (via +cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result +back (via cvCopy or cvMerge) if needed. +@param image A pointer to the image header +@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected, +etc. Note that the channel indices become 1-based. + */ +CVAPI(void) cvSetImageCOI( IplImage* image, int coi ); + +/** @brief Returns the index of the channel of interest. + +Returns the channel of interest of in an IplImage. Returned values correspond to the coi in +cvSetImageCOI. +@param image A pointer to the image header + */ +CVAPI(int) cvGetImageCOI( const IplImage* image ); + +/** @brief Sets an image Region Of Interest (ROI) for a given rectangle. + +If the original image ROI was NULL and the rect is not the whole image, the ROI structure is +allocated. + +Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For +example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the +ROI, not the original image. +@param image A pointer to the image header +@param rect The ROI rectangle + */ +CVAPI(void) cvSetImageROI( IplImage* image, CvRect rect ); + +/** @brief Resets the image ROI to include the entire image and releases the ROI structure. + +This produces a similar result to the following, but in addition it releases the ROI structure. : +@code + cvSetImageROI(image, cvRect(0, 0, image->width, image->height )); + cvSetImageCOI(image, 0); +@endcode +@param image A pointer to the image header + */ +CVAPI(void) cvResetImageROI( IplImage* image ); + +/** @brief Returns the image ROI. + +If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned. +@param image A pointer to the image header + */ +CVAPI(CvRect) cvGetImageROI( const IplImage* image ); + +/** @brief Creates a matrix header but does not allocate the matrix data. + +The function allocates a new matrix header and returns a pointer to it. The matrix data can then be +allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type Type of the matrix elements, see cvCreateMat + */ +CVAPI(CvMat*) cvCreateMatHeader( int rows, int cols, int type ); + +#define CV_AUTOSTEP 0x7fffffff + +/** @brief Initializes a pre-allocated matrix header. + +This function is often used to process raw data with OpenCV matrix functions. For example, the +following code computes the matrix product of two matrices, stored as ordinary arrays: +@code + double a[] = { 1, 2, 3, 4, + 5, 6, 7, 8, + 9, 10, 11, 12 }; + + double b[] = { 1, 5, 9, + 2, 6, 10, + 3, 7, 11, + 4, 8, 12 }; + + double c[9]; + CvMat Ma, Mb, Mc ; + + cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a); + cvInitMatHeader(&Mb, 4, 3, CV_64FC1, b); + cvInitMatHeader(&Mc, 3, 3, CV_64FC1, c); + + cvMatMulAdd(&Ma, &Mb, 0, &Mc); + // the c array now contains the product of a (3x4) and b (4x3) +@endcode +@param mat A pointer to the matrix header to be initialized +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type Type of the matrix elements, see cvCreateMat . +@param data Optional: data pointer assigned to the matrix header +@param step Optional: full row width in bytes of the assigned data. By default, the minimal +possible step is used which assumes there are no gaps between subsequent rows of the matrix. + */ +CVAPI(CvMat*) cvInitMatHeader( CvMat* mat, int rows, int cols, + int type, void* data CV_DEFAULT(NULL), + int step CV_DEFAULT(CV_AUTOSTEP) ); + +/** @brief Creates a matrix header and allocates the matrix data. + +The function call is equivalent to the following code: +@code + CvMat* mat = cvCreateMatHeader(rows, cols, type); + cvCreateData(mat); +@endcode +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type The type of the matrix elements in the form +CV_\\C\ , where S=signed, U=unsigned, F=float. For +example, CV _ 8UC1 means the elements are 8-bit unsigned and the there is 1 channel, and CV _ +32SC2 means the elements are 32-bit signed and there are 2 channels. + */ +CVAPI(CvMat*) cvCreateMat( int rows, int cols, int type ); + +/** @brief Deallocates a matrix. + +The function decrements the matrix data reference counter and deallocates matrix header. If the data +reference counter is 0, it also deallocates the data. : +@code + if(*mat ) + cvDecRefData(*mat); + cvFree((void**)mat); +@endcode +@param mat Double pointer to the matrix + */ +CVAPI(void) cvReleaseMat( CvMat** mat ); + +/** @brief Decrements an array data reference counter. + +The function decrements the data reference counter in a CvMat or CvMatND if the reference counter + +pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current +implementation the reference counter is not NULL only if the data was allocated using the +cvCreateData function. The counter will be NULL in other cases such as: external data was assigned +to the header using cvSetData, header is part of a larger matrix or image, or the header was +converted from an image or n-dimensional matrix header. +@param arr Pointer to an array header + */ +CV_INLINE void cvDecRefData( CvArr* arr ) +{ + if( CV_IS_MAT( arr )) + { + CvMat* mat = (CvMat*)arr; + mat->data.ptr = NULL; + if( mat->refcount != NULL && --*mat->refcount == 0 ) + cvFree( &mat->refcount ); + mat->refcount = NULL; + } + else if( CV_IS_MATND( arr )) + { + CvMatND* mat = (CvMatND*)arr; + mat->data.ptr = NULL; + if( mat->refcount != NULL && --*mat->refcount == 0 ) + cvFree( &mat->refcount ); + mat->refcount = NULL; + } +} + +/** @brief Increments array data reference counter. + +The function increments CvMat or CvMatND data reference counter and returns the new counter value if +the reference counter pointer is not NULL, otherwise it returns zero. +@param arr Array header + */ +CV_INLINE int cvIncRefData( CvArr* arr ) +{ + int refcount = 0; + if( CV_IS_MAT( arr )) + { + CvMat* mat = (CvMat*)arr; + if( mat->refcount != NULL ) + refcount = ++*mat->refcount; + } + else if( CV_IS_MATND( arr )) + { + CvMatND* mat = (CvMatND*)arr; + if( mat->refcount != NULL ) + refcount = ++*mat->refcount; + } + return refcount; +} + + +/** Creates an exact copy of the input matrix (except, may be, step value) */ +CVAPI(CvMat*) cvCloneMat( const CvMat* mat ); + + +/** @brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix. + +The function returns header, corresponding to a specified rectangle of the input array. In other + +words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is +taken into account by the function so the sub-array of ROI is actually extracted. +@param arr Input array +@param submat Pointer to the resultant sub-array header +@param rect Zero-based coordinates of the rectangle of interest + */ +CVAPI(CvMat*) cvGetSubRect( const CvArr* arr, CvMat* submat, CvRect rect ); +#define cvGetSubArr cvGetSubRect + +/** @brief Returns array row or row span. + +The functions return the header, corresponding to a specified row/row span of the input array. +cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1). +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param start_row Zero-based index of the starting row (inclusive) of the span +@param end_row Zero-based index of the ending row (exclusive) of the span +@param delta_row Index step in the row span. That is, the function extracts every delta_row -th +row from start_row and up to (but not including) end_row . + */ +CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat, + int start_row, int end_row, + int delta_row CV_DEFAULT(1)); + +/** @overload +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param row Zero-based index of the selected row +*/ +CV_INLINE CvMat* cvGetRow( const CvArr* arr, CvMat* submat, int row ) +{ + return cvGetRows( arr, submat, row, row + 1, 1 ); +} + + +/** @brief Returns one of more array columns. + +The functions return the header, corresponding to a specified column span of the input array. That + +is, no data is copied. Therefore, any modifications of the submatrix will affect the original array. +If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for +cvGetCols(arr, submat, col, col+1). +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param start_col Zero-based index of the starting column (inclusive) of the span +@param end_col Zero-based index of the ending column (exclusive) of the span + */ +CVAPI(CvMat*) cvGetCols( const CvArr* arr, CvMat* submat, + int start_col, int end_col ); + +/** @overload +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param col Zero-based index of the selected column +*/ +CV_INLINE CvMat* cvGetCol( const CvArr* arr, CvMat* submat, int col ) +{ + return cvGetCols( arr, submat, col, col + 1 ); +} + +/** @brief Returns one of array diagonals. + +The function returns the header, corresponding to a specified diagonal of the input array. +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1 +corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so +forth. + */ +CVAPI(CvMat*) cvGetDiag( const CvArr* arr, CvMat* submat, + int diag CV_DEFAULT(0)); + +/** low-level scalar <-> raw data conversion functions */ +CVAPI(void) cvScalarToRawData( const CvScalar* scalar, void* data, int type, + int extend_to_12 CV_DEFAULT(0) ); + +CVAPI(void) cvRawDataToScalar( const void* data, int type, CvScalar* scalar ); + +/** @brief Creates a new matrix header but does not allocate the matrix data. + +The function allocates a header for a multi-dimensional dense array. The array data can further be +allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. +@param dims Number of array dimensions +@param sizes Array of dimension sizes +@param type Type of array elements, see cvCreateMat + */ +CVAPI(CvMatND*) cvCreateMatNDHeader( int dims, const int* sizes, int type ); + +/** @brief Creates the header and allocates the data for a multi-dimensional dense array. + +This function call is equivalent to the following code: +@code + CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type); + cvCreateData(mat); +@endcode +@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be +changed at build time). +@param sizes Array of dimension sizes. +@param type Type of array elements, see cvCreateMat . + */ +CVAPI(CvMatND*) cvCreateMatND( int dims, const int* sizes, int type ); + +/** @brief Initializes a pre-allocated multi-dimensional array header. + +@param mat A pointer to the array header to be initialized +@param dims The number of array dimensions +@param sizes An array of dimension sizes +@param type Type of array elements, see cvCreateMat +@param data Optional data pointer assigned to the matrix header + */ +CVAPI(CvMatND*) cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes, + int type, void* data CV_DEFAULT(NULL) ); + +/** @brief Deallocates a multi-dimensional array. + +The function decrements the array data reference counter and releases the array header. If the +reference counter reaches 0, it also deallocates the data. : +@code + if(*mat ) + cvDecRefData(*mat); + cvFree((void**)mat); +@endcode +@param mat Double pointer to the array + */ +CV_INLINE void cvReleaseMatND( CvMatND** mat ) +{ + cvReleaseMat( (CvMat**)mat ); +} + +/** Creates a copy of CvMatND (except, may be, steps) */ +CVAPI(CvMatND*) cvCloneMatND( const CvMatND* mat ); + +/** @brief Creates sparse array. + +The function allocates a multi-dimensional sparse array. Initially the array contain no elements, +that is PtrND and other related functions will return 0 for every index. +@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is +practically unlimited (up to \f$2^{16}\f$ ). +@param sizes Array of dimension sizes +@param type Type of array elements. The same as for CvMat + */ +CVAPI(CvSparseMat*) cvCreateSparseMat( int dims, const int* sizes, int type ); + +/** @brief Deallocates sparse array. + +The function releases the sparse array and clears the array pointer upon exit. +@param mat Double pointer to the array + */ +CVAPI(void) cvReleaseSparseMat( CvSparseMat** mat ); + +/** Creates a copy of CvSparseMat (except, may be, zero items) */ +CVAPI(CvSparseMat*) cvCloneSparseMat( const CvSparseMat* mat ); + +/** @brief Initializes sparse array elements iterator. + +The function initializes iterator of sparse array elements and returns pointer to the first element, +or NULL if the array is empty. +@param mat Input array +@param mat_iterator Initialized iterator + */ +CVAPI(CvSparseNode*) cvInitSparseMatIterator( const CvSparseMat* mat, + CvSparseMatIterator* mat_iterator ); + +/** @brief Returns the next sparse matrix element + +The function moves iterator to the next sparse matrix element and returns pointer to it. In the +current version there is no any particular order of the elements, because they are stored in the +hash table. The sample below demonstrates how to iterate through the sparse matrix: +@code + // print all the non-zero sparse matrix elements and compute their sum + double sum = 0; + int i, dims = cvGetDims(sparsemat); + CvSparseMatIterator it; + CvSparseNode* node = cvInitSparseMatIterator(sparsemat, &it); + + for(; node != 0; node = cvGetNextSparseNode(&it)) + { + int* idx = CV_NODE_IDX(array, node); + float val = *(float*)CV_NODE_VAL(array, node); + printf("M"); + for(i = 0; i < dims; i++ ) + printf("[%d]", idx[i]); + printf("=%g\n", val); + + sum += val; + } + + printf("nTotal sum = %g\n", sum); +@endcode +@param mat_iterator Sparse array iterator + */ +CV_INLINE CvSparseNode* cvGetNextSparseNode( CvSparseMatIterator* mat_iterator ) +{ + if( mat_iterator->node->next ) + return mat_iterator->node = mat_iterator->node->next; + else + { + int idx; + for( idx = ++mat_iterator->curidx; idx < mat_iterator->mat->hashsize; idx++ ) + { + CvSparseNode* node = (CvSparseNode*)mat_iterator->mat->hashtable[idx]; + if( node ) + { + mat_iterator->curidx = idx; + return mat_iterator->node = node; + } + } + return NULL; + } +} + + +#define CV_MAX_ARR 10 + +/** matrix iterator: used for n-ary operations on dense arrays */ +typedef struct CvNArrayIterator +{ + int count; /**< number of arrays */ + int dims; /**< number of dimensions to iterate */ + CvSize size; /**< maximal common linear size: { width = size, height = 1 } */ + uchar* ptr[CV_MAX_ARR]; /**< pointers to the array slices */ + int stack[CV_MAX_DIM]; /**< for internal use */ + CvMatND* hdr[CV_MAX_ARR]; /**< pointers to the headers of the + matrices that are processed */ +} +CvNArrayIterator; + +#define CV_NO_DEPTH_CHECK 1 +#define CV_NO_CN_CHECK 2 +#define CV_NO_SIZE_CHECK 4 + +/** initializes iterator that traverses through several arrays simulteneously + (the function together with cvNextArraySlice is used for + N-ari element-wise operations) */ +CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs, + const CvArr* mask, CvMatND* stubs, + CvNArrayIterator* array_iterator, + int flags CV_DEFAULT(0) ); + +/** returns zero value if iteration is finished, non-zero (slice length) otherwise */ +CVAPI(int) cvNextNArraySlice( CvNArrayIterator* array_iterator ); + + +/** @brief Returns type of array elements. + +The function returns type of the array elements. In the case of IplImage the type is converted to +CvMat-like representation. For example, if the image has been created as: +@code + IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3); +@endcode +The code cvGetElemType(img) will return CV_8UC3. +@param arr Input array + */ +CVAPI(int) cvGetElemType( const CvArr* arr ); + +/** @brief Return number of array dimensions + +The function returns the array dimensionality and the array of dimension sizes. In the case of +IplImage or CvMat it always returns 2 regardless of number of image/matrix rows. For example, the +following code calculates total number of array elements: +@code + int sizes[CV_MAX_DIM]; + int i, total = 1; + int dims = cvGetDims(arr, size); + for(i = 0; i < dims; i++ ) + total *= sizes[i]; +@endcode +@param arr Input array +@param sizes Optional output vector of the array dimension sizes. For 2d arrays the number of rows +(height) goes first, number of columns (width) next. + */ +CVAPI(int) cvGetDims( const CvArr* arr, int* sizes CV_DEFAULT(NULL) ); + + +/** @brief Returns array size along the specified dimension. + +@param arr Input array +@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of +columns; for images 0 means height, 1 means width) + */ +CVAPI(int) cvGetDimSize( const CvArr* arr, int index ); + + +/** @brief Return pointer to a particular array element. + +The functions return a pointer to a specific array element. Number of array dimension should match +to the number of indices passed to the function except for cvPtr1D function that can be used for +sequential access to 1D, 2D or nD dense arrays. + +The functions can be used for sparse arrays as well - if the requested node does not exist they +create it and set it to zero. + +All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet +, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param type Optional output parameter: type of matrix elements + */ +CVAPI(uchar*) cvPtr1D( const CvArr* arr, int idx0, int* type CV_DEFAULT(NULL)); +/** @overload */ +CVAPI(uchar*) cvPtr2D( const CvArr* arr, int idx0, int idx1, int* type CV_DEFAULT(NULL) ); +/** @overload */ +CVAPI(uchar*) cvPtr3D( const CvArr* arr, int idx0, int idx1, int idx2, + int* type CV_DEFAULT(NULL)); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param type Optional output parameter: type of matrix elements +@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter +means that the requested element is created if it does not exist already. +@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL, +the function does not recalculate the node hash value, but takes it from the specified location. +It is useful for speeding up pair-wise operations (TODO: provide an example) +*/ +CVAPI(uchar*) cvPtrND( const CvArr* arr, const int* idx, int* type CV_DEFAULT(NULL), + int create_node CV_DEFAULT(1), + unsigned* precalc_hashval CV_DEFAULT(NULL)); + +/** @brief Return a specific array element. + +The functions return a specific array element. In the case of a sparse array the functions return 0 +if the requested node does not exist (no new node is created by the functions). +@param arr Input array +@param idx0 The first zero-based component of the element index + */ +CVAPI(CvScalar) cvGet1D( const CvArr* arr, int idx0 ); +/** @overload */ +CVAPI(CvScalar) cvGet2D( const CvArr* arr, int idx0, int idx1 ); +/** @overload */ +CVAPI(CvScalar) cvGet3D( const CvArr* arr, int idx0, int idx1, int idx2 ); +/** @overload +@param arr Input array +@param idx Array of the element indices +*/ +CVAPI(CvScalar) cvGetND( const CvArr* arr, const int* idx ); + +/** @brief Return a specific element of single-channel 1D, 2D, 3D or nD array. + +Returns a specific element of a single-channel array. If the array has multiple channels, a runtime +error is raised. Note that Get?D functions can be used safely for both single-channel and +multiple-channel arrays though they are a bit slower. + +In the case of a sparse array the functions return 0 if the requested node does not exist (no new +node is created by the functions). +@param arr Input array. Must have a single channel. +@param idx0 The first zero-based component of the element index + */ +CVAPI(double) cvGetReal1D( const CvArr* arr, int idx0 ); +/** @overload */ +CVAPI(double) cvGetReal2D( const CvArr* arr, int idx0, int idx1 ); +/** @overload */ +CVAPI(double) cvGetReal3D( const CvArr* arr, int idx0, int idx1, int idx2 ); +/** @overload +@param arr Input array. Must have a single channel. +@param idx Array of the element indices +*/ +CVAPI(double) cvGetRealND( const CvArr* arr, const int* idx ); + +/** @brief Change the particular array element. + +The functions assign the new value to a particular array element. In the case of a sparse array the +functions create the node if it does not exist yet. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param value The assigned value + */ +CVAPI(void) cvSet1D( CvArr* arr, int idx0, CvScalar value ); +/** @overload */ +CVAPI(void) cvSet2D( CvArr* arr, int idx0, int idx1, CvScalar value ); +/** @overload */ +CVAPI(void) cvSet3D( CvArr* arr, int idx0, int idx1, int idx2, CvScalar value ); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param value The assigned value +*/ +CVAPI(void) cvSetND( CvArr* arr, const int* idx, CvScalar value ); + +/** @brief Change a specific array element. + +The functions assign a new value to a specific element of a single-channel array. If the array has +multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for +both single-channel and multiple-channel arrays, though they are a bit slower. + +In the case of a sparse array the functions create the node if it does not yet exist. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param value The assigned value + */ +CVAPI(void) cvSetReal1D( CvArr* arr, int idx0, double value ); +/** @overload */ +CVAPI(void) cvSetReal2D( CvArr* arr, int idx0, int idx1, double value ); +/** @overload */ +CVAPI(void) cvSetReal3D( CvArr* arr, int idx0, + int idx1, int idx2, double value ); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param value The assigned value +*/ +CVAPI(void) cvSetRealND( CvArr* arr, const int* idx, double value ); + +/** clears element of ND dense array, + in case of sparse arrays it deletes the specified node */ +CVAPI(void) cvClearND( CvArr* arr, const int* idx ); + +/** @brief Returns matrix header for arbitrary array. + +The function returns a matrix header for the input array that can be a matrix - CvMat, an image - +IplImage, or a multi-dimensional dense array - CvMatND (the third option is allowed only if +allowND != 0) . In the case of matrix the function simply returns the input pointer. In the case of +IplImage\* or CvMatND it initializes the header structure with parameters of the current image ROI +and returns &header. Because COI is not supported by CvMat, it is returned separately. + +The function provides an easy way to handle both types of arrays - IplImage and CvMat using the same +code. Input array must have non-zero data pointer, otherwise the function will report an error. + +@note If the input array is IplImage with planar data layout and COI set, the function returns the +pointer to the selected plane and COI == 0. This feature allows user to process IplImage structures +with planar data layout, even though OpenCV does not support such images. +@param arr Input array +@param header Pointer to CvMat structure used as a temporary buffer +@param coi Optional output parameter for storing COI +@param allowND If non-zero, the function accepts multi-dimensional dense arrays (CvMatND\*) and +returns 2D matrix (if CvMatND has two dimensions) or 1D matrix (when CvMatND has 1 dimension or +more than 2 dimensions). The CvMatND array must be continuous. +@sa cvGetImage, cvarrToMat. + */ +CVAPI(CvMat*) cvGetMat( const CvArr* arr, CvMat* header, + int* coi CV_DEFAULT(NULL), + int allowND CV_DEFAULT(0)); + +/** @brief Returns image header for arbitrary array. + +The function returns the image header for the input array that can be a matrix (CvMat) or image +(IplImage). In the case of an image the function simply returns the input pointer. In the case of +CvMat it initializes an image_header structure with the parameters of the input matrix. Note that +if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using +this function, we will get different headers if the ROI is set in the original image. +@param arr Input array +@param image_header Pointer to IplImage structure used as a temporary buffer + */ +CVAPI(IplImage*) cvGetImage( const CvArr* arr, IplImage* image_header ); + + +/** @brief Changes the shape of a multi-dimensional array without copying the data. + +The function is an advanced version of cvReshape that can work with multi-dimensional arrays as +well (though it can work with ordinary images and matrices) and change the number of dimensions. + +Below are the two samples from the cvReshape description rewritten using cvReshapeMatND: +@code + IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); + IplImage gray_img_hdr, *gray_img; + gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0); + ... + int size[] = { 2, 2, 2 }; + CvMatND* mat = cvCreateMatND(3, size, CV_32F); + CvMat row_header, *row; + row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0); +@endcode +In C, the header file for this function includes a convenient macro cvReshapeND that does away with +the sizeof_header parameter. So, the lines containing the call to cvReshapeMatND in the examples +may be replaced as follow: +@code + gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0); + ... + row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0); +@endcode +@param arr Input array +@param sizeof_header Size of output header to distinguish between IplImage, CvMat and CvMatND +output headers +@param header Output header to be filled +@param new_cn New number of channels. new_cn = 0 means that the number of channels remains +unchanged. +@param new_dims New number of dimensions. new_dims = 0 means that the number of dimensions +remains the same. +@param new_sizes Array of new dimension sizes. Only new_dims-1 values are used, because the +total number of elements must remain the same. Thus, if new_dims = 1, new_sizes array is not +used. + */ +CVAPI(CvArr*) cvReshapeMatND( const CvArr* arr, + int sizeof_header, CvArr* header, + int new_cn, int new_dims, int* new_sizes ); + +#define cvReshapeND( arr, header, new_cn, new_dims, new_sizes ) \ + cvReshapeMatND( (arr), sizeof(*(header)), (header), \ + (new_cn), (new_dims), (new_sizes)) + +/** @brief Changes shape of matrix/image without copying data. + +The function initializes the CvMat header so that it points to the same data as the original array +but has a different shape - different number of channels, different number of rows, or both. + +The following example code creates one image buffer and two image headers, the first is for a +320x240x3 image and the second is for a 960x240x1 image: +@code + IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); + CvMat gray_mat_hdr; + IplImage gray_img_hdr, *gray_img; + cvReshape(color_img, &gray_mat_hdr, 1); + gray_img = cvGetImage(&gray_mat_hdr, &gray_img_hdr); +@endcode +And the next example converts a 3x3 matrix to a single 1x9 vector: +@code + CvMat* mat = cvCreateMat(3, 3, CV_32F); + CvMat row_header, *row; + row = cvReshape(mat, &row_header, 0, 1); +@endcode +@param arr Input array +@param header Output header to be filled +@param new_cn New number of channels. 'new_cn = 0' means that the number of channels remains +unchanged. +@param new_rows New number of rows. 'new_rows = 0' means that the number of rows remains +unchanged unless it needs to be changed according to new_cn value. +*/ +CVAPI(CvMat*) cvReshape( const CvArr* arr, CvMat* header, + int new_cn, int new_rows CV_DEFAULT(0) ); + +/** Repeats source 2d array several times in both horizontal and + vertical direction to fill destination array */ +CVAPI(void) cvRepeat( const CvArr* src, CvArr* dst ); + +/** @brief Allocates array data + +The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of +matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless +CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used +to allocate the data. +@param arr Array header + */ +CVAPI(void) cvCreateData( CvArr* arr ); + +/** @brief Releases array data. + +The function releases the array data. In the case of CvMat or CvMatND it simply calls +cvDecRefData(), that is the function can not deallocate external data. See also the note to +cvCreateData . +@param arr Array header + */ +CVAPI(void) cvReleaseData( CvArr* arr ); + +/** @brief Assigns user data to the array header. + +The function assigns user data to the array header. Header should be initialized before using +cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader, +cvInitImageHeader or cvInitMatNDHeader. +@param arr Array header +@param data User data +@param step Full row length in bytes + */ +CVAPI(void) cvSetData( CvArr* arr, void* data, int step ); + +/** @brief Retrieves low-level information about the array. + +The function fills output variables with low-level information about the array data. All output + +parameters are optional, so some of the pointers may be set to NULL. If the array is IplImage with +ROI set, the parameters of ROI are returned. + +The following example shows how to get access to array elements. It computes absolute values of the +array elements : +@code + float* data; + int step; + CvSize size; + + cvGetRawData(array, (uchar**)&data, &step, &size); + step /= sizeof(data[0]); + + for(int y = 0; y < size.height; y++, data += step ) + for(int x = 0; x < size.width; x++ ) + data[x] = (float)fabs(data[x]); +@endcode +@param arr Array header +@param data Output pointer to the whole image origin or ROI origin if ROI is set +@param step Output full row length in bytes +@param roi_size Output ROI size + */ +CVAPI(void) cvGetRawData( const CvArr* arr, uchar** data, + int* step CV_DEFAULT(NULL), + CvSize* roi_size CV_DEFAULT(NULL)); + +/** @brief Returns size of matrix or image ROI. + +The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the +input matrix or image. In the case of image the size of ROI is returned. +@param arr array header + */ +CVAPI(CvSize) cvGetSize( const CvArr* arr ); + +/** @brief Copies one array to another. + +The function copies selected elements from an input array to an output array: + +\f[\texttt{dst} (I)= \texttt{src} (I) \quad \text{if} \quad \texttt{mask} (I) \ne 0.\f] + +If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays +must have the same type, the same number of dimensions, and the same size. The function can also +copy sparse arrays (mask is not supported in this case). +@param src The source array +@param dst The destination array +@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array +to be changed + */ +CVAPI(void) cvCopy( const CvArr* src, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Sets every element of an array to a given value. + +The function copies the scalar value to every selected element of the destination array: +\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I) \ne 0\f] +If array arr is of IplImage type, then is ROI used, but COI must not be set. +@param arr The destination array +@param value Fill value +@param mask Operation mask, 8-bit single channel array; specifies elements of the destination +array to be changed + */ +CVAPI(void) cvSet( CvArr* arr, CvScalar value, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Clears the array. + +The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage), +cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the +elements are removed. +@param arr Array to be cleared + */ +CVAPI(void) cvSetZero( CvArr* arr ); +#define cvZero cvSetZero + + +/** Splits a multi-channel array into the set of single-channel arrays or + extracts particular [color] plane */ +CVAPI(void) cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1, + CvArr* dst2, CvArr* dst3 ); + +/** Merges a set of single-channel arrays into the single multi-channel array + or inserts one particular [color] plane to the array */ +CVAPI(void) cvMerge( const CvArr* src0, const CvArr* src1, + const CvArr* src2, const CvArr* src3, + CvArr* dst ); + +/** Copies several channels from input arrays to + certain channels of output arrays */ +CVAPI(void) cvMixChannels( const CvArr** src, int src_count, + CvArr** dst, int dst_count, + const int* from_to, int pair_count ); + +/** @brief Converts one array to another with optional linear transformation. + +The function has several different purposes, and thus has several different names. It copies one +array to another with optional scaling, which is performed first, and/or optional type conversion, +performed after: + +\f[\texttt{dst} (I) = \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f] + +All the channels of multi-channel arrays are processed independently. + +The type of conversion is done with rounding and saturation, that is if the result of scaling + +conversion can not be represented exactly by a value of the destination array element type, it is +set to the nearest representable value on the real axis. +@param src Source array +@param dst Destination array +@param scale Scale factor +@param shift Value added to the scaled source array elements + */ +CVAPI(void) cvConvertScale( const CvArr* src, CvArr* dst, + double scale CV_DEFAULT(1), + double shift CV_DEFAULT(0) ); +#define cvCvtScale cvConvertScale +#define cvScale cvConvertScale +#define cvConvert( src, dst ) cvConvertScale( (src), (dst), 1, 0 ) + + +/** Performs linear transformation on every source array element, + stores absolute value of the result: + dst(x,y,c) = abs(scale*src(x,y,c)+shift). + destination array must have 8u type. + In other cases one may use cvConvertScale + cvAbsDiffS */ +CVAPI(void) cvConvertScaleAbs( const CvArr* src, CvArr* dst, + double scale CV_DEFAULT(1), + double shift CV_DEFAULT(0) ); +#define cvCvtScaleAbs cvConvertScaleAbs + + +/** checks termination criteria validity and + sets eps to default_eps (if it is not set), + max_iter to default_max_iters (if it is not set) +*/ +CVAPI(CvTermCriteria) cvCheckTermCriteria( CvTermCriteria criteria, + double default_eps, + int default_max_iters ); + +/****************************************************************************************\ +* Arithmetic, logic and comparison operations * +\****************************************************************************************/ + +/** dst(mask) = src1(mask) + src2(mask) */ +CVAPI(void) cvAdd( const CvArr* src1, const CvArr* src2, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src(mask) + value */ +CVAPI(void) cvAddS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src1(mask) - src2(mask) */ +CVAPI(void) cvSub( const CvArr* src1, const CvArr* src2, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src(mask) - value = src(mask) + (-value) */ +CV_INLINE void cvSubS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)) +{ + cvAddS( src, cvScalar( -value.val[0], -value.val[1], -value.val[2], -value.val[3]), + dst, mask ); +} + +/** dst(mask) = value - src(mask) */ +CVAPI(void) cvSubRS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) * src2(idx) * scale + (scaled element-wise multiplication of 2 arrays) */ +CVAPI(void) cvMul( const CvArr* src1, const CvArr* src2, + CvArr* dst, double scale CV_DEFAULT(1) ); + +/** element-wise division/inversion with scaling: + dst(idx) = src1(idx) * scale / src2(idx) + or dst(idx) = scale / src2(idx) if src1 == 0 */ +CVAPI(void) cvDiv( const CvArr* src1, const CvArr* src2, + CvArr* dst, double scale CV_DEFAULT(1)); + +/** dst = src1 * scale + src2 */ +CVAPI(void) cvScaleAdd( const CvArr* src1, CvScalar scale, + const CvArr* src2, CvArr* dst ); +#define cvAXPY( A, real_scalar, B, C ) cvScaleAdd(A, cvRealScalar(real_scalar), B, C) + +/** dst = src1 * alpha + src2 * beta + gamma */ +CVAPI(void) cvAddWeighted( const CvArr* src1, double alpha, + const CvArr* src2, double beta, + double gamma, CvArr* dst ); + +/** @brief Calculates the dot product of two arrays in Euclidean metrics. + +The function calculates and returns the Euclidean dot product of two arrays. + +\f[src1 \bullet src2 = \sum _I ( \texttt{src1} (I) \texttt{src2} (I))\f] + +In the case of multiple channel arrays, the results for all channels are accumulated. In particular, +cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can +process multi-dimensional arrays, row by row, layer by layer, and so on. +@param src1 The first source array +@param src2 The second source array + */ +CVAPI(double) cvDotProduct( const CvArr* src1, const CvArr* src2 ); + +/** dst(idx) = src1(idx) & src2(idx) */ +CVAPI(void) cvAnd( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) & value */ +CVAPI(void) cvAndS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) | src2(idx) */ +CVAPI(void) cvOr( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) | value */ +CVAPI(void) cvOrS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) ^ src2(idx) */ +CVAPI(void) cvXor( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) ^ value */ +CVAPI(void) cvXorS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = ~src(idx) */ +CVAPI(void) cvNot( const CvArr* src, CvArr* dst ); + +/** dst(idx) = lower(idx) <= src(idx) < upper(idx) */ +CVAPI(void) cvInRange( const CvArr* src, const CvArr* lower, + const CvArr* upper, CvArr* dst ); + +/** dst(idx) = lower <= src(idx) < upper */ +CVAPI(void) cvInRangeS( const CvArr* src, CvScalar lower, + CvScalar upper, CvArr* dst ); + +#define CV_CMP_EQ 0 +#define CV_CMP_GT 1 +#define CV_CMP_GE 2 +#define CV_CMP_LT 3 +#define CV_CMP_LE 4 +#define CV_CMP_NE 5 + +/** The comparison operation support single-channel arrays only. + Destination image should be 8uC1 or 8sC1 */ + +/** dst(idx) = src1(idx) _cmp_op_ src2(idx) */ +CVAPI(void) cvCmp( const CvArr* src1, const CvArr* src2, CvArr* dst, int cmp_op ); + +/** dst(idx) = src1(idx) _cmp_op_ value */ +CVAPI(void) cvCmpS( const CvArr* src, double value, CvArr* dst, int cmp_op ); + +/** dst(idx) = min(src1(idx),src2(idx)) */ +CVAPI(void) cvMin( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(idx) = max(src1(idx),src2(idx)) */ +CVAPI(void) cvMax( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(idx) = min(src(idx),value) */ +CVAPI(void) cvMinS( const CvArr* src, double value, CvArr* dst ); + +/** dst(idx) = max(src(idx),value) */ +CVAPI(void) cvMaxS( const CvArr* src, double value, CvArr* dst ); + +/** dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c)) */ +CVAPI(void) cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(x,y,c) = abs(src(x,y,c) - value(c)) */ +CVAPI(void) cvAbsDiffS( const CvArr* src, CvArr* dst, CvScalar value ); +#define cvAbs( src, dst ) cvAbsDiffS( (src), (dst), cvScalarAll(0)) + +/****************************************************************************************\ +* Math operations * +\****************************************************************************************/ + +/** Does cartesian->polar coordinates conversion. + Either of output components (magnitude or angle) is optional */ +CVAPI(void) cvCartToPolar( const CvArr* x, const CvArr* y, + CvArr* magnitude, CvArr* angle CV_DEFAULT(NULL), + int angle_in_degrees CV_DEFAULT(0)); + +/** Does polar->cartesian coordinates conversion. + Either of output components (magnitude or angle) is optional. + If magnitude is missing it is assumed to be all 1's */ +CVAPI(void) cvPolarToCart( const CvArr* magnitude, const CvArr* angle, + CvArr* x, CvArr* y, + int angle_in_degrees CV_DEFAULT(0)); + +/** Does powering: dst(idx) = src(idx)^power */ +CVAPI(void) cvPow( const CvArr* src, CvArr* dst, double power ); + +/** Does exponention: dst(idx) = exp(src(idx)). + Overflow is not handled yet. Underflow is handled. + Maximal relative error is ~7e-6 for single-precision input */ +CVAPI(void) cvExp( const CvArr* src, CvArr* dst ); + +/** Calculates natural logarithms: dst(idx) = log(abs(src(idx))). + Logarithm of 0 gives large negative number(~-700) + Maximal relative error is ~3e-7 for single-precision output +*/ +CVAPI(void) cvLog( const CvArr* src, CvArr* dst ); + +/** Fast arctangent calculation */ +CVAPI(float) cvFastArctan( float y, float x ); + +/** Fast cubic root calculation */ +CVAPI(float) cvCbrt( float value ); + +#define CV_CHECK_RANGE 1 +#define CV_CHECK_QUIET 2 +/** Checks array values for NaNs, Infs or simply for too large numbers + (if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set, + no runtime errors is raised (function returns zero value in case of "bad" values). + Otherwise cvError is called */ +CVAPI(int) cvCheckArr( const CvArr* arr, int flags CV_DEFAULT(0), + double min_val CV_DEFAULT(0), double max_val CV_DEFAULT(0)); +#define cvCheckArray cvCheckArr + +#define CV_RAND_UNI 0 +#define CV_RAND_NORMAL 1 + +/** @brief Fills an array with random numbers and updates the RNG state. + +The function fills the destination array with uniformly or normally distributed random numbers. +@param rng CvRNG state initialized by cvRNG +@param arr The destination array +@param dist_type Distribution type +> - **CV_RAND_UNI** uniform distribution +> - **CV_RAND_NORMAL** normal or Gaussian distribution +@param param1 The first parameter of the distribution. In the case of a uniform distribution it is +the inclusive lower boundary of the random numbers range. In the case of a normal distribution it +is the mean value of the random numbers. +@param param2 The second parameter of the distribution. In the case of a uniform distribution it +is the exclusive upper boundary of the random numbers range. In the case of a normal distribution +it is the standard deviation of the random numbers. +@sa randu, randn, RNG::fill. + */ +CVAPI(void) cvRandArr( CvRNG* rng, CvArr* arr, int dist_type, + CvScalar param1, CvScalar param2 ); + +CVAPI(void) cvRandShuffle( CvArr* mat, CvRNG* rng, + double iter_factor CV_DEFAULT(1.)); + +#define CV_SORT_EVERY_ROW 0 +#define CV_SORT_EVERY_COLUMN 1 +#define CV_SORT_ASCENDING 0 +#define CV_SORT_DESCENDING 16 + +CVAPI(void) cvSort( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), + CvArr* idxmat CV_DEFAULT(NULL), + int flags CV_DEFAULT(0)); + +/** Finds real roots of a cubic equation */ +CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots ); + +/** Finds all real and complex roots of a polynomial equation */ +CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2, + int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100)); + +/****************************************************************************************\ +* Matrix operations * +\****************************************************************************************/ + +/** @brief Calculates the cross product of two 3D vectors. + +The function calculates the cross product of two 3D vectors: +\f[\texttt{dst} = \texttt{src1} \times \texttt{src2}\f] +or: +\f[\begin{array}{l} \texttt{dst} _1 = \texttt{src1} _2 \texttt{src2} _3 - \texttt{src1} _3 \texttt{src2} _2 \\ \texttt{dst} _2 = \texttt{src1} _3 \texttt{src2} _1 - \texttt{src1} _1 \texttt{src2} _3 \\ \texttt{dst} _3 = \texttt{src1} _1 \texttt{src2} _2 - \texttt{src1} _2 \texttt{src2} _1 \end{array}\f] +@param src1 The first source vector +@param src2 The second source vector +@param dst The destination vector + */ +CVAPI(void) cvCrossProduct( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** Matrix transform: dst = A*B + C, C is optional */ +#define cvMatMulAdd( src1, src2, src3, dst ) cvGEMM( (src1), (src2), 1., (src3), 1., (dst), 0 ) +#define cvMatMul( src1, src2, dst ) cvMatMulAdd( (src1), (src2), NULL, (dst)) + +#define CV_GEMM_A_T 1 +#define CV_GEMM_B_T 2 +#define CV_GEMM_C_T 4 +/** Extended matrix transform: + dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T */ +CVAPI(void) cvGEMM( const CvArr* src1, const CvArr* src2, double alpha, + const CvArr* src3, double beta, CvArr* dst, + int tABC CV_DEFAULT(0)); +#define cvMatMulAddEx cvGEMM + +/** Transforms each element of source array and stores + resultant vectors in destination array */ +CVAPI(void) cvTransform( const CvArr* src, CvArr* dst, + const CvMat* transmat, + const CvMat* shiftvec CV_DEFAULT(NULL)); +#define cvMatMulAddS cvTransform + +/** Does perspective transform on every element of input array */ +CVAPI(void) cvPerspectiveTransform( const CvArr* src, CvArr* dst, + const CvMat* mat ); + +/** Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1) */ +CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order, + const CvArr* delta CV_DEFAULT(NULL), + double scale CV_DEFAULT(1.) ); + +/** Tranposes matrix. Square matrices can be transposed in-place */ +CVAPI(void) cvTranspose( const CvArr* src, CvArr* dst ); +#define cvT cvTranspose + +/** Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part */ +CVAPI(void) cvCompleteSymm( CvMat* matrix, int LtoR CV_DEFAULT(0) ); + +/** Mirror array data around horizontal (flip=0), + vertical (flip=1) or both(flip=-1) axises: + cvFlip(src) flips images vertically and sequences horizontally (inplace) */ +CVAPI(void) cvFlip( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), + int flip_mode CV_DEFAULT(0)); +#define cvMirror cvFlip + + +#define CV_SVD_MODIFY_A 1 +#define CV_SVD_U_T 2 +#define CV_SVD_V_T 4 + +/** Performs Singular Value Decomposition of a matrix */ +CVAPI(void) cvSVD( CvArr* A, CvArr* W, CvArr* U CV_DEFAULT(NULL), + CvArr* V CV_DEFAULT(NULL), int flags CV_DEFAULT(0)); + +/** Performs Singular Value Back Substitution (solves A*X = B): + flags must be the same as in cvSVD */ +CVAPI(void) cvSVBkSb( const CvArr* W, const CvArr* U, + const CvArr* V, const CvArr* B, + CvArr* X, int flags ); + +#define CV_LU 0 +#define CV_SVD 1 +#define CV_SVD_SYM 2 +#define CV_CHOLESKY 3 +#define CV_QR 4 +#define CV_NORMAL 16 + +/** Inverts matrix */ +CVAPI(double) cvInvert( const CvArr* src, CvArr* dst, + int method CV_DEFAULT(CV_LU)); +#define cvInv cvInvert + +/** Solves linear system (src1)*(dst) = (src2) + (returns 0 if src1 is a singular and CV_LU method is used) */ +CVAPI(int) cvSolve( const CvArr* src1, const CvArr* src2, CvArr* dst, + int method CV_DEFAULT(CV_LU)); + +/** Calculates determinant of input matrix */ +CVAPI(double) cvDet( const CvArr* mat ); + +/** Calculates trace of the matrix (sum of elements on the main diagonal) */ +CVAPI(CvScalar) cvTrace( const CvArr* mat ); + +/** Finds eigen values and vectors of a symmetric matrix */ +CVAPI(void) cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, + double eps CV_DEFAULT(0), + int lowindex CV_DEFAULT(-1), + int highindex CV_DEFAULT(-1)); + +///* Finds selected eigen values and vectors of a symmetric matrix */ +//CVAPI(void) cvSelectedEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, +// int lowindex, int highindex ); + +/** Makes an identity matrix (mat_ij = i == j) */ +CVAPI(void) cvSetIdentity( CvArr* mat, CvScalar value CV_DEFAULT(cvRealScalar(1)) ); + +/** Fills matrix with given range of numbers */ +CVAPI(CvArr*) cvRange( CvArr* mat, double start, double end ); + +/** @anchor core_c_CovarFlags +@name Flags for cvCalcCovarMatrix +@see cvCalcCovarMatrix + @{ +*/ + +/** flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...] */ +#define CV_COVAR_SCRAMBLED 0 + +/** flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...]) */ +#define CV_COVAR_NORMAL 1 + +/** flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead + (useful for calculating covariance matrix by parts) */ +#define CV_COVAR_USE_AVG 2 + +/** flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors */ +#define CV_COVAR_SCALE 4 + +/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows */ +#define CV_COVAR_ROWS 8 + +/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns */ +#define CV_COVAR_COLS 16 + +/** @} */ + +/** Calculates covariation matrix for a set of vectors +@see @ref core_c_CovarFlags "flags" +*/ +CVAPI(void) cvCalcCovarMatrix( const CvArr** vects, int count, + CvArr* cov_mat, CvArr* avg, int flags ); + +#define CV_PCA_DATA_AS_ROW 0 +#define CV_PCA_DATA_AS_COL 1 +#define CV_PCA_USE_AVG 2 +CVAPI(void) cvCalcPCA( const CvArr* data, CvArr* mean, + CvArr* eigenvals, CvArr* eigenvects, int flags ); + +CVAPI(void) cvProjectPCA( const CvArr* data, const CvArr* mean, + const CvArr* eigenvects, CvArr* result ); + +CVAPI(void) cvBackProjectPCA( const CvArr* proj, const CvArr* mean, + const CvArr* eigenvects, CvArr* result ); + +/** Calculates Mahalanobis(weighted) distance */ +CVAPI(double) cvMahalanobis( const CvArr* vec1, const CvArr* vec2, const CvArr* mat ); +#define cvMahalonobis cvMahalanobis + +/****************************************************************************************\ +* Array Statistics * +\****************************************************************************************/ + +/** Finds sum of array elements */ +CVAPI(CvScalar) cvSum( const CvArr* arr ); + +/** Calculates number of non-zero pixels */ +CVAPI(int) cvCountNonZero( const CvArr* arr ); + +/** Calculates mean value of array elements */ +CVAPI(CvScalar) cvAvg( const CvArr* arr, const CvArr* mask CV_DEFAULT(NULL) ); + +/** Calculates mean and standard deviation of pixel values */ +CVAPI(void) cvAvgSdv( const CvArr* arr, CvScalar* mean, CvScalar* std_dev, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** Finds global minimum, maximum and their positions */ +CVAPI(void) cvMinMaxLoc( const CvArr* arr, double* min_val, double* max_val, + CvPoint* min_loc CV_DEFAULT(NULL), + CvPoint* max_loc CV_DEFAULT(NULL), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @anchor core_c_NormFlags + @name Flags for cvNorm and cvNormalize + @{ +*/ +#define CV_C 1 +#define CV_L1 2 +#define CV_L2 4 +#define CV_NORM_MASK 7 +#define CV_RELATIVE 8 +#define CV_DIFF 16 +#define CV_MINMAX 32 + +#define CV_DIFF_C (CV_DIFF | CV_C) +#define CV_DIFF_L1 (CV_DIFF | CV_L1) +#define CV_DIFF_L2 (CV_DIFF | CV_L2) +#define CV_RELATIVE_C (CV_RELATIVE | CV_C) +#define CV_RELATIVE_L1 (CV_RELATIVE | CV_L1) +#define CV_RELATIVE_L2 (CV_RELATIVE | CV_L2) +/** @} */ + +/** Finds norm, difference norm or relative difference norm for an array (or two arrays) +@see ref core_c_NormFlags "flags" +*/ +CVAPI(double) cvNorm( const CvArr* arr1, const CvArr* arr2 CV_DEFAULT(NULL), + int norm_type CV_DEFAULT(CV_L2), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @see ref core_c_NormFlags "flags" */ +CVAPI(void) cvNormalize( const CvArr* src, CvArr* dst, + double a CV_DEFAULT(1.), double b CV_DEFAULT(0.), + int norm_type CV_DEFAULT(CV_L2), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @anchor core_c_ReduceFlags + @name Flags for cvReduce + @{ +*/ +#define CV_REDUCE_SUM 0 +#define CV_REDUCE_AVG 1 +#define CV_REDUCE_MAX 2 +#define CV_REDUCE_MIN 3 +/** @} */ + +/** @see @ref core_c_ReduceFlags "flags" */ +CVAPI(void) cvReduce( const CvArr* src, CvArr* dst, int dim CV_DEFAULT(-1), + int op CV_DEFAULT(CV_REDUCE_SUM) ); + +/****************************************************************************************\ +* Discrete Linear Transforms and Related Functions * +\****************************************************************************************/ + +/** @anchor core_c_DftFlags + @name Flags for cvDFT, cvDCT and cvMulSpectrums + @{ + */ +#define CV_DXT_FORWARD 0 +#define CV_DXT_INVERSE 1 +#define CV_DXT_SCALE 2 /**< divide result by size of array */ +#define CV_DXT_INV_SCALE (CV_DXT_INVERSE + CV_DXT_SCALE) +#define CV_DXT_INVERSE_SCALE CV_DXT_INV_SCALE +#define CV_DXT_ROWS 4 /**< transform each row individually */ +#define CV_DXT_MUL_CONJ 8 /**< conjugate the second argument of cvMulSpectrums */ +/** @} */ + +/** Discrete Fourier Transform: + complex->complex, + real->ccs (forward), + ccs->real (inverse) +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvDFT( const CvArr* src, CvArr* dst, int flags, + int nonzero_rows CV_DEFAULT(0) ); +#define cvFFT cvDFT + +/** Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y)) +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvMulSpectrums( const CvArr* src1, const CvArr* src2, + CvArr* dst, int flags ); + +/** Finds optimal DFT vector size >= size0 */ +CVAPI(int) cvGetOptimalDFTSize( int size0 ); + +/** Discrete Cosine Transform +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvDCT( const CvArr* src, CvArr* dst, int flags ); + +/****************************************************************************************\ +* Dynamic data structures * +\****************************************************************************************/ + +/** Calculates length of sequence slice (with support of negative indices). */ +CVAPI(int) cvSliceLength( CvSlice slice, const CvSeq* seq ); + + +/** Creates new memory storage. + block_size == 0 means that default, + somewhat optimal size, is used (currently, it is 64K) */ +CVAPI(CvMemStorage*) cvCreateMemStorage( int block_size CV_DEFAULT(0)); + + +/** Creates a memory storage that will borrow memory blocks from parent storage */ +CVAPI(CvMemStorage*) cvCreateChildMemStorage( CvMemStorage* parent ); + + +/** Releases memory storage. All the children of a parent must be released before + the parent. A child storage returns all the blocks to parent when it is released */ +CVAPI(void) cvReleaseMemStorage( CvMemStorage** storage ); + + +/** Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos) + to reuse memory allocated for the storage - cvClearSeq,cvClearSet ... + do not free any memory. + A child storage returns all the blocks to the parent when it is cleared */ +CVAPI(void) cvClearMemStorage( CvMemStorage* storage ); + +/** Remember a storage "free memory" position */ +CVAPI(void) cvSaveMemStoragePos( const CvMemStorage* storage, CvMemStoragePos* pos ); + +/** Restore a storage "free memory" position */ +CVAPI(void) cvRestoreMemStoragePos( CvMemStorage* storage, CvMemStoragePos* pos ); + +/** Allocates continuous buffer of the specified size in the storage */ +CVAPI(void*) cvMemStorageAlloc( CvMemStorage* storage, size_t size ); + +/** Allocates string in memory storage */ +CVAPI(CvString) cvMemStorageAllocString( CvMemStorage* storage, const char* ptr, + int len CV_DEFAULT(-1) ); + +/** Creates new empty sequence that will reside in the specified storage */ +CVAPI(CvSeq*) cvCreateSeq( int seq_flags, size_t header_size, + size_t elem_size, CvMemStorage* storage ); + +/** Changes default size (granularity) of sequence blocks. + The default size is ~1Kbyte */ +CVAPI(void) cvSetSeqBlockSize( CvSeq* seq, int delta_elems ); + + +/** Adds new element to the end of sequence. Returns pointer to the element */ +CVAPI(schar*) cvSeqPush( CvSeq* seq, const void* element CV_DEFAULT(NULL)); + + +/** Adds new element to the beginning of sequence. Returns pointer to it */ +CVAPI(schar*) cvSeqPushFront( CvSeq* seq, const void* element CV_DEFAULT(NULL)); + + +/** Removes the last element from sequence and optionally saves it */ +CVAPI(void) cvSeqPop( CvSeq* seq, void* element CV_DEFAULT(NULL)); + + +/** Removes the first element from sequence and optioanally saves it */ +CVAPI(void) cvSeqPopFront( CvSeq* seq, void* element CV_DEFAULT(NULL)); + + +#define CV_FRONT 1 +#define CV_BACK 0 +/** Adds several new elements to the end of sequence */ +CVAPI(void) cvSeqPushMulti( CvSeq* seq, const void* elements, + int count, int in_front CV_DEFAULT(0) ); + +/** Removes several elements from the end of sequence and optionally saves them */ +CVAPI(void) cvSeqPopMulti( CvSeq* seq, void* elements, + int count, int in_front CV_DEFAULT(0) ); + +/** Inserts a new element in the middle of sequence. + cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem) */ +CVAPI(schar*) cvSeqInsert( CvSeq* seq, int before_index, + const void* element CV_DEFAULT(NULL)); + +/** Removes specified sequence element */ +CVAPI(void) cvSeqRemove( CvSeq* seq, int index ); + + +/** Removes all the elements from the sequence. The freed memory + can be reused later only by the same sequence unless cvClearMemStorage + or cvRestoreMemStoragePos is called */ +CVAPI(void) cvClearSeq( CvSeq* seq ); + + +/** Retrieves pointer to specified sequence element. + Negative indices are supported and mean counting from the end + (e.g -1 means the last sequence element) */ +CVAPI(schar*) cvGetSeqElem( const CvSeq* seq, int index ); + +/** Calculates index of the specified sequence element. + Returns -1 if element does not belong to the sequence */ +CVAPI(int) cvSeqElemIdx( const CvSeq* seq, const void* element, + CvSeqBlock** block CV_DEFAULT(NULL) ); + +/** Initializes sequence writer. The new elements will be added to the end of sequence */ +CVAPI(void) cvStartAppendToSeq( CvSeq* seq, CvSeqWriter* writer ); + + +/** Combination of cvCreateSeq and cvStartAppendToSeq */ +CVAPI(void) cvStartWriteSeq( int seq_flags, int header_size, + int elem_size, CvMemStorage* storage, + CvSeqWriter* writer ); + +/** Closes sequence writer, updates sequence header and returns pointer + to the resultant sequence + (which may be useful if the sequence was created using cvStartWriteSeq)) +*/ +CVAPI(CvSeq*) cvEndWriteSeq( CvSeqWriter* writer ); + + +/** Updates sequence header. May be useful to get access to some of previously + written elements via cvGetSeqElem or sequence reader */ +CVAPI(void) cvFlushSeqWriter( CvSeqWriter* writer ); + + +/** Initializes sequence reader. + The sequence can be read in forward or backward direction */ +CVAPI(void) cvStartReadSeq( const CvSeq* seq, CvSeqReader* reader, + int reverse CV_DEFAULT(0) ); + + +/** Returns current sequence reader position (currently observed sequence element) */ +CVAPI(int) cvGetSeqReaderPos( CvSeqReader* reader ); + + +/** Changes sequence reader position. It may seek to an absolute or + to relative to the current position */ +CVAPI(void) cvSetSeqReaderPos( CvSeqReader* reader, int index, + int is_relative CV_DEFAULT(0)); + +/** Copies sequence content to a continuous piece of memory */ +CVAPI(void*) cvCvtSeqToArray( const CvSeq* seq, void* elements, + CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ) ); + +/** Creates sequence header for array. + After that all the operations on sequences that do not alter the content + can be applied to the resultant sequence */ +CVAPI(CvSeq*) cvMakeSeqHeaderForArray( int seq_type, int header_size, + int elem_size, void* elements, int total, + CvSeq* seq, CvSeqBlock* block ); + +/** Extracts sequence slice (with or without copying sequence elements) */ +CVAPI(CvSeq*) cvSeqSlice( const CvSeq* seq, CvSlice slice, + CvMemStorage* storage CV_DEFAULT(NULL), + int copy_data CV_DEFAULT(0)); + +CV_INLINE CvSeq* cvCloneSeq( const CvSeq* seq, CvMemStorage* storage CV_DEFAULT(NULL)) +{ + return cvSeqSlice( seq, CV_WHOLE_SEQ, storage, 1 ); +} + +/** Removes sequence slice */ +CVAPI(void) cvSeqRemoveSlice( CvSeq* seq, CvSlice slice ); + +/** Inserts a sequence or array into another sequence */ +CVAPI(void) cvSeqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); + +/** a < b ? -1 : a > b ? 1 : 0 */ +typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata ); + +/** Sorts sequence in-place given element comparison function */ +CVAPI(void) cvSeqSort( CvSeq* seq, CvCmpFunc func, void* userdata CV_DEFAULT(NULL) ); + +/** Finds element in a [sorted] sequence */ +CVAPI(schar*) cvSeqSearch( CvSeq* seq, const void* elem, CvCmpFunc func, + int is_sorted, int* elem_idx, + void* userdata CV_DEFAULT(NULL) ); + +/** Reverses order of sequence elements in-place */ +CVAPI(void) cvSeqInvert( CvSeq* seq ); + +/** Splits sequence into one or more equivalence classes using the specified criteria */ +CVAPI(int) cvSeqPartition( const CvSeq* seq, CvMemStorage* storage, + CvSeq** labels, CvCmpFunc is_equal, void* userdata ); + +/************ Internal sequence functions ************/ +CVAPI(void) cvChangeSeqBlock( void* reader, int direction ); +CVAPI(void) cvCreateSeqBlock( CvSeqWriter* writer ); + + +/** Creates a new set */ +CVAPI(CvSet*) cvCreateSet( int set_flags, int header_size, + int elem_size, CvMemStorage* storage ); + +/** Adds new element to the set and returns pointer to it */ +CVAPI(int) cvSetAdd( CvSet* set_header, CvSetElem* elem CV_DEFAULT(NULL), + CvSetElem** inserted_elem CV_DEFAULT(NULL) ); + +/** Fast variant of cvSetAdd */ +CV_INLINE CvSetElem* cvSetNew( CvSet* set_header ) +{ + CvSetElem* elem = set_header->free_elems; + if( elem ) + { + set_header->free_elems = elem->next_free; + elem->flags = elem->flags & CV_SET_ELEM_IDX_MASK; + set_header->active_count++; + } + else + cvSetAdd( set_header, NULL, &elem ); + return elem; +} + +/** Removes set element given its pointer */ +CV_INLINE void cvSetRemoveByPtr( CvSet* set_header, void* elem ) +{ + CvSetElem* _elem = (CvSetElem*)elem; + assert( _elem->flags >= 0 /*&& (elem->flags & CV_SET_ELEM_IDX_MASK) < set_header->total*/ ); + _elem->next_free = set_header->free_elems; + _elem->flags = (_elem->flags & CV_SET_ELEM_IDX_MASK) | CV_SET_ELEM_FREE_FLAG; + set_header->free_elems = _elem; + set_header->active_count--; +} + +/** Removes element from the set by its index */ +CVAPI(void) cvSetRemove( CvSet* set_header, int index ); + +/** Returns a set element by index. If the element doesn't belong to the set, + NULL is returned */ +CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx ) +{ + CvSetElem* elem = (CvSetElem*)(void *)cvGetSeqElem( (CvSeq*)set_header, idx ); + return elem && CV_IS_SET_ELEM( elem ) ? elem : 0; +} + +/** Removes all the elements from the set */ +CVAPI(void) cvClearSet( CvSet* set_header ); + +/** Creates new graph */ +CVAPI(CvGraph*) cvCreateGraph( int graph_flags, int header_size, + int vtx_size, int edge_size, + CvMemStorage* storage ); + +/** Adds new vertex to the graph */ +CVAPI(int) cvGraphAddVtx( CvGraph* graph, const CvGraphVtx* vtx CV_DEFAULT(NULL), + CvGraphVtx** inserted_vtx CV_DEFAULT(NULL) ); + + +/** Removes vertex from the graph together with all incident edges */ +CVAPI(int) cvGraphRemoveVtx( CvGraph* graph, int index ); +CVAPI(int) cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx ); + + +/** Link two vertices specifed by indices or pointers if they + are not connected or return pointer to already existing edge + connecting the vertices. + Functions return 1 if a new edge was created, 0 otherwise */ +CVAPI(int) cvGraphAddEdge( CvGraph* graph, + int start_idx, int end_idx, + const CvGraphEdge* edge CV_DEFAULT(NULL), + CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); + +CVAPI(int) cvGraphAddEdgeByPtr( CvGraph* graph, + CvGraphVtx* start_vtx, CvGraphVtx* end_vtx, + const CvGraphEdge* edge CV_DEFAULT(NULL), + CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); + +/** Remove edge connecting two vertices */ +CVAPI(void) cvGraphRemoveEdge( CvGraph* graph, int start_idx, int end_idx ); +CVAPI(void) cvGraphRemoveEdgeByPtr( CvGraph* graph, CvGraphVtx* start_vtx, + CvGraphVtx* end_vtx ); + +/** Find edge connecting two vertices */ +CVAPI(CvGraphEdge*) cvFindGraphEdge( const CvGraph* graph, int start_idx, int end_idx ); +CVAPI(CvGraphEdge*) cvFindGraphEdgeByPtr( const CvGraph* graph, + const CvGraphVtx* start_vtx, + const CvGraphVtx* end_vtx ); +#define cvGraphFindEdge cvFindGraphEdge +#define cvGraphFindEdgeByPtr cvFindGraphEdgeByPtr + +/** Remove all vertices and edges from the graph */ +CVAPI(void) cvClearGraph( CvGraph* graph ); + + +/** Count number of edges incident to the vertex */ +CVAPI(int) cvGraphVtxDegree( const CvGraph* graph, int vtx_idx ); +CVAPI(int) cvGraphVtxDegreeByPtr( const CvGraph* graph, const CvGraphVtx* vtx ); + + +/** Retrieves graph vertex by given index */ +#define cvGetGraphVtx( graph, idx ) (CvGraphVtx*)cvGetSetElem((CvSet*)(graph), (idx)) + +/** Retrieves index of a graph vertex given its pointer */ +#define cvGraphVtxIdx( graph, vtx ) ((vtx)->flags & CV_SET_ELEM_IDX_MASK) + +/** Retrieves index of a graph edge given its pointer */ +#define cvGraphEdgeIdx( graph, edge ) ((edge)->flags & CV_SET_ELEM_IDX_MASK) + +#define cvGraphGetVtxCount( graph ) ((graph)->active_count) +#define cvGraphGetEdgeCount( graph ) ((graph)->edges->active_count) + +#define CV_GRAPH_VERTEX 1 +#define CV_GRAPH_TREE_EDGE 2 +#define CV_GRAPH_BACK_EDGE 4 +#define CV_GRAPH_FORWARD_EDGE 8 +#define CV_GRAPH_CROSS_EDGE 16 +#define CV_GRAPH_ANY_EDGE 30 +#define CV_GRAPH_NEW_TREE 32 +#define CV_GRAPH_BACKTRACKING 64 +#define CV_GRAPH_OVER -1 + +#define CV_GRAPH_ALL_ITEMS -1 + +/** flags for graph vertices and edges */ +#define CV_GRAPH_ITEM_VISITED_FLAG (1 << 30) +#define CV_IS_GRAPH_VERTEX_VISITED(vtx) \ + (((CvGraphVtx*)(vtx))->flags & CV_GRAPH_ITEM_VISITED_FLAG) +#define CV_IS_GRAPH_EDGE_VISITED(edge) \ + (((CvGraphEdge*)(edge))->flags & CV_GRAPH_ITEM_VISITED_FLAG) +#define CV_GRAPH_SEARCH_TREE_NODE_FLAG (1 << 29) +#define CV_GRAPH_FORWARD_EDGE_FLAG (1 << 28) + +typedef struct CvGraphScanner +{ + CvGraphVtx* vtx; /* current graph vertex (or current edge origin) */ + CvGraphVtx* dst; /* current graph edge destination vertex */ + CvGraphEdge* edge; /* current edge */ + + CvGraph* graph; /* the graph */ + CvSeq* stack; /* the graph vertex stack */ + int index; /* the lower bound of certainly visited vertices */ + int mask; /* event mask */ +} +CvGraphScanner; + +/** Creates new graph scanner. */ +CVAPI(CvGraphScanner*) cvCreateGraphScanner( CvGraph* graph, + CvGraphVtx* vtx CV_DEFAULT(NULL), + int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS)); + +/** Releases graph scanner. */ +CVAPI(void) cvReleaseGraphScanner( CvGraphScanner** scanner ); + +/** Get next graph element */ +CVAPI(int) cvNextGraphItem( CvGraphScanner* scanner ); + +/** Creates a copy of graph */ +CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage ); + + +/** Does look-up transformation. Elements of the source array + (that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */ +CVAPI(void) cvLUT( const CvArr* src, CvArr* dst, const CvArr* lut ); + + +/******************* Iteration through the sequence tree *****************/ +typedef struct CvTreeNodeIterator +{ + const void* node; + int level; + int max_level; +} +CvTreeNodeIterator; + +CVAPI(void) cvInitTreeNodeIterator( CvTreeNodeIterator* tree_iterator, + const void* first, int max_level ); +CVAPI(void*) cvNextTreeNode( CvTreeNodeIterator* tree_iterator ); +CVAPI(void*) cvPrevTreeNode( CvTreeNodeIterator* tree_iterator ); + +/** Inserts sequence into tree with specified "parent" sequence. + If parent is equal to frame (e.g. the most external contour), + then added contour will have null pointer to parent. */ +CVAPI(void) cvInsertNodeIntoTree( void* node, void* parent, void* frame ); + +/** Removes contour from tree (together with the contour children). */ +CVAPI(void) cvRemoveNodeFromTree( void* node, void* frame ); + +/** Gathers pointers to all the sequences, + accessible from the `first`, to the single sequence */ +CVAPI(CvSeq*) cvTreeToNodeSeq( const void* first, int header_size, + CvMemStorage* storage ); + +/** The function implements the K-means algorithm for clustering an array of sample + vectors in a specified number of classes */ +#define CV_KMEANS_USE_INITIAL_LABELS 1 +CVAPI(int) cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels, + CvTermCriteria termcrit, int attempts CV_DEFAULT(1), + CvRNG* rng CV_DEFAULT(0), int flags CV_DEFAULT(0), + CvArr* _centers CV_DEFAULT(0), double* compactness CV_DEFAULT(0) ); + +/****************************************************************************************\ +* System functions * +\****************************************************************************************/ + +/** Loads optimized functions from IPP, MKL etc. or switches back to pure C code */ +CVAPI(int) cvUseOptimized( int on_off ); + +typedef IplImage* (CV_STDCALL* Cv_iplCreateImageHeader) + (int,int,int,char*,char*,int,int,int,int,int, + IplROI*,IplImage*,void*,IplTileInfo*); +typedef void (CV_STDCALL* Cv_iplAllocateImageData)(IplImage*,int,int); +typedef void (CV_STDCALL* Cv_iplDeallocate)(IplImage*,int); +typedef IplROI* (CV_STDCALL* Cv_iplCreateROI)(int,int,int,int,int); +typedef IplImage* (CV_STDCALL* Cv_iplCloneImage)(const IplImage*); + +/** @brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures. + +Normally, the function is not called directly. Instead, a simple macro +CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers +to IPL allocation functions. : +@code + ... + CV_TURN_ON_IPL_COMPATIBILITY() + ... +@endcode +@param create_header pointer to a function, creating IPL image header. +@param allocate_data pointer to a function, allocating IPL image data. +@param deallocate pointer to a function, deallocating IPL image. +@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest). +@param clone_image pointer to a function, cloning an IPL image. + */ +CVAPI(void) cvSetIPLAllocators( Cv_iplCreateImageHeader create_header, + Cv_iplAllocateImageData allocate_data, + Cv_iplDeallocate deallocate, + Cv_iplCreateROI create_roi, + Cv_iplCloneImage clone_image ); + +#define CV_TURN_ON_IPL_COMPATIBILITY() \ + cvSetIPLAllocators( iplCreateImageHeader, iplAllocateImage, \ + iplDeallocate, iplCreateROI, iplCloneImage ) + +/****************************************************************************************\ +* Data Persistence * +\****************************************************************************************/ + +/********************************** High-level functions ********************************/ + +/** @brief Opens file storage for reading or writing data. + +The function opens file storage for reading or writing data. In the latter case, a new file is +created or an existing file is rewritten. The type of the read or written file is determined by the +filename extension: .xml for XML, .yml or .yaml for YAML and .json for JSON. + +At the same time, it also supports adding parameters like "example.xml?base64". The three ways +are the same: +@snippet samples/cpp/filestorage_base64.cpp suffix_in_file_name +@snippet samples/cpp/filestorage_base64.cpp flag_write_base64 +@snippet samples/cpp/filestorage_base64.cpp flag_write_and_flag_base64 + +The function returns a pointer to the CvFileStorage structure. +If the file cannot be opened then the function returns NULL. +@param filename Name of the file associated with the storage +@param memstorage Memory storage used for temporary data and for +: storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory + storage is created and used. +@param flags Can be one of the following: +> - **CV_STORAGE_READ** the storage is open for reading +> - **CV_STORAGE_WRITE** the storage is open for writing + (use **CV_STORAGE_WRITE | CV_STORAGE_WRITE_BASE64** to write rawdata in Base64) +@param encoding + */ +CVAPI(CvFileStorage*) cvOpenFileStorage( const char* filename, CvMemStorage* memstorage, + int flags, const char* encoding CV_DEFAULT(NULL) ); + +/** @brief Releases file storage. + +The function closes the file associated with the storage and releases all the temporary structures. +It must be called after all I/O operations with the storage are finished. +@param fs Double pointer to the released file storage + */ +CVAPI(void) cvReleaseFileStorage( CvFileStorage** fs ); + +/** returns attribute value or 0 (NULL) if there is no such attribute */ +CVAPI(const char*) cvAttrValue( const CvAttrList* attr, const char* attr_name ); + +/** @brief Starts writing a new structure. + +The function starts writing a compound structure (collection) that can be a sequence or a map. After +all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should +be called. The function can be used to group some objects or to implement the write function for a +some user object (see CvTypeInfo). +@param fs File storage +@param name Name of the written structure. The structure can be accessed by this name when the +storage is read. +@param struct_flags A combination one of the following values: +- **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ), + that is, its elements do not have a name. +- **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that + is, all its elements have names. +One and only one of the two above flags must be specified +- **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that + the structure is written as a flow (not as a block), which is more compact. It is + recommended to use this flag for structures or arrays whose elements are all scalars. +@param type_name Optional parameter - the object type name. In + case of XML it is written as a type_id attribute of the structure opening tag. In the case of + YAML it is written after a colon following the structure name (see the example in + CvFileStorage description). In case of JSON it is written as a name/value pair. + Mainly it is used with user objects. When the storage is read, the + encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ). +@param attributes This parameter is not used in the current implementation + */ +CVAPI(void) cvStartWriteStruct( CvFileStorage* fs, const char* name, + int struct_flags, const char* type_name CV_DEFAULT(NULL), + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Finishes writing to a file node collection. +@param fs File storage +@sa cvStartWriteStruct. + */ +CVAPI(void) cvEndWriteStruct( CvFileStorage* fs ); + +/** @brief Writes an integer value. + +The function writes a single integer value (with or without a name) to the file storage. +@param fs File storage +@param name Name of the written value. Should be NULL if and only if the parent structure is a +sequence. +@param value The written value + */ +CVAPI(void) cvWriteInt( CvFileStorage* fs, const char* name, int value ); + +/** @brief Writes a floating-point value. + +The function writes a single floating-point value (with or without a name) to file storage. Special +values are encoded as follows: NaN (Not A Number) as .NaN, infinity as +.Inf or -.Inf. + +The following example shows how to use the low-level writing functions to store custom structures, +such as termination criteria, without registering a new type. : +@code + void write_termcriteria( CvFileStorage* fs, const char* struct_name, + CvTermCriteria* termcrit ) + { + cvStartWriteStruct( fs, struct_name, CV_NODE_MAP, NULL, cvAttrList(0,0)); + cvWriteComment( fs, "termination criteria", 1 ); // just a description + if( termcrit->type & CV_TERMCRIT_ITER ) + cvWriteInteger( fs, "max_iterations", termcrit->max_iter ); + if( termcrit->type & CV_TERMCRIT_EPS ) + cvWriteReal( fs, "accuracy", termcrit->epsilon ); + cvEndWriteStruct( fs ); + } +@endcode +@param fs File storage +@param name Name of the written value. Should be NULL if and only if the parent structure is a +sequence. +@param value The written value +*/ +CVAPI(void) cvWriteReal( CvFileStorage* fs, const char* name, double value ); + +/** @brief Writes a text string. + +The function writes a text string to file storage. +@param fs File storage +@param name Name of the written string . Should be NULL if and only if the parent structure is a +sequence. +@param str The written text string +@param quote If non-zero, the written string is put in quotes, regardless of whether they are +required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when +the string starts with a digit or contains spaces). + */ +CVAPI(void) cvWriteString( CvFileStorage* fs, const char* name, + const char* str, int quote CV_DEFAULT(0) ); + +/** @brief Writes a comment. + +The function writes a comment into file storage. The comments are skipped when the storage is read. +@param fs File storage +@param comment The written comment, single-line or multi-line +@param eol_comment If non-zero, the function tries to put the comment at the end of current line. +If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current +line, the comment starts a new line. + */ +CVAPI(void) cvWriteComment( CvFileStorage* fs, const char* comment, + int eol_comment ); + +/** @brief Writes an object to file storage. + +The function writes an object to file storage. First, the appropriate type info is found using +cvTypeOf. Then, the write method associated with the type info is called. + +Attributes are used to customize the writing procedure. The standard types support the following +attributes (all the dt attributes have the same format as in cvWriteRawData): + +-# CvSeq + - **header_dt** description of user fields of the sequence header that follow CvSeq, or + CvChain (if the sequence is a Freeman chain) or CvContour (if the sequence is a contour or + point sequence) + - **dt** description of the sequence elements. + - **recursive** if the attribute is present and is not equal to "0" or "false", the whole + tree of sequences (contours) is stored. +-# CvGraph + - **header_dt** description of user fields of the graph header that follows CvGraph; + - **vertex_dt** description of user fields of graph vertices + - **edge_dt** description of user fields of graph edges (note that the edge weight is + always written, so there is no need to specify it explicitly) + +Below is the code that creates the YAML file shown in the CvFileStorage description: +@code + #include "cxcore.h" + + int main( int argc, char** argv ) + { + CvMat* mat = cvCreateMat( 3, 3, CV_32F ); + CvFileStorage* fs = cvOpenFileStorage( "example.yml", 0, CV_STORAGE_WRITE ); + + cvSetIdentity( mat ); + cvWrite( fs, "A", mat, cvAttrList(0,0) ); + + cvReleaseFileStorage( &fs ); + cvReleaseMat( &mat ); + return 0; + } +@endcode +@param fs File storage +@param name Name of the written object. Should be NULL if and only if the parent structure is a +sequence. +@param ptr Pointer to the object +@param attributes The attributes of the object. They are specific for each particular type (see +the discussion below). + */ +CVAPI(void) cvWrite( CvFileStorage* fs, const char* name, const void* ptr, + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Starts the next stream. + +The function finishes the currently written stream and starts the next stream. In the case of XML +the file with multiple streams looks like this: +@code{.xml} + + + + + + + ... +@endcode +The YAML file will look like this: +@code{.yaml} + %YAML 1.0 + # stream #1 data + ... + --- + # stream #2 data +@endcode +This is useful for concatenating files or for resuming the writing process. +@param fs File storage + */ +CVAPI(void) cvStartNextStream( CvFileStorage* fs ); + +/** @brief Writes multiple numbers. + +The function writes an array, whose elements consist of single or multiple numbers. The function +call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single +call is more efficient. Note that because none of the elements have a name, they should be written +to a sequence rather than a map. +@param fs File storage +@param src Pointer to the written array +@param len Number of the array elements to write +@param dt Specification of each array element, see @ref format_spec "format specification" + */ +CVAPI(void) cvWriteRawData( CvFileStorage* fs, const void* src, + int len, const char* dt ); + +/** @brief Writes multiple numbers in Base64. + +If either CV_STORAGE_WRITE_BASE64 or cv::FileStorage::WRITE_BASE64 is used, +this function will be the same as cvWriteRawData. If neither, the main +difference is that it outputs a sequence in Base64 encoding rather than +in plain text. + +This function can only be used to write a sequence with a type "binary". + +Consider the following two examples where their output is the same: +@snippet samples/cpp/filestorage_base64.cpp without_base64_flag +and +@snippet samples/cpp/filestorage_base64.cpp with_write_base64_flag + +@param fs File storage +@param src Pointer to the written array +@param len Number of the array elements to write +@param dt Specification of each array element, see @ref format_spec "format specification" +*/ +CVAPI(void) cvWriteRawDataBase64( CvFileStorage* fs, const void* src, + int len, const char* dt ); + +/** @brief Returns a unique pointer for a given name. + +The function returns a unique pointer for each particular file node name. This pointer can be then +passed to the cvGetFileNode function that is faster than cvGetFileNodeByName because it compares +text strings by comparing pointers rather than the strings' content. + +Consider the following example where an array of points is encoded as a sequence of 2-entry maps: +@code + points: + - { x: 10, y: 10 } + - { x: 20, y: 20 } + - { x: 30, y: 30 } + # ... +@endcode +Then, it is possible to get hashed "x" and "y" pointers to speed up decoding of the points. : +@code + #include "cxcore.h" + + int main( int argc, char** argv ) + { + CvFileStorage* fs = cvOpenFileStorage( "points.yml", 0, CV_STORAGE_READ ); + CvStringHashNode* x_key = cvGetHashedNode( fs, "x", -1, 1 ); + CvStringHashNode* y_key = cvGetHashedNode( fs, "y", -1, 1 ); + CvFileNode* points = cvGetFileNodeByName( fs, 0, "points" ); + + if( CV_NODE_IS_SEQ(points->tag) ) + { + CvSeq* seq = points->data.seq; + int i, total = seq->total; + CvSeqReader reader; + cvStartReadSeq( seq, &reader, 0 ); + for( i = 0; i < total; i++ ) + { + CvFileNode* pt = (CvFileNode*)reader.ptr; + #if 1 // faster variant + CvFileNode* xnode = cvGetFileNode( fs, pt, x_key, 0 ); + CvFileNode* ynode = cvGetFileNode( fs, pt, y_key, 0 ); + assert( xnode && CV_NODE_IS_INT(xnode->tag) && + ynode && CV_NODE_IS_INT(ynode->tag)); + int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); + int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); + #elif 1 // slower variant; does not use x_key & y_key + CvFileNode* xnode = cvGetFileNodeByName( fs, pt, "x" ); + CvFileNode* ynode = cvGetFileNodeByName( fs, pt, "y" ); + assert( xnode && CV_NODE_IS_INT(xnode->tag) && + ynode && CV_NODE_IS_INT(ynode->tag)); + int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); + int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); + #else // the slowest yet the easiest to use variant + int x = cvReadIntByName( fs, pt, "x", 0 ); + int y = cvReadIntByName( fs, pt, "y", 0 ); + #endif + CV_NEXT_SEQ_ELEM( seq->elem_size, reader ); + printf(" + } + } + cvReleaseFileStorage( &fs ); + return 0; + } +@endcode +Please note that whatever method of accessing a map you are using, it is still much slower than +using plain sequences; for example, in the above example, it is more efficient to encode the points +as pairs of integers in a single numeric sequence. +@param fs File storage +@param name Literal node name +@param len Length of the name (if it is known apriori), or -1 if it needs to be calculated +@param create_missing Flag that specifies, whether an absent key should be added into the hash table +*/ +CVAPI(CvStringHashNode*) cvGetHashedKey( CvFileStorage* fs, const char* name, + int len CV_DEFAULT(-1), + int create_missing CV_DEFAULT(0)); + +/** @brief Retrieves one of the top-level nodes of the file storage. + +The function returns one of the top-level file nodes. The top-level nodes do not have a name, they +correspond to the streams that are stored one after another in the file storage. If the index is out +of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by +subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned. +This function can be used as a base for recursive traversal of the file storage. +@param fs File storage +@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases, +there is only one stream in the file; however, there can be several. + */ +CVAPI(CvFileNode*) cvGetRootFileNode( const CvFileStorage* fs, + int stream_index CV_DEFAULT(0) ); + +/** @brief Finds a node in a map or file storage. + +The function finds a file node. It is a faster version of cvGetFileNodeByName (see +cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. If both map and +key are NULLs, the function returns the root file node - a map that contains top-level nodes. +@param key Unique pointer to the node name, retrieved with cvGetHashedKey +@param create_missing Flag that specifies whether an absent node should be added to the map + */ +CVAPI(CvFileNode*) cvGetFileNode( CvFileStorage* fs, CvFileNode* map, + const CvStringHashNode* key, + int create_missing CV_DEFAULT(0) ); + +/** @brief Finds a node in a map or file storage. + +The function finds a file node by name. The node is searched either in map or, if the pointer is +NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or +sequence reader) for sequences, it is possible to navigate through the file storage. To speed up +multiple queries for a certain key (e.g., in the case of an array of structures) one may use a +combination of cvGetHashedKey and cvGetFileNode. +@param fs File storage +@param map The parent map. If it is NULL, the function searches in all the top-level nodes +(streams), starting with the first one. +@param name The file node name + */ +CVAPI(CvFileNode*) cvGetFileNodeByName( const CvFileStorage* fs, + const CvFileNode* map, + const char* name ); + +/** @brief Retrieves an integer value from a file node. + +The function returns an integer that is represented by the file node. If the file node is NULL, the +default_value is returned (thus, it is convenient to call the function right after cvGetFileNode +without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is +returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer +and returned. Otherwise the error is reported. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE int cvReadInt( const CvFileNode* node, int default_value CV_DEFAULT(0) ) +{ + return !node ? default_value : + CV_NODE_IS_INT(node->tag) ? node->data.i : + CV_NODE_IS_REAL(node->tag) ? cvRound(node->data.f) : 0x7fffffff; +} + +/** @brief Finds a file node and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadInt. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE int cvReadIntByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, int default_value CV_DEFAULT(0) ) +{ + return cvReadInt( cvGetFileNodeByName( fs, map, name ), default_value ); +} + +/** @brief Retrieves a floating-point value from a file node. + +The function returns a floating-point value that is represented by the file node. If the file node +is NULL, the default_value is returned (thus, it is convenient to call the function right after +cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL , +then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f +is converted to floating-point and returned. Otherwise the result is not determined. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE double cvReadReal( const CvFileNode* node, double default_value CV_DEFAULT(0.) ) +{ + return !node ? default_value : + CV_NODE_IS_INT(node->tag) ? (double)node->data.i : + CV_NODE_IS_REAL(node->tag) ? node->data.f : 1e300; +} + +/** @brief Finds a file node and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadReal . +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE double cvReadRealByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, double default_value CV_DEFAULT(0.) ) +{ + return cvReadReal( cvGetFileNodeByName( fs, map, name ), default_value ); +} + +/** @brief Retrieves a text string from a file node. + +The function returns a text string that is represented by the file node. If the file node is NULL, +the default_value is returned (thus, it is convenient to call the function right after +cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then +node-:math:\>data.str.ptr is returned. Otherwise the result is not determined. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE const char* cvReadString( const CvFileNode* node, + const char* default_value CV_DEFAULT(NULL) ) +{ + return !node ? default_value : CV_NODE_IS_STRING(node->tag) ? node->data.str.ptr : 0; +} + +/** @brief Finds a file node by its name and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadString . +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE const char* cvReadStringByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, const char* default_value CV_DEFAULT(NULL) ) +{ + return cvReadString( cvGetFileNodeByName( fs, map, name ), default_value ); +} + + +/** @brief Decodes an object and returns a pointer to it. + +The function decodes a user object (creates an object in a native representation from the file +storage subtree) and returns it. The object to be decoded must be an instance of a registered type +that supports the read method (see CvTypeInfo). The type of the object is determined by the type +name that is encoded in the file. If the object is a dynamic structure, it is created either in +memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary +memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is +not a dynamic structure, it is created in a heap and should be released with a specialized function +or by using the generic cvRelease. +@param fs File storage +@param node The root object node +@param attributes Unused parameter + */ +CVAPI(void*) cvRead( CvFileStorage* fs, CvFileNode* node, + CvAttrList* attributes CV_DEFAULT(NULL)); + +/** @brief Finds an object by name and decodes it. + +The function is a simple superposition of cvGetFileNodeByName and cvRead. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param attributes Unused parameter + */ +CV_INLINE void* cvReadByName( CvFileStorage* fs, const CvFileNode* map, + const char* name, CvAttrList* attributes CV_DEFAULT(NULL) ) +{ + return cvRead( fs, cvGetFileNodeByName( fs, map, name ), attributes ); +} + + +/** @brief Initializes the file node sequence reader. + +The function initializes the sequence reader to read data from a file node. The initialized reader +can be then passed to cvReadRawDataSlice. +@param fs File storage +@param src The file node (a sequence) to read numbers from +@param reader Pointer to the sequence reader + */ +CVAPI(void) cvStartReadRawData( const CvFileStorage* fs, const CvFileNode* src, + CvSeqReader* reader ); + +/** @brief Initializes file node sequence reader. + +The function reads one or more elements from the file node, representing a sequence, to a +user-specified array. The total number of read sequence elements is a product of total and the +number of components in each array element. For example, if dt=2if, the function will read total\*3 +sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read +repeatedly by repositioning the reader using cvSetSeqReaderPos. +@param fs File storage +@param reader The sequence reader. Initialize it with cvStartReadRawData . +@param count The number of elements to read +@param dst Pointer to the destination array +@param dt Specification of each array element. It has the same format as in cvWriteRawData . + */ +CVAPI(void) cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader, + int count, void* dst, const char* dt ); + +/** @brief Reads multiple numbers. + +The function reads elements from a file node that represents a sequence of scalars. +@param fs File storage +@param src The file node (a sequence) to read numbers from +@param dst Pointer to the destination array +@param dt Specification of each array element. It has the same format as in cvWriteRawData . + */ +CVAPI(void) cvReadRawData( const CvFileStorage* fs, const CvFileNode* src, + void* dst, const char* dt ); + +/** @brief Writes a file node to another file storage. + +The function writes a copy of a file node to file storage. Possible applications of the function are +merging several file storages into one and conversion between XML, YAML and JSON formats. +@param fs Destination file storage +@param new_node_name New name of the file node in the destination file storage. To keep the +existing name, use cvcvGetFileNodeName +@param node The written node +@param embed If the written node is a collection and this parameter is not zero, no extra level of +hierarchy is created. Instead, all the elements of node are written into the currently written +structure. Of course, map elements can only be embedded into another map, and sequence elements +can only be embedded into another sequence. + */ +CVAPI(void) cvWriteFileNode( CvFileStorage* fs, const char* new_node_name, + const CvFileNode* node, int embed ); + +/** @brief Returns the name of a file node. + +The function returns the name of a file node or NULL, if the file node does not have a name or if +node is NULL. +@param node File node + */ +CVAPI(const char*) cvGetFileNodeName( const CvFileNode* node ); + +/*********************************** Adding own types ***********************************/ + +/** @brief Registers a new type. + +The function registers a new type, which is described by info . The function creates a copy of the +structure, so the user should delete it after calling the function. +@param info Type info structure + */ +CVAPI(void) cvRegisterType( const CvTypeInfo* info ); + +/** @brief Unregisters the type. + +The function unregisters a type with a specified name. If the name is unknown, it is possible to +locate the type info by an instance of the type using cvTypeOf or by iterating the type list, +starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName). +@param type_name Name of an unregistered type + */ +CVAPI(void) cvUnregisterType( const char* type_name ); + +/** @brief Returns the beginning of a type list. + +The function returns the first type in the list of registered types. Navigation through the list can +be done via the prev and next fields of the CvTypeInfo structure. + */ +CVAPI(CvTypeInfo*) cvFirstType(void); + +/** @brief Finds a type by its name. + +The function finds a registered type by its name. It returns NULL if there is no type with the +specified name. +@param type_name Type name + */ +CVAPI(CvTypeInfo*) cvFindType( const char* type_name ); + +/** @brief Returns the type of an object. + +The function finds the type of a given object. It iterates through the list of registered types and +calls the is_instance function/method for every type info structure with that object until one of +them returns non-zero or until the whole list has been traversed. In the latter case, the function +returns NULL. +@param struct_ptr The object pointer + */ +CVAPI(CvTypeInfo*) cvTypeOf( const void* struct_ptr ); + +/** @brief Releases an object. + +The function finds the type of a given object and calls release with the double pointer. +@param struct_ptr Double pointer to the object + */ +CVAPI(void) cvRelease( void** struct_ptr ); + +/** @brief Makes a clone of an object. + +The function finds the type of a given object and calls clone with the passed object. Of course, if +you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific +function, like cvCloneMat. +@param struct_ptr The object to clone + */ +CVAPI(void*) cvClone( const void* struct_ptr ); + +/** @brief Saves an object to a file. + +The function saves an object to a file. It provides a simple interface to cvWrite . +@param filename File name +@param struct_ptr Object to save +@param name Optional object name. If it is NULL, the name will be formed from filename . +@param comment Optional comment to put in the beginning of the file +@param attributes Optional attributes passed to cvWrite + */ +CVAPI(void) cvSave( const char* filename, const void* struct_ptr, + const char* name CV_DEFAULT(NULL), + const char* comment CV_DEFAULT(NULL), + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Loads an object from a file. + +The function loads an object from a file. It basically reads the specified file, find the first +top-level node and calls cvRead for that node. If the file node does not have type information or +the type information can not be found by the type name, the function returns NULL. After the object +is loaded, the file storage is closed and all the temporary buffers are deleted. Thus, to load a +dynamic structure, such as a sequence, contour, or graph, one should pass a valid memory storage +destination to the function. +@param filename File name +@param memstorage Memory storage for dynamic structures, such as CvSeq or CvGraph . It is not used +for matrices or images. +@param name Optional object name. If it is NULL, the first top-level object in the storage will be +loaded. +@param real_name Optional output parameter that will contain the name of the loaded object +(useful if name=NULL ) + */ +CVAPI(void*) cvLoad( const char* filename, + CvMemStorage* memstorage CV_DEFAULT(NULL), + const char* name CV_DEFAULT(NULL), + const char** real_name CV_DEFAULT(NULL) ); + +/*********************************** Measuring Execution Time ***************************/ + +/** helper functions for RNG initialization and accurate time measurement: + uses internal clock counter on x86 */ +CVAPI(int64) cvGetTickCount( void ); +CVAPI(double) cvGetTickFrequency( void ); + +/*********************************** CPU capabilities ***********************************/ + +CVAPI(int) cvCheckHardwareSupport(int feature); + +/*********************************** Multi-Threading ************************************/ + +/** retrieve/set the number of threads used in OpenMP implementations */ +CVAPI(int) cvGetNumThreads( void ); +CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) ); +/** get index of the thread being executed */ +CVAPI(int) cvGetThreadNum( void ); + + +/********************************** Error Handling **************************************/ + +/** Get current OpenCV error status */ +CVAPI(int) cvGetErrStatus( void ); + +/** Sets error status silently */ +CVAPI(void) cvSetErrStatus( int status ); + +#define CV_ErrModeLeaf 0 /* Print error and exit program */ +#define CV_ErrModeParent 1 /* Print error and continue */ +#define CV_ErrModeSilent 2 /* Don't print and continue */ + +/** Retrives current error processing mode */ +CVAPI(int) cvGetErrMode( void ); + +/** Sets error processing mode, returns previously used mode */ +CVAPI(int) cvSetErrMode( int mode ); + +/** Sets error status and performs some additonal actions (displaying message box, + writing message to stderr, terminating application etc.) + depending on the current error mode */ +CVAPI(void) cvError( int status, const char* func_name, + const char* err_msg, const char* file_name, int line ); + +/** Retrieves textual description of the error given its code */ +CVAPI(const char*) cvErrorStr( int status ); + +/** Retrieves detailed information about the last error occured */ +CVAPI(int) cvGetErrInfo( const char** errcode_desc, const char** description, + const char** filename, int* line ); + +/** Maps IPP error codes to the counterparts from OpenCV */ +CVAPI(int) cvErrorFromIppStatus( int ipp_status ); + +typedef int (CV_CDECL *CvErrorCallback)( int status, const char* func_name, + const char* err_msg, const char* file_name, int line, void* userdata ); + +/** Assigns a new error-handling function */ +CVAPI(CvErrorCallback) cvRedirectError( CvErrorCallback error_handler, + void* userdata CV_DEFAULT(NULL), + void** prev_userdata CV_DEFAULT(NULL) ); + +/** Output nothing */ +CVAPI(int) cvNulDevReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +/** Output to console(fprintf(stderr,...)) */ +CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +/** Output to MessageBox(WIN32) */ +CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +#define OPENCV_ERROR(status,func,context) \ +cvError((status),(func),(context),__FILE__,__LINE__) + +#define OPENCV_ASSERT(expr,func,context) \ +{if (! (expr)) \ +{OPENCV_ERROR(CV_StsInternal,(func),(context));}} + +#define OPENCV_CALL( Func ) \ +{ \ +Func; \ +} + + +/** CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */ +#ifdef CV_NO_FUNC_NAMES +#define CV_FUNCNAME( Name ) +#define cvFuncName "" +#else +#define CV_FUNCNAME( Name ) \ +static char cvFuncName[] = Name +#endif + + +/** + CV_ERROR macro unconditionally raises error with passed code and message. + After raising error, control will be transferred to the exit label. + */ +#define CV_ERROR( Code, Msg ) \ +{ \ + cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ ); \ + __CV_EXIT__; \ +} + +/** + CV_CHECK macro checks error status after CV (or IPL) + function call. If error detected, control will be transferred to the exit + label. + */ +#define CV_CHECK() \ +{ \ + if( cvGetErrStatus() < 0 ) \ + CV_ERROR( CV_StsBackTrace, "Inner function failed." ); \ +} + + +/** + CV_CALL macro calls CV (or IPL) function, checks error status and + signals a error if the function failed. Useful in "parent node" + error procesing mode + */ +#define CV_CALL( Func ) \ +{ \ + Func; \ + CV_CHECK(); \ +} + + +/** Runtime assertion macro */ +#define CV_ASSERT( Condition ) \ +{ \ + if( !(Condition) ) \ + CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \ +} + +#define __CV_BEGIN__ { +#define __CV_END__ goto exit; exit: ; } +#define __CV_EXIT__ goto exit + +/** @} core_c */ + +#ifdef __cplusplus +} // extern "C" +#endif + +#ifdef __cplusplus + +//! @addtogroup core_c_glue +//! @{ + +//! class for automatic module/RTTI data registration/unregistration +struct CV_EXPORTS CvType +{ + CvType( const char* type_name, + CvIsInstanceFunc is_instance, CvReleaseFunc release=0, + CvReadFunc read=0, CvWriteFunc write=0, CvCloneFunc clone=0 ); + ~CvType(); + CvTypeInfo* info; + + static CvTypeInfo* first; + static CvTypeInfo* last; +}; + +//! @} + +#include "opencv2/core/utility.hpp" + +namespace cv +{ + +//! @addtogroup core_c_glue +//! @{ + +/////////////////////////////////////////// glue /////////////////////////////////////////// + +//! converts array (CvMat or IplImage) to cv::Mat +CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false, + bool allowND=true, int coiMode=0, + AutoBuffer* buf=0); + +static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0) +{ + return cvarrToMat(arr, copyData, true, coiMode); +} + + +//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it. +CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1); +//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage +CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1); + + + +////// specialized implementations of DefaultDeleter::operator() for classic OpenCV types ////// + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMat* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(IplImage* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMatND* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvSparseMat* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMemStorage* obj) const; + +////////////// convenient wrappers for operating old-style dynamic structures ////////////// + +template class SeqIterator; + +typedef Ptr MemStorage; + +/*! + Template Sequence Class derived from CvSeq + + The class provides more convenient access to sequence elements, + STL-style operations and iterators. + + \note The class is targeted for simple data types, + i.e. no constructors or destructors + are called for the sequence elements. +*/ +template class Seq +{ +public: + typedef SeqIterator<_Tp> iterator; + typedef SeqIterator<_Tp> const_iterator; + + //! the default constructor + Seq(); + //! the constructor for wrapping CvSeq structure. The real element type in CvSeq should match _Tp. + Seq(const CvSeq* seq); + //! creates the empty sequence that resides in the specified storage + Seq(MemStorage& storage, int headerSize = sizeof(CvSeq)); + //! returns read-write reference to the specified element + _Tp& operator [](int idx); + //! returns read-only reference to the specified element + const _Tp& operator[](int idx) const; + //! returns iterator pointing to the beginning of the sequence + SeqIterator<_Tp> begin() const; + //! returns iterator pointing to the element following the last sequence element + SeqIterator<_Tp> end() const; + //! returns the number of elements in the sequence + size_t size() const; + //! returns the type of sequence elements (CV_8UC1 ... CV_64FC(CV_CN_MAX) ...) + int type() const; + //! returns the depth of sequence elements (CV_8U ... CV_64F) + int depth() const; + //! returns the number of channels in each sequence element + int channels() const; + //! returns the size of each sequence element + size_t elemSize() const; + //! returns index of the specified sequence element + size_t index(const _Tp& elem) const; + //! appends the specified element to the end of the sequence + void push_back(const _Tp& elem); + //! appends the specified element to the front of the sequence + void push_front(const _Tp& elem); + //! appends zero or more elements to the end of the sequence + void push_back(const _Tp* elems, size_t count); + //! appends zero or more elements to the front of the sequence + void push_front(const _Tp* elems, size_t count); + //! inserts the specified element to the specified position + void insert(int idx, const _Tp& elem); + //! inserts zero or more elements to the specified position + void insert(int idx, const _Tp* elems, size_t count); + //! removes element at the specified position + void remove(int idx); + //! removes the specified subsequence + void remove(const Range& r); + + //! returns reference to the first sequence element + _Tp& front(); + //! returns read-only reference to the first sequence element + const _Tp& front() const; + //! returns reference to the last sequence element + _Tp& back(); + //! returns read-only reference to the last sequence element + const _Tp& back() const; + //! returns true iff the sequence contains no elements + bool empty() const; + + //! removes all the elements from the sequence + void clear(); + //! removes the first element from the sequence + void pop_front(); + //! removes the last element from the sequence + void pop_back(); + //! removes zero or more elements from the beginning of the sequence + void pop_front(_Tp* elems, size_t count); + //! removes zero or more elements from the end of the sequence + void pop_back(_Tp* elems, size_t count); + + //! copies the whole sequence or the sequence slice to the specified vector + void copyTo(std::vector<_Tp>& vec, const Range& range=Range::all()) const; + //! returns the vector containing all the sequence elements + operator std::vector<_Tp>() const; + + CvSeq* seq; +}; + + +/*! + STL-style Sequence Iterator inherited from the CvSeqReader structure +*/ +template class SeqIterator : public CvSeqReader +{ +public: + //! the default constructor + SeqIterator(); + //! the constructor setting the iterator to the beginning or to the end of the sequence + SeqIterator(const Seq<_Tp>& seq, bool seekEnd=false); + //! positions the iterator within the sequence + void seek(size_t pos); + //! reports the current iterator position + size_t tell() const; + //! returns reference to the current sequence element + _Tp& operator *(); + //! returns read-only reference to the current sequence element + const _Tp& operator *() const; + //! moves iterator to the next sequence element + SeqIterator& operator ++(); + //! moves iterator to the next sequence element + SeqIterator operator ++(int) const; + //! moves iterator to the previous sequence element + SeqIterator& operator --(); + //! moves iterator to the previous sequence element + SeqIterator operator --(int) const; + + //! moves iterator forward by the specified offset (possibly negative) + SeqIterator& operator +=(int); + //! moves iterator backward by the specified offset (possibly negative) + SeqIterator& operator -=(int); + + // this is index of the current element module seq->total*2 + // (to distinguish between 0 and seq->total) + int index; +}; + + + +// bridge C++ => C Seq API +CV_EXPORTS schar* seqPush( CvSeq* seq, const void* element=0); +CV_EXPORTS schar* seqPushFront( CvSeq* seq, const void* element=0); +CV_EXPORTS void seqPop( CvSeq* seq, void* element=0); +CV_EXPORTS void seqPopFront( CvSeq* seq, void* element=0); +CV_EXPORTS void seqPopMulti( CvSeq* seq, void* elements, + int count, int in_front=0 ); +CV_EXPORTS void seqRemove( CvSeq* seq, int index ); +CV_EXPORTS void clearSeq( CvSeq* seq ); +CV_EXPORTS schar* getSeqElem( const CvSeq* seq, int index ); +CV_EXPORTS void seqRemoveSlice( CvSeq* seq, CvSlice slice ); +CV_EXPORTS void seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); + +template inline Seq<_Tp>::Seq() : seq(0) {} +template inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq) +{ + CV_Assert(!_seq || _seq->elem_size == sizeof(_Tp)); +} + +template inline Seq<_Tp>::Seq( MemStorage& storage, + int headerSize ) +{ + CV_Assert(headerSize >= (int)sizeof(CvSeq)); + seq = cvCreateSeq(DataType<_Tp>::type, headerSize, sizeof(_Tp), storage); +} + +template inline _Tp& Seq<_Tp>::operator [](int idx) +{ return *(_Tp*)getSeqElem(seq, idx); } + +template inline const _Tp& Seq<_Tp>::operator [](int idx) const +{ return *(_Tp*)getSeqElem(seq, idx); } + +template inline SeqIterator<_Tp> Seq<_Tp>::begin() const +{ return SeqIterator<_Tp>(*this); } + +template inline SeqIterator<_Tp> Seq<_Tp>::end() const +{ return SeqIterator<_Tp>(*this, true); } + +template inline size_t Seq<_Tp>::size() const +{ return seq ? seq->total : 0; } + +template inline int Seq<_Tp>::type() const +{ return seq ? CV_MAT_TYPE(seq->flags) : 0; } + +template inline int Seq<_Tp>::depth() const +{ return seq ? CV_MAT_DEPTH(seq->flags) : 0; } + +template inline int Seq<_Tp>::channels() const +{ return seq ? CV_MAT_CN(seq->flags) : 0; } + +template inline size_t Seq<_Tp>::elemSize() const +{ return seq ? seq->elem_size : 0; } + +template inline size_t Seq<_Tp>::index(const _Tp& elem) const +{ return cvSeqElemIdx(seq, &elem); } + +template inline void Seq<_Tp>::push_back(const _Tp& elem) +{ cvSeqPush(seq, &elem); } + +template inline void Seq<_Tp>::push_front(const _Tp& elem) +{ cvSeqPushFront(seq, &elem); } + +template inline void Seq<_Tp>::push_back(const _Tp* elem, size_t count) +{ cvSeqPushMulti(seq, elem, (int)count, 0); } + +template inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count) +{ cvSeqPushMulti(seq, elem, (int)count, 1); } + +template inline _Tp& Seq<_Tp>::back() +{ return *(_Tp*)getSeqElem(seq, -1); } + +template inline const _Tp& Seq<_Tp>::back() const +{ return *(const _Tp*)getSeqElem(seq, -1); } + +template inline _Tp& Seq<_Tp>::front() +{ return *(_Tp*)getSeqElem(seq, 0); } + +template inline const _Tp& Seq<_Tp>::front() const +{ return *(const _Tp*)getSeqElem(seq, 0); } + +template inline bool Seq<_Tp>::empty() const +{ return !seq || seq->total == 0; } + +template inline void Seq<_Tp>::clear() +{ if(seq) clearSeq(seq); } + +template inline void Seq<_Tp>::pop_back() +{ seqPop(seq); } + +template inline void Seq<_Tp>::pop_front() +{ seqPopFront(seq); } + +template inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count) +{ seqPopMulti(seq, elem, (int)count, 0); } + +template inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count) +{ seqPopMulti(seq, elem, (int)count, 1); } + +template inline void Seq<_Tp>::insert(int idx, const _Tp& elem) +{ seqInsert(seq, idx, &elem); } + +template inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count) +{ + CvMat m = cvMat(1, count, DataType<_Tp>::type, elems); + seqInsertSlice(seq, idx, &m); +} + +template inline void Seq<_Tp>::remove(int idx) +{ seqRemove(seq, idx); } + +template inline void Seq<_Tp>::remove(const Range& r) +{ seqRemoveSlice(seq, cvSlice(r.start, r.end)); } + +template inline void Seq<_Tp>::copyTo(std::vector<_Tp>& vec, const Range& range) const +{ + size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start; + vec.resize(len); + if( seq && len ) + cvCvtSeqToArray(seq, &vec[0], range); +} + +template inline Seq<_Tp>::operator std::vector<_Tp>() const +{ + std::vector<_Tp> vec; + copyTo(vec); + return vec; +} + +template inline SeqIterator<_Tp>::SeqIterator() +{ memset(this, 0, sizeof(*this)); } + +template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd) +{ + cvStartReadSeq(_seq.seq, this); + index = seekEnd ? _seq.seq->total : 0; +} + +template inline void SeqIterator<_Tp>::seek(size_t pos) +{ + cvSetSeqReaderPos(this, (int)pos, false); + index = pos; +} + +template inline size_t SeqIterator<_Tp>::tell() const +{ return index; } + +template inline _Tp& SeqIterator<_Tp>::operator *() +{ return *(_Tp*)ptr; } + +template inline const _Tp& SeqIterator<_Tp>::operator *() const +{ return *(const _Tp*)ptr; } + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator ++() +{ + CV_NEXT_SEQ_ELEM(sizeof(_Tp), *this); + if( ++index >= seq->total*2 ) + index = 0; + return *this; +} + +template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator ++(int) const +{ + SeqIterator<_Tp> it = *this; + ++*this; + return it; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator --() +{ + CV_PREV_SEQ_ELEM(sizeof(_Tp), *this); + if( --index < 0 ) + index = seq->total*2-1; + return *this; +} + +template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator --(int) const +{ + SeqIterator<_Tp> it = *this; + --*this; + return it; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator +=(int delta) +{ + cvSetSeqReaderPos(this, delta, 1); + index += delta; + int n = seq->total*2; + if( index < 0 ) + index += n; + if( index >= n ) + index -= n; + return *this; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator -=(int delta) +{ + return (*this += -delta); +} + +template inline ptrdiff_t operator - (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + ptrdiff_t delta = a.index - b.index, n = a.seq->total; + if( delta > n || delta < -n ) + delta += delta < 0 ? n : -n; + return delta; +} + +template inline bool operator == (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + return a.seq == b.seq && a.index == b.index; +} + +template inline bool operator != (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + return !(a == b); +} + +//! @} + +} // cv + +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/core/cuda.hpp b/thirdparty/linux/include/opencv2/core/cuda.hpp new file mode 100644 index 0000000..c538392 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda.hpp @@ -0,0 +1,874 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_HPP +#define OPENCV_CORE_CUDA_HPP + +#ifndef __cplusplus +# error cuda.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "opencv2/core/cuda_types.hpp" + +/** + @defgroup cuda CUDA-accelerated Computer Vision + @{ + @defgroup cudacore Core part + @{ + @defgroup cudacore_init Initalization and Information + @defgroup cudacore_struct Data Structures + @} + @} + */ + +namespace cv { namespace cuda { + +//! @addtogroup cudacore_struct +//! @{ + +//=================================================================================== +// GpuMat +//=================================================================================== + +/** @brief Base storage class for GPU memory with reference counting. + +Its interface matches the Mat interface with the following limitations: + +- no arbitrary dimensions support (only 2D) +- no functions that return references to their data (because references on GPU are not valid for + CPU) +- no expression templates technique support + +Beware that the latter limitation may lead to overloaded matrix operators that cause memory +allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be +passed directly to the kernel. + +@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are +aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix. + +@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely +on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory +release function returns error if the CUDA context has been destroyed before. + +@sa Mat + */ +class CV_EXPORTS GpuMat +{ +public: + class CV_EXPORTS Allocator + { + public: + virtual ~Allocator() {} + + // allocator must fill data, step and refcount fields + virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0; + virtual void free(GpuMat* mat) = 0; + }; + + //! default allocator + static Allocator* defaultAllocator(); + static void setDefaultAllocator(Allocator* allocator); + + //! default constructor + explicit GpuMat(Allocator* allocator = defaultAllocator()); + + //! constructs GpuMat of the specified size and type + GpuMat(int rows, int cols, int type, Allocator* allocator = defaultAllocator()); + GpuMat(Size size, int type, Allocator* allocator = defaultAllocator()); + + //! constucts GpuMat and fills it with the specified value _s + GpuMat(int rows, int cols, int type, Scalar s, Allocator* allocator = defaultAllocator()); + GpuMat(Size size, int type, Scalar s, Allocator* allocator = defaultAllocator()); + + //! copy constructor + GpuMat(const GpuMat& m); + + //! constructor for GpuMat headers pointing to user-allocated data + GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP); + GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP); + + //! creates a GpuMat header for a part of the bigger matrix + GpuMat(const GpuMat& m, Range rowRange, Range colRange); + GpuMat(const GpuMat& m, Rect roi); + + //! builds GpuMat from host memory (Blocking call) + explicit GpuMat(InputArray arr, Allocator* allocator = defaultAllocator()); + + //! destructor - calls release() + ~GpuMat(); + + //! assignment operators + GpuMat& operator =(const GpuMat& m); + + //! allocates new GpuMat data unless the GpuMat already has specified size and type + void create(int rows, int cols, int type); + void create(Size size, int type); + + //! decreases reference counter, deallocate the data when reference counter reaches 0 + void release(); + + //! swaps with other smart pointer + void swap(GpuMat& mat); + + //! pefroms upload data to GpuMat (Blocking call) + void upload(InputArray arr); + + //! pefroms upload data to GpuMat (Non-Blocking call) + void upload(InputArray arr, Stream& stream); + + //! pefroms download data from device to host memory (Blocking call) + void download(OutputArray dst) const; + + //! pefroms download data from device to host memory (Non-Blocking call) + void download(OutputArray dst, Stream& stream) const; + + //! returns deep copy of the GpuMat, i.e. the data is copied + GpuMat clone() const; + + //! copies the GpuMat content to device memory (Blocking call) + void copyTo(OutputArray dst) const; + + //! copies the GpuMat content to device memory (Non-Blocking call) + void copyTo(OutputArray dst, Stream& stream) const; + + //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call) + void copyTo(OutputArray dst, InputArray mask) const; + + //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call) + void copyTo(OutputArray dst, InputArray mask, Stream& stream) const; + + //! sets some of the GpuMat elements to s (Blocking call) + GpuMat& setTo(Scalar s); + + //! sets some of the GpuMat elements to s (Non-Blocking call) + GpuMat& setTo(Scalar s, Stream& stream); + + //! sets some of the GpuMat elements to s, according to the mask (Blocking call) + GpuMat& setTo(Scalar s, InputArray mask); + + //! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call) + GpuMat& setTo(Scalar s, InputArray mask, Stream& stream); + + //! converts GpuMat to another datatype (Blocking call) + void convertTo(OutputArray dst, int rtype) const; + + //! converts GpuMat to another datatype (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, Stream& stream) const; + + //! converts GpuMat to another datatype with scaling (Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const; + + //! converts GpuMat to another datatype with scaling (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const; + + //! converts GpuMat to another datatype with scaling (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const; + + void assignTo(GpuMat& m, int type=-1) const; + + //! returns pointer to y-th row + uchar* ptr(int y = 0); + const uchar* ptr(int y = 0) const; + + //! template version of the above method + template _Tp* ptr(int y = 0); + template const _Tp* ptr(int y = 0) const; + + template operator PtrStepSz<_Tp>() const; + template operator PtrStep<_Tp>() const; + + //! returns a new GpuMat header for the specified row + GpuMat row(int y) const; + + //! returns a new GpuMat header for the specified column + GpuMat col(int x) const; + + //! ... for the specified row span + GpuMat rowRange(int startrow, int endrow) const; + GpuMat rowRange(Range r) const; + + //! ... for the specified column span + GpuMat colRange(int startcol, int endcol) const; + GpuMat colRange(Range r) const; + + //! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.) + GpuMat operator ()(Range rowRange, Range colRange) const; + GpuMat operator ()(Rect roi) const; + + //! creates alternative GpuMat header for the same data, with different + //! number of channels and/or different number of rows + GpuMat reshape(int cn, int rows = 0) const; + + //! locates GpuMat header within a parent GpuMat + void locateROI(Size& wholeSize, Point& ofs) const; + + //! moves/resizes the current GpuMat ROI inside the parent GpuMat + GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright); + + //! returns true iff the GpuMat data is continuous + //! (i.e. when there are no gaps between successive rows) + bool isContinuous() const; + + //! returns element size in bytes + size_t elemSize() const; + + //! returns the size of element channel in bytes + size_t elemSize1() const; + + //! returns element type + int type() const; + + //! returns element type + int depth() const; + + //! returns number of channels + int channels() const; + + //! returns step/elemSize1() + size_t step1() const; + + //! returns GpuMat size : width == number of columns, height == number of rows + Size size() const; + + //! returns true if GpuMat data is NULL + bool empty() const; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + + //! the number of rows and columns + int rows, cols; + + //! a distance between successive rows in bytes; includes the gap if any + size_t step; + + //! pointer to the data + uchar* data; + + //! pointer to the reference counter; + //! when GpuMat points to user-allocated data, the pointer is NULL + int* refcount; + + //! helper fields used in locateROI and adjustROI + uchar* datastart; + const uchar* dataend; + + //! allocator + Allocator* allocator; +}; + +/** @brief Creates a continuous matrix. + +@param rows Row count. +@param cols Column count. +@param type Type of the matrix. +@param arr Destination matrix. This parameter changes only if it has a proper type and area ( +\f$\texttt{rows} \times \texttt{cols}\f$ ). + +Matrix is called continuous if its elements are stored continuously, that is, without gaps at the +end of each row. + */ +CV_EXPORTS void createContinuous(int rows, int cols, int type, OutputArray arr); + +/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type. + +@param rows Minimum desired number of rows. +@param cols Minimum desired number of columns. +@param type Desired matrix type. +@param arr Destination matrix. + +The function does not reallocate memory if the matrix has proper attributes already. + */ +CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr); + +//! BufferPool management (must be called before Stream creation) +CV_EXPORTS void setBufferPoolUsage(bool on); +CV_EXPORTS void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount); + +//=================================================================================== +// HostMem +//=================================================================================== + +/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA. + +Its interface is also Mat-like but with additional memory type parameters. + +- **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous + uploading/downloading data from/to GPU. +- **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU + address space, if supported. +- **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are + used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache + utilization. + +@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2 +Pinned Memory APIs* document or *CUDA C Programming Guide*. + */ +class CV_EXPORTS HostMem +{ +public: + enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 }; + + static MatAllocator* getAllocator(AllocType alloc_type = PAGE_LOCKED); + + explicit HostMem(AllocType alloc_type = PAGE_LOCKED); + + HostMem(const HostMem& m); + + HostMem(int rows, int cols, int type, AllocType alloc_type = PAGE_LOCKED); + HostMem(Size size, int type, AllocType alloc_type = PAGE_LOCKED); + + //! creates from host memory with coping data + explicit HostMem(InputArray arr, AllocType alloc_type = PAGE_LOCKED); + + ~HostMem(); + + HostMem& operator =(const HostMem& m); + + //! swaps with other smart pointer + void swap(HostMem& b); + + //! returns deep copy of the matrix, i.e. the data is copied + HostMem clone() const; + + //! allocates new matrix data unless the matrix already has specified size and type. + void create(int rows, int cols, int type); + void create(Size size, int type); + + //! creates alternative HostMem header for the same data, with different + //! number of channels and/or different number of rows + HostMem reshape(int cn, int rows = 0) const; + + //! decrements reference counter and released memory if needed. + void release(); + + //! returns matrix header with disabled reference counting for HostMem data. + Mat createMatHeader() const; + + /** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting + for it. + + This can be done only if memory was allocated with the SHARED flag and if it is supported by the + hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which + eliminates an extra copy. + */ + GpuMat createGpuMatHeader() const; + + // Please see cv::Mat for descriptions + bool isContinuous() const; + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1() const; + Size size() const; + bool empty() const; + + // Please see cv::Mat for descriptions + int flags; + int rows, cols; + size_t step; + + uchar* data; + int* refcount; + + uchar* datastart; + const uchar* dataend; + + AllocType alloc_type; +}; + +/** @brief Page-locks the memory of matrix and maps it for the device(s). + +@param m Input matrix. + */ +CV_EXPORTS void registerPageLocked(Mat& m); + +/** @brief Unmaps the memory of matrix and makes it pageable again. + +@param m Input matrix. + */ +CV_EXPORTS void unregisterPageLocked(Mat& m); + +//=================================================================================== +// Stream +//=================================================================================== + +/** @brief This class encapsulates a queue of asynchronous calls. + +@note Currently, you may face problems if an operation is enqueued twice with different data. Some +functions use the constant GPU memory, and next call may update the memory before the previous one +has been finished. But calling different operations asynchronously is safe because each operation +has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are +also safe. + +@note The Stream class is not thread-safe. Please use different Stream objects for different CPU threads. + +@code +void thread1() +{ + cv::cuda::Stream stream1; + cv::cuda::func1(..., stream1); +} + +void thread2() +{ + cv::cuda::Stream stream2; + cv::cuda::func2(..., stream2); +} +@endcode + +@note By default all CUDA routines are launched in Stream::Null() object, if the stream is not specified by user. +In multi-threading environment the stream objects must be passed explicitly (see previous note). + */ +class CV_EXPORTS Stream +{ + typedef void (Stream::*bool_type)() const; + void this_type_does_not_support_comparisons() const {} + +public: + typedef void (*StreamCallback)(int status, void* userData); + + //! creates a new asynchronous stream + Stream(); + + /** @brief Returns true if the current stream queue is finished. Otherwise, it returns false. + */ + bool queryIfComplete() const; + + /** @brief Blocks the current CPU thread until all operations in the stream are complete. + */ + void waitForCompletion(); + + /** @brief Makes a compute stream wait on an event. + */ + void waitEvent(const Event& event); + + /** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have + completed. + + @note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization + that may depend on outstanding device work or other callbacks that are not mandated to run earlier. + Callbacks without a mandated order (in independent streams) execute in undefined order and may be + serialized. + */ + void enqueueHostCallback(StreamCallback callback, void* userData); + + //! return Stream object for default CUDA stream + static Stream& Null(); + + //! returns true if stream object is not default (!= 0) + operator bool_type() const; + + class Impl; + +private: + Ptr impl_; + Stream(const Ptr& impl); + + friend struct StreamAccessor; + friend class BufferPool; + friend class DefaultDeviceInitializer; +}; + +class CV_EXPORTS Event +{ +public: + enum CreateFlags + { + DEFAULT = 0x00, /**< Default event flag */ + BLOCKING_SYNC = 0x01, /**< Event uses blocking synchronization */ + DISABLE_TIMING = 0x02, /**< Event will not record timing data */ + INTERPROCESS = 0x04 /**< Event is suitable for interprocess use. DisableTiming must be set */ + }; + + explicit Event(CreateFlags flags = DEFAULT); + + //! records an event + void record(Stream& stream = Stream::Null()); + + //! queries an event's status + bool queryIfComplete() const; + + //! waits for an event to complete + void waitForCompletion(); + + //! computes the elapsed time between events + static float elapsedTime(const Event& start, const Event& end); + + class Impl; + +private: + Ptr impl_; + Event(const Ptr& impl); + + friend struct EventAccessor; +}; + +//! @} cudacore_struct + +//=================================================================================== +// Initialization & Info +//=================================================================================== + +//! @addtogroup cudacore_init +//! @{ + +/** @brief Returns the number of installed CUDA-enabled devices. + +Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support, +this function returns 0. + */ +CV_EXPORTS int getCudaEnabledDeviceCount(); + +/** @brief Sets a device and initializes it for the current thread. + +@param device System index of a CUDA device starting with 0. + +If the call of this function is omitted, a default device is initialized at the fist CUDA usage. + */ +CV_EXPORTS void setDevice(int device); + +/** @brief Returns the current device index set by cuda::setDevice or initialized by default. + */ +CV_EXPORTS int getDevice(); + +/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current +process. + +Any subsequent API call to this device will reinitialize the device. + */ +CV_EXPORTS void resetDevice(); + +/** @brief Enumeration providing CUDA computing features. + */ +enum FeatureSet +{ + FEATURE_SET_COMPUTE_10 = 10, + FEATURE_SET_COMPUTE_11 = 11, + FEATURE_SET_COMPUTE_12 = 12, + FEATURE_SET_COMPUTE_13 = 13, + FEATURE_SET_COMPUTE_20 = 20, + FEATURE_SET_COMPUTE_21 = 21, + FEATURE_SET_COMPUTE_30 = 30, + FEATURE_SET_COMPUTE_32 = 32, + FEATURE_SET_COMPUTE_35 = 35, + FEATURE_SET_COMPUTE_50 = 50, + + GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11, + SHARED_ATOMICS = FEATURE_SET_COMPUTE_12, + NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13, + WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30, + DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35 +}; + +//! checks whether current device supports the given feature +CV_EXPORTS bool deviceSupports(FeatureSet feature_set); + +/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was +built for. + +According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute +capability can always be compiled to binary code of greater or equal compute capability". + */ +class CV_EXPORTS TargetArchs +{ +public: + /** @brief The following method checks whether the module was built with the support of the given feature: + + @param feature_set Features to be checked. See :ocvcuda::FeatureSet. + */ + static bool builtWith(FeatureSet feature_set); + + /** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA + code for the given architecture(s): + + @param major Major compute capability version. + @param minor Minor compute capability version. + */ + static bool has(int major, int minor); + static bool hasPtx(int major, int minor); + static bool hasBin(int major, int minor); + + static bool hasEqualOrLessPtx(int major, int minor); + static bool hasEqualOrGreater(int major, int minor); + static bool hasEqualOrGreaterPtx(int major, int minor); + static bool hasEqualOrGreaterBin(int major, int minor); +}; + +/** @brief Class providing functionality for querying the specified GPU properties. + */ +class CV_EXPORTS DeviceInfo +{ +public: + //! creates DeviceInfo object for the current GPU + DeviceInfo(); + + /** @brief The constructors. + + @param device_id System index of the CUDA device starting with 0. + + Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it + constructs an object for the current device. + */ + DeviceInfo(int device_id); + + /** @brief Returns system index of the CUDA device starting with 0. + */ + int deviceID() const; + + //! ASCII string identifying device + const char* name() const; + + //! global memory available on device in bytes + size_t totalGlobalMem() const; + + //! shared memory available per block in bytes + size_t sharedMemPerBlock() const; + + //! 32-bit registers available per block + int regsPerBlock() const; + + //! warp size in threads + int warpSize() const; + + //! maximum pitch in bytes allowed by memory copies + size_t memPitch() const; + + //! maximum number of threads per block + int maxThreadsPerBlock() const; + + //! maximum size of each dimension of a block + Vec3i maxThreadsDim() const; + + //! maximum size of each dimension of a grid + Vec3i maxGridSize() const; + + //! clock frequency in kilohertz + int clockRate() const; + + //! constant memory available on device in bytes + size_t totalConstMem() const; + + //! major compute capability + int majorVersion() const; + + //! minor compute capability + int minorVersion() const; + + //! alignment requirement for textures + size_t textureAlignment() const; + + //! pitch alignment requirement for texture references bound to pitched memory + size_t texturePitchAlignment() const; + + //! number of multiprocessors on device + int multiProcessorCount() const; + + //! specified whether there is a run time limit on kernels + bool kernelExecTimeoutEnabled() const; + + //! device is integrated as opposed to discrete + bool integrated() const; + + //! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer + bool canMapHostMemory() const; + + enum ComputeMode + { + ComputeModeDefault, /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */ + ComputeModeExclusive, /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */ + ComputeModeProhibited, /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */ + ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */ + }; + + //! compute mode + ComputeMode computeMode() const; + + //! maximum 1D texture size + int maxTexture1D() const; + + //! maximum 1D mipmapped texture size + int maxTexture1DMipmap() const; + + //! maximum size for 1D textures bound to linear memory + int maxTexture1DLinear() const; + + //! maximum 2D texture dimensions + Vec2i maxTexture2D() const; + + //! maximum 2D mipmapped texture dimensions + Vec2i maxTexture2DMipmap() const; + + //! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory + Vec3i maxTexture2DLinear() const; + + //! maximum 2D texture dimensions if texture gather operations have to be performed + Vec2i maxTexture2DGather() const; + + //! maximum 3D texture dimensions + Vec3i maxTexture3D() const; + + //! maximum Cubemap texture dimensions + int maxTextureCubemap() const; + + //! maximum 1D layered texture dimensions + Vec2i maxTexture1DLayered() const; + + //! maximum 2D layered texture dimensions + Vec3i maxTexture2DLayered() const; + + //! maximum Cubemap layered texture dimensions + Vec2i maxTextureCubemapLayered() const; + + //! maximum 1D surface size + int maxSurface1D() const; + + //! maximum 2D surface dimensions + Vec2i maxSurface2D() const; + + //! maximum 3D surface dimensions + Vec3i maxSurface3D() const; + + //! maximum 1D layered surface dimensions + Vec2i maxSurface1DLayered() const; + + //! maximum 2D layered surface dimensions + Vec3i maxSurface2DLayered() const; + + //! maximum Cubemap surface dimensions + int maxSurfaceCubemap() const; + + //! maximum Cubemap layered surface dimensions + Vec2i maxSurfaceCubemapLayered() const; + + //! alignment requirements for surfaces + size_t surfaceAlignment() const; + + //! device can possibly execute multiple kernels concurrently + bool concurrentKernels() const; + + //! device has ECC support enabled + bool ECCEnabled() const; + + //! PCI bus ID of the device + int pciBusID() const; + + //! PCI device ID of the device + int pciDeviceID() const; + + //! PCI domain ID of the device + int pciDomainID() const; + + //! true if device is a Tesla device using TCC driver, false otherwise + bool tccDriver() const; + + //! number of asynchronous engines + int asyncEngineCount() const; + + //! device shares a unified address space with the host + bool unifiedAddressing() const; + + //! peak memory clock frequency in kilohertz + int memoryClockRate() const; + + //! global memory bus width in bits + int memoryBusWidth() const; + + //! size of L2 cache in bytes + int l2CacheSize() const; + + //! maximum resident threads per multiprocessor + int maxThreadsPerMultiProcessor() const; + + //! gets free and total device memory + void queryMemory(size_t& totalMemory, size_t& freeMemory) const; + size_t freeMemory() const; + size_t totalMemory() const; + + /** @brief Provides information on CUDA feature support. + + @param feature_set Features to be checked. See cuda::FeatureSet. + + This function returns true if the device has the specified CUDA feature. Otherwise, it returns false + */ + bool supports(FeatureSet feature_set) const; + + /** @brief Checks the CUDA module and device compatibility. + + This function returns true if the CUDA module can be run on the specified device. Otherwise, it + returns false . + */ + bool isCompatible() const; + +private: + int device_id_; +}; + +CV_EXPORTS void printCudaDeviceInfo(int device); +CV_EXPORTS void printShortCudaDeviceInfo(int device); + +/** @brief Converts an array to half precision floating number. + +@param _src input array. +@param _dst output array. +@param stream Stream for the asynchronous version. +@sa convertFp16 +*/ +CV_EXPORTS void convertFp16(InputArray _src, OutputArray _dst, Stream& stream = Stream::Null()); + +//! @} cudacore_init + +}} // namespace cv { namespace cuda { + + +#include "opencv2/core/cuda.inl.hpp" + +#endif /* OPENCV_CORE_CUDA_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/cuda.inl.hpp b/thirdparty/linux/include/opencv2/core/cuda.inl.hpp new file mode 100644 index 0000000..35ae2e4 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda.inl.hpp @@ -0,0 +1,631 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDAINL_HPP +#define OPENCV_CORE_CUDAINL_HPP + +#include "opencv2/core/cuda.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { + +//=================================================================================== +// GpuMat +//=================================================================================== + +inline +GpuMat::GpuMat(Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{} + +inline +GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (rows_ > 0 && cols_ > 0) + create(rows_, cols_, type_); +} + +inline +GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (size_.height > 0 && size_.width > 0) + create(size_.height, size_.width, type_); +} + +inline +GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (rows_ > 0 && cols_ > 0) + { + create(rows_, cols_, type_); + setTo(s_); + } +} + +inline +GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (size_.height > 0 && size_.width > 0) + { + create(size_.height, size_.width, type_); + setTo(s_); + } +} + +inline +GpuMat::GpuMat(const GpuMat& m) + : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator) +{ + if (refcount) + CV_XADD(refcount, 1); +} + +inline +GpuMat::GpuMat(InputArray arr, Allocator* allocator_) : + flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + upload(arr); +} + +inline +GpuMat::~GpuMat() +{ + release(); +} + +inline +GpuMat& GpuMat::operator =(const GpuMat& m) +{ + if (this != &m) + { + GpuMat temp(m); + swap(temp); + } + + return *this; +} + +inline +void GpuMat::create(Size size_, int type_) +{ + create(size_.height, size_.width, type_); +} + +inline +void GpuMat::swap(GpuMat& b) +{ + std::swap(flags, b.flags); + std::swap(rows, b.rows); + std::swap(cols, b.cols); + std::swap(step, b.step); + std::swap(data, b.data); + std::swap(datastart, b.datastart); + std::swap(dataend, b.dataend); + std::swap(refcount, b.refcount); + std::swap(allocator, b.allocator); +} + +inline +GpuMat GpuMat::clone() const +{ + GpuMat m; + copyTo(m); + return m; +} + +inline +void GpuMat::copyTo(OutputArray dst, InputArray mask) const +{ + copyTo(dst, mask, Stream::Null()); +} + +inline +GpuMat& GpuMat::setTo(Scalar s) +{ + return setTo(s, Stream::Null()); +} + +inline +GpuMat& GpuMat::setTo(Scalar s, InputArray mask) +{ + return setTo(s, mask, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype) const +{ + convertTo(dst, rtype, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const +{ + convertTo(dst, rtype, alpha, beta, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const +{ + convertTo(dst, rtype, alpha, 0.0, stream); +} + +inline +void GpuMat::assignTo(GpuMat& m, int _type) const +{ + if (_type < 0) + m = *this; + else + convertTo(m, _type); +} + +inline +uchar* GpuMat::ptr(int y) +{ + CV_DbgAssert( (unsigned)y < (unsigned)rows ); + return data + step * y; +} + +inline +const uchar* GpuMat::ptr(int y) const +{ + CV_DbgAssert( (unsigned)y < (unsigned)rows ); + return data + step * y; +} + +template inline +_Tp* GpuMat::ptr(int y) +{ + return (_Tp*)ptr(y); +} + +template inline +const _Tp* GpuMat::ptr(int y) const +{ + return (const _Tp*)ptr(y); +} + +template inline +GpuMat::operator PtrStepSz() const +{ + return PtrStepSz(rows, cols, (T*)data, step); +} + +template inline +GpuMat::operator PtrStep() const +{ + return PtrStep((T*)data, step); +} + +inline +GpuMat GpuMat::row(int y) const +{ + return GpuMat(*this, Range(y, y+1), Range::all()); +} + +inline +GpuMat GpuMat::col(int x) const +{ + return GpuMat(*this, Range::all(), Range(x, x+1)); +} + +inline +GpuMat GpuMat::rowRange(int startrow, int endrow) const +{ + return GpuMat(*this, Range(startrow, endrow), Range::all()); +} + +inline +GpuMat GpuMat::rowRange(Range r) const +{ + return GpuMat(*this, r, Range::all()); +} + +inline +GpuMat GpuMat::colRange(int startcol, int endcol) const +{ + return GpuMat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +GpuMat GpuMat::colRange(Range r) const +{ + return GpuMat(*this, Range::all(), r); +} + +inline +GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const +{ + return GpuMat(*this, rowRange_, colRange_); +} + +inline +GpuMat GpuMat::operator ()(Rect roi) const +{ + return GpuMat(*this, roi); +} + +inline +bool GpuMat::isContinuous() const +{ + return (flags & Mat::CONTINUOUS_FLAG) != 0; +} + +inline +size_t GpuMat::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t GpuMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int GpuMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int GpuMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int GpuMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t GpuMat::step1() const +{ + return step / elemSize1(); +} + +inline +Size GpuMat::size() const +{ + return Size(cols, rows); +} + +inline +bool GpuMat::empty() const +{ + return data == 0; +} + +static inline +GpuMat createContinuous(int rows, int cols, int type) +{ + GpuMat m; + createContinuous(rows, cols, type, m); + return m; +} + +static inline +void createContinuous(Size size, int type, OutputArray arr) +{ + createContinuous(size.height, size.width, type, arr); +} + +static inline +GpuMat createContinuous(Size size, int type) +{ + GpuMat m; + createContinuous(size, type, m); + return m; +} + +static inline +void ensureSizeIsEnough(Size size, int type, OutputArray arr) +{ + ensureSizeIsEnough(size.height, size.width, type, arr); +} + +static inline +void swap(GpuMat& a, GpuMat& b) +{ + a.swap(b); +} + +//=================================================================================== +// HostMem +//=================================================================================== + +inline +HostMem::HostMem(AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ +} + +inline +HostMem::HostMem(const HostMem& m) + : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type) +{ + if( refcount ) + CV_XADD(refcount, 1); +} + +inline +HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + if (rows_ > 0 && cols_ > 0) + create(rows_, cols_, type_); +} + +inline +HostMem::HostMem(Size size_, int type_, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + if (size_.height > 0 && size_.width > 0) + create(size_.height, size_.width, type_); +} + +inline +HostMem::HostMem(InputArray arr, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + arr.getMat().copyTo(*this); +} + +inline +HostMem::~HostMem() +{ + release(); +} + +inline +HostMem& HostMem::operator =(const HostMem& m) +{ + if (this != &m) + { + HostMem temp(m); + swap(temp); + } + + return *this; +} + +inline +void HostMem::swap(HostMem& b) +{ + std::swap(flags, b.flags); + std::swap(rows, b.rows); + std::swap(cols, b.cols); + std::swap(step, b.step); + std::swap(data, b.data); + std::swap(datastart, b.datastart); + std::swap(dataend, b.dataend); + std::swap(refcount, b.refcount); + std::swap(alloc_type, b.alloc_type); +} + +inline +HostMem HostMem::clone() const +{ + HostMem m(size(), type(), alloc_type); + createMatHeader().copyTo(m); + return m; +} + +inline +void HostMem::create(Size size_, int type_) +{ + create(size_.height, size_.width, type_); +} + +inline +Mat HostMem::createMatHeader() const +{ + return Mat(size(), type(), data, step); +} + +inline +bool HostMem::isContinuous() const +{ + return (flags & Mat::CONTINUOUS_FLAG) != 0; +} + +inline +size_t HostMem::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t HostMem::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int HostMem::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int HostMem::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int HostMem::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t HostMem::step1() const +{ + return step / elemSize1(); +} + +inline +Size HostMem::size() const +{ + return Size(cols, rows); +} + +inline +bool HostMem::empty() const +{ + return data == 0; +} + +static inline +void swap(HostMem& a, HostMem& b) +{ + a.swap(b); +} + +//=================================================================================== +// Stream +//=================================================================================== + +inline +Stream::Stream(const Ptr& impl) + : impl_(impl) +{ +} + +//=================================================================================== +// Event +//=================================================================================== + +inline +Event::Event(const Ptr& impl) + : impl_(impl) +{ +} + +//=================================================================================== +// Initialization & Info +//=================================================================================== + +inline +bool TargetArchs::has(int major, int minor) +{ + return hasPtx(major, minor) || hasBin(major, minor); +} + +inline +bool TargetArchs::hasEqualOrGreater(int major, int minor) +{ + return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor); +} + +inline +DeviceInfo::DeviceInfo() +{ + device_id_ = getDevice(); +} + +inline +DeviceInfo::DeviceInfo(int device_id) +{ + CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() ); + device_id_ = device_id; +} + +inline +int DeviceInfo::deviceID() const +{ + return device_id_; +} + +inline +size_t DeviceInfo::freeMemory() const +{ + size_t _totalMemory = 0, _freeMemory = 0; + queryMemory(_totalMemory, _freeMemory); + return _freeMemory; +} + +inline +size_t DeviceInfo::totalMemory() const +{ + size_t _totalMemory = 0, _freeMemory = 0; + queryMemory(_totalMemory, _freeMemory); + return _totalMemory; +} + +inline +bool DeviceInfo::supports(FeatureSet feature_set) const +{ + int version = majorVersion() * 10 + minorVersion(); + return version >= feature_set; +} + + +}} // namespace cv { namespace cuda { + +//=================================================================================== +// Mat +//=================================================================================== + +namespace cv { + +inline +Mat::Mat(const cuda::GpuMat& m) + : flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows) +{ + m.download(*this); +} + +} + +//! @endcond + +#endif // OPENCV_CORE_CUDAINL_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/block.hpp b/thirdparty/linux/include/opencv2/core/cuda/block.hpp new file mode 100644 index 0000000..330cf1d --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/block.hpp @@ -0,0 +1,211 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP +#define OPENCV_CUDA_DEVICE_BLOCK_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Block + { + static __device__ __forceinline__ unsigned int id() + { + return blockIdx.x; + } + + static __device__ __forceinline__ unsigned int stride() + { + return blockDim.x * blockDim.y * blockDim.z; + } + + static __device__ __forceinline__ void sync() + { + __syncthreads(); + } + + static __device__ __forceinline__ int flattenedThreadId() + { + return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x; + } + + template + static __device__ __forceinline__ void fill(It beg, It end, const T& value) + { + int STRIDE = stride(); + It t = beg + flattenedThreadId(); + + for(; t < end; t += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) + { + int STRIDE = stride(); + int tid = flattenedThreadId(); + value += tid; + + for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out) + { + int STRIDE = stride(); + InIt t = beg + flattenedThreadId(); + OutIt o = out + (t - beg); + + for(; t < end; t += STRIDE, o += STRIDE) + *o = *t; + } + + template + static __device__ __forceinline__ void transfrom(InIt beg, InIt end, OutIt out, UnOp op) + { + int STRIDE = stride(); + InIt t = beg + flattenedThreadId(); + OutIt o = out + (t - beg); + + for(; t < end; t += STRIDE, o += STRIDE) + *o = op(*t); + } + + template + static __device__ __forceinline__ void transfrom(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + { + int STRIDE = stride(); + InIt1 t1 = beg1 + flattenedThreadId(); + InIt2 t2 = beg2 + flattenedThreadId(); + OutIt o = out + (t1 - beg1); + + for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE) + *o = op(*t1, *t2); + } + + template + static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op) + { + int tid = flattenedThreadId(); + T val = buffer[tid]; + + if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } + if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } + if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } + if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } + + if (tid < 32) + { + if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } + if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } + if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } + if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } + if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } + if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } + } + } + + template + static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op) + { + int tid = flattenedThreadId(); + T val = buffer[tid] = init; + __syncthreads(); + + if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } + if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } + if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } + if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } + + if (tid < 32) + { + if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } + if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } + if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } + if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } + if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } + if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } + } + __syncthreads(); + return buffer[0]; + } + + template + static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op) + { + int ftid = flattenedThreadId(); + int sft = stride(); + + if (sft < n) + { + for (unsigned int i = sft + ftid; i < n; i += sft) + data[ftid] = op(data[ftid], data[i]); + + __syncthreads(); + + n = sft; + } + + while (n > 1) + { + unsigned int half = n/2; + + if (ftid < half) + data[ftid] = op(data[ftid], data[n - ftid - 1]); + + __syncthreads(); + + n = n - half; + } + } + }; +}}} + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_BLOCK_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/cuda/border_interpolate.hpp b/thirdparty/linux/include/opencv2/core/cuda/border_interpolate.hpp new file mode 100644 index 0000000..874f705 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/border_interpolate.hpp @@ -0,0 +1,722 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_BORDER_INTERPOLATE_HPP +#define OPENCV_CUDA_BORDER_INTERPOLATE_HPP + +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "vec_math.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + ////////////////////////////////////////////////////////////// + // BrdConstant + + template struct BrdRowConstant + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits::all(0)) : width(width_), val(val_) {} + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return x >= 0 ? saturate_cast(data[x]) : val; + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return x < width ? saturate_cast(data[x]) : val; + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return (x >= 0 && x < width) ? saturate_cast(data[x]) : val; + } + + int width; + D val; + }; + + template struct BrdColConstant + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits::all(0)) : height(height_), val(val_) {} + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return y >= 0 ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return y < height ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return (y >= 0 && y < height) ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + int height; + D val; + }; + + template struct BrdConstant + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits::all(0)) : height(height_), width(width_), val(val_) + { + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(((const T*)((const uchar*)data + y * step))[x]) : val; + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; + } + + int height; + int width; + D val; + }; + + ////////////////////////////////////////////////////////////// + // BrdReplicate + + template struct BrdRowReplicate + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::max(x, 0); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::min(x, last_col); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReplicate + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::max(y, 0); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::min(y, last_row); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReplicate + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::max(y, 0); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::min(y, last_row); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::max(x, 0); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::min(x, last_col); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdReflect101 + + template struct BrdRowReflect101 + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::abs(x) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReflect101 + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::abs(y) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReflect101 + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::abs(y) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::abs(x) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdReflect + + template struct BrdRowReflect + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (::abs(x) - (x < 0)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(::abs(x) - (x < 0)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReflect + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (::abs(y) - (y < 0)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(::abs(y) - (y < 0)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReflect + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (::abs(y) - (y < 0)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/; + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (::abs(x) - (x < 0)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (last_col - ::abs(last_col - x) + (x > last_col)); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdWrap + + template struct BrdRowWrap + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {} + template __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (x < width) * x + (x >= width) * (x % width); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(idx_col_low(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int width; + }; + + template struct BrdColWrap + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {} + template __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return (y < height) * y + (y >= height) * (y % height); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(idx_row_low(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int height; + }; + + template struct BrdWrap + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) : + height(height_), width(width_) + { + } + template + __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) : + height(height_), width(width_) + { + } + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (y >= 0) ? y : (y - ((y - height + 1) / height) * height); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return (y < height) ? y : (y % height); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(idx_row_low(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (x >= 0) ? x : (x - ((x - width + 1) / width) * width); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (x < width) ? x : (x % width); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(idx_col_low(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int height; + int width; + }; + + ////////////////////////////////////////////////////////////// + // BorderReader + + template struct BorderReader + { + typedef typename B::result_type elem_type; + typedef typename Ptr2D::index_type index_type; + + __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {} + + __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const + { + return b.at(y, x, ptr); + } + + Ptr2D ptr; + B b; + }; + + // under win32 there is some bug with templated types that passed as kernel parameters + // with this specialization all works fine + template struct BorderReader< Ptr2D, BrdConstant > + { + typedef typename BrdConstant::result_type elem_type; + typedef typename Ptr2D::index_type index_type; + + __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant& b) : + src(src_), height(b.height), width(b.width), val(b.val) + { + } + + __device__ __forceinline__ D operator ()(index_type y, index_type x) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; + } + + Ptr2D src; + int height; + int width; + D val; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_BORDER_INTERPOLATE_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/color.hpp b/thirdparty/linux/include/opencv2/core/cuda/color.hpp new file mode 100644 index 0000000..dcce280 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/color.hpp @@ -0,0 +1,309 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COLOR_HPP +#define OPENCV_CUDA_COLOR_HPP + +#include "detail/color_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements + // template class ColorSpace1_to_ColorSpace2_traits + // { + // typedef ... functor_type; + // static __host__ __device__ functor_type create_functor(); + // }; + + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3) + OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4) + + #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5) + OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6) + + #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS + + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS + + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS + + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS + + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS + + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS + + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS + + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_COLOR_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/common.hpp b/thirdparty/linux/include/opencv2/core/cuda/common.hpp new file mode 100644 index 0000000..14b1f3f --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/common.hpp @@ -0,0 +1,109 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COMMON_HPP +#define OPENCV_CUDA_COMMON_HPP + +#include +#include "opencv2/core/cuda_types.hpp" +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +#ifndef CV_PI_F + #ifndef CV_PI + #define CV_PI_F 3.14159265f + #else + #define CV_PI_F ((float)CV_PI) + #endif +#endif + +namespace cv { namespace cuda { + static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func) + { + if (cudaSuccess != err) + cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line); + } +}} + +#ifndef cudaSafeCall + #define cudaSafeCall(expr) cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func) +#endif + +namespace cv { namespace cuda +{ + template static inline bool isAligned(const T* ptr, size_t size) + { + return reinterpret_cast(ptr) % size == 0; + } + + static inline bool isAligned(size_t step, size_t size) + { + return step % size == 0; + } +}} + +namespace cv { namespace cuda +{ + namespace device + { + __host__ __device__ __forceinline__ int divUp(int total, int grain) + { + return (total + grain - 1) / grain; + } + + template inline void bindTexture(const textureReference* tex, const PtrStepSz& img) + { + cudaChannelFormatDesc desc = cudaCreateChannelDesc(); + cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) ); + } + } +}} + +//! @endcond + +#endif // OPENCV_CUDA_COMMON_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/datamov_utils.hpp b/thirdparty/linux/include/opencv2/core/cuda/datamov_utils.hpp new file mode 100644 index 0000000..6820d0f --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/datamov_utils.hpp @@ -0,0 +1,113 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DATAMOV_UTILS_HPP +#define OPENCV_CUDA_DATAMOV_UTILS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 + + // for Fermi memory space is detected automatically + template struct ForceGlob + { + __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val) { val = ptr[offset]; } + }; + + #else // __CUDA_ARCH__ >= 200 + + #if defined(_WIN64) || defined(__LP64__) + // 64-bit register modifier for inlined asm + #define OPENCV_CUDA_ASM_PTR "l" + #else + // 32-bit register modifier for inlined asm + #define OPENCV_CUDA_ASM_PTR "r" + #endif + + template struct ForceGlob; + + #define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \ + template <> struct ForceGlob \ + { \ + __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ + { \ + asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ + } \ + }; + + #define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \ + template <> struct ForceGlob \ + { \ + __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ + { \ + asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ + } \ + }; + + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar, u8) + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar, s8) + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char, b8) + OPENCV_CUDA_DEFINE_FORCE_GLOB (ushort, u16, h) + OPENCV_CUDA_DEFINE_FORCE_GLOB (short, s16, h) + OPENCV_CUDA_DEFINE_FORCE_GLOB (uint, u32, r) + OPENCV_CUDA_DEFINE_FORCE_GLOB (int, s32, r) + OPENCV_CUDA_DEFINE_FORCE_GLOB (float, f32, f) + OPENCV_CUDA_DEFINE_FORCE_GLOB (double, f64, d) + + #undef OPENCV_CUDA_DEFINE_FORCE_GLOB + #undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B + #undef OPENCV_CUDA_ASM_PTR + + #endif // __CUDA_ARCH__ >= 200 +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_DATAMOV_UTILS_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/detail/color_detail.hpp b/thirdparty/linux/include/opencv2/core/cuda/detail/color_detail.hpp new file mode 100644 index 0000000..bfb4055 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/detail/color_detail.hpp @@ -0,0 +1,1980 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COLOR_DETAIL_HPP +#define OPENCV_CUDA_COLOR_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" +#include "../saturate_cast.hpp" +#include "../limits.hpp" +#include "../functional.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + #ifndef CV_DESCALE + #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n)) + #endif + + namespace color_detail + { + template struct ColorChannel + { + typedef float worktype_f; + static __device__ __forceinline__ T max() { return numeric_limits::max(); } + static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); } + }; + + template<> struct ColorChannel + { + typedef float worktype_f; + static __device__ __forceinline__ float max() { return 1.f; } + static __device__ __forceinline__ float half() { return 0.5f; } + }; + + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) + { + } + + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) + { + vec.w = val; + } + + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) + { + return ColorChannel::max(); + } + + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) + { + return vec.w; + } + + enum + { + yuv_shift = 14, + xyz_shift = 12, + R2Y = 4899, + G2Y = 9617, + B2Y = 1868, + BLOCK_SIZE = 256 + }; + } + +////////////////// Various 3/4-channel to 3/4-channel RGB transformations ///////////////// + + namespace color_detail + { + template struct RGB2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + dst.x = (&src.x)[bidx]; + dst.y = src.y; + dst.z = (&src.x)[bidx^2]; + setAlpha(dst, getAlpha(src)); + + return dst; + } + + __host__ __device__ __forceinline__ RGB2RGB() {} + __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} + }; + + template <> struct RGB2RGB : unary_function + { + __device__ uint operator()(uint src) const + { + uint dst = 0; + + dst |= (0xffu & (src >> 16)); + dst |= (0xffu & (src >> 8)) << 8; + dst |= (0xffu & (src)) << 16; + dst |= (0xffu & (src >> 24)) << 24; + + return dst; + } + + __host__ __device__ __forceinline__ RGB2RGB() {} + __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB ////////// + + namespace color_detail + { + template struct RGB2RGB5x5Converter; + template struct RGB2RGB5x5Converter<6, bidx> + { + static __device__ __forceinline__ ushort cvt(const uchar3& src) + { + return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8)); + } + + static __device__ __forceinline__ ushort cvt(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8)); + } + }; + + template struct RGB2RGB5x5Converter<5, bidx> + { + static __device__ __forceinline__ ushort cvt(const uchar3& src) + { + return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7)); + } + + static __device__ __forceinline__ ushort cvt(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + uint a = 0xffu & (src >> 24); + return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000)); + } + }; + + template struct RGB2RGB5x5; + + template struct RGB2RGB5x5<3, bidx,green_bits> : unary_function + { + __device__ __forceinline__ ushort operator()(const uchar3& src) const + { + return RGB2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ RGB2RGB5x5() {} + __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} + }; + + template struct RGB2RGB5x5<4, bidx,green_bits> : unary_function + { + __device__ __forceinline__ ushort operator()(uint src) const + { + return RGB2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ RGB2RGB5x5() {} + __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2RGB5x5 functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template struct RGB5x52RGBConverter; + + template struct RGB5x52RGBConverter<5, bidx> + { + static __device__ __forceinline__ void cvt(uint src, uchar3& dst) + { + (&dst.x)[bidx] = src << 3; + dst.y = (src >> 2) & ~7; + (&dst.x)[bidx ^ 2] = (src >> 7) & ~7; + } + + static __device__ __forceinline__ void cvt(uint src, uint& dst) + { + dst = 0; + + dst |= (0xffu & (src << 3)) << (bidx * 8); + dst |= (0xffu & ((src >> 2) & ~7)) << 8; + dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8); + dst |= ((src & 0x8000) * 0xffu) << 24; + } + }; + + template struct RGB5x52RGBConverter<6, bidx> + { + static __device__ __forceinline__ void cvt(uint src, uchar3& dst) + { + (&dst.x)[bidx] = src << 3; + dst.y = (src >> 3) & ~3; + (&dst.x)[bidx ^ 2] = (src >> 8) & ~7; + } + + static __device__ __forceinline__ void cvt(uint src, uint& dst) + { + dst = 0xffu << 24; + + dst |= (0xffu & (src << 3)) << (bidx * 8); + dst |= (0xffu &((src >> 3) & ~3)) << 8; + dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8); + } + }; + + template struct RGB5x52RGB; + + template struct RGB5x52RGB<3, bidx, green_bits> : unary_function + { + __device__ __forceinline__ uchar3 operator()(ushort src) const + { + uchar3 dst; + RGB5x52RGBConverter::cvt(src, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB5x52RGB() {} + __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} + + }; + + template struct RGB5x52RGB<4, bidx, green_bits> : unary_function + { + __device__ __forceinline__ uint operator()(ushort src) const + { + uint dst; + RGB5x52RGBConverter::cvt(src, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB5x52RGB() {} + __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB5x52RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////// Grayscale to Color //////////////////////////////// + + namespace color_detail + { + template struct Gray2RGB : unary_function::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(T src) const + { + typename TypeVec::vec_type dst; + + dst.z = dst.y = dst.x = src; + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ Gray2RGB() {} + __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} + }; + + template <> struct Gray2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + uint dst = 0xffu << 24; + + dst |= src; + dst |= src << 8; + dst |= src << 16; + + return dst; + } + __host__ __device__ __forceinline__ Gray2RGB() {} + __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Gray2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template struct Gray2RGB5x5Converter; + template<> struct Gray2RGB5x5Converter<6> + { + static __device__ __forceinline__ ushort cvt(uint t) + { + return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8)); + } + }; + + template<> struct Gray2RGB5x5Converter<5> + { + static __device__ __forceinline__ ushort cvt(uint t) + { + t >>= 3; + return (ushort)(t | (t << 5) | (t << 10)); + } + }; + + template struct Gray2RGB5x5 : unary_function + { + __device__ __forceinline__ ushort operator()(uint src) const + { + return Gray2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ Gray2RGB5x5() {} + __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Gray2RGB5x5 functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////// Color to Grayscale //////////////////////////////// + + namespace color_detail + { + template struct RGB5x52GrayConverter; + template <> struct RGB5x52GrayConverter<6> + { + static __device__ __forceinline__ uchar cvt(uint t) + { + return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift); + } + }; + + template <> struct RGB5x52GrayConverter<5> + { + static __device__ __forceinline__ uchar cvt(uint t) + { + return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 2) & 0xf8) * G2Y + ((t >> 7) & 0xf8) * R2Y, yuv_shift); + } + }; + + template struct RGB5x52Gray : unary_function + { + __device__ __forceinline__ uchar operator()(uint src) const + { + return RGB5x52GrayConverter::cvt(src); + } + __host__ __device__ __forceinline__ RGB5x52Gray() {} + __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB5x52Gray functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template static __device__ __forceinline__ T RGB2GrayConvert(const T* src) + { + return (T)CV_DESCALE((unsigned)(src[bidx] * B2Y + src[1] * G2Y + src[bidx^2] * R2Y), yuv_shift); + } + + template static __device__ __forceinline__ uchar RGB2GrayConvert(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift); + } + + template static __device__ __forceinline__ float RGB2GrayConvert(const float* src) + { + return src[bidx] * 0.114f + src[1] * 0.587f + src[bidx^2] * 0.299f; + } + + template struct RGB2Gray : unary_function::vec_type, T> + { + __device__ __forceinline__ T operator()(const typename TypeVec::vec_type& src) const + { + return RGB2GrayConvert(&src.x); + } + __host__ __device__ __forceinline__ RGB2Gray() {} + __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} + }; + + template struct RGB2Gray : unary_function + { + __device__ __forceinline__ uchar operator()(uint src) const + { + return RGB2GrayConvert(src); + } + __host__ __device__ __forceinline__ RGB2Gray() {} + __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Gray functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> YUV ////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2YUVCoeffs_f[5] = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f }; + __constant__ int c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, 8061, 14369 }; + + template static __device__ void RGB2YUVConvert(const T* src, D& dst) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE(src[0] * c_RGB2YUVCoeffs_i[bidx^2] + src[1] * c_RGB2YUVCoeffs_i[1] + src[2] * c_RGB2YUVCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift); + + dst.x = saturate_cast(Y); + dst.y = saturate_cast(Cr); + dst.z = saturate_cast(Cb); + } + + template static __device__ __forceinline__ void RGB2YUVConvert(const float* src, D& dst) + { + dst.x = src[0] * c_RGB2YUVCoeffs_f[bidx^2] + src[1] * c_RGB2YUVCoeffs_f[1] + src[2] * c_RGB2YUVCoeffs_f[bidx]; + dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel::half(); + dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel::half(); + } + + template struct RGB2YUV + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + RGB2YUVConvert(&src.x, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB2YUV() {} + __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2YUV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f }; + __constant__ int c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 }; + + template static __device__ void YUV2RGBConvert(const T& src, D* dst) + { + const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); + + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); + + dst[bidx] = saturate_cast(b); + dst[1] = saturate_cast(g); + dst[bidx^2] = saturate_cast(r); + } + + template static __device__ uint YUV2RGBConvert(uint src) + { + const int x = 0xff & (src); + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); + + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(b) << (bidx * 8); + dst |= saturate_cast(g) << 8; + dst |= saturate_cast(r) << ((bidx ^ 2) * 8); + + return dst; + } + + template static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst) + { + dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[3]; + + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[1]; + + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[0]; + } + + template struct YUV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + YUV2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ YUV2RGB() {} + __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} + }; + + template struct YUV2RGB : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return YUV2RGBConvert(src); + } + __host__ __device__ __forceinline__ YUV2RGB() {} + __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::YUV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> YCrCb ////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; + __constant__ int c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241}; + + template static __device__ void RGB2YCrCbConvert(const T* src, D& dst) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE(src[0] * c_RGB2YCrCbCoeffs_i[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_i[1] + src[2] * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); + + dst.x = saturate_cast(Y); + dst.y = saturate_cast(Cr); + dst.z = saturate_cast(Cb); + } + + template static __device__ uint RGB2YCrCbConvert(uint src) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); + + uint dst = 0; + + dst |= saturate_cast(Y); + dst |= saturate_cast(Cr) << 8; + dst |= saturate_cast(Cb) << 16; + + return dst; + } + + template static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst) + { + dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx]; + dst.y = (src[bidx^2] - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel::half(); + dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel::half(); + } + + template struct RGB2YCrCb + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + RGB2YCrCbConvert(&src.x, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB2YCrCb() {} + __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} + }; + + template struct RGB2YCrCb : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return RGB2YCrCbConvert(src); + } + + __host__ __device__ __forceinline__ RGB2YCrCb() {} + __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2YCrCb functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_YCrCb2RGBCoeffs_f[5] = {1.403f, -0.714f, -0.344f, 1.773f}; + __constant__ int c_YCrCb2RGBCoeffs_i[5] = {22987, -11698, -5636, 29049}; + + template static __device__ void YCrCb2RGBConvert(const T& src, D* dst) + { + const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); + + dst[bidx] = saturate_cast(b); + dst[1] = saturate_cast(g); + dst[bidx^2] = saturate_cast(r); + } + + template static __device__ uint YCrCb2RGBConvert(uint src) + { + const int x = 0xff & (src); + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(b) << (bidx * 8); + dst |= saturate_cast(g) << 8; + dst |= saturate_cast(r) << ((bidx ^ 2) * 8); + + return dst; + } + + template __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst) + { + dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[3]; + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[1]; + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[0]; + } + + template struct YCrCb2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + YCrCb2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ YCrCb2RGB() {} + __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} + }; + + template struct YCrCb2RGB : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return YCrCb2RGBConvert(src); + } + __host__ __device__ __forceinline__ YCrCb2RGB() {} + __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::YCrCb2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +////////////////////////////////////// RGB <-> XYZ /////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f }; + __constant__ int c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 }; + + template static __device__ __forceinline__ void RGB2XYZConvert(const T* src, D& dst) + { + dst.z = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift)); + dst.x = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[0] + src[1] * c_RGB2XYZ_D65i[1] + src[bidx] * c_RGB2XYZ_D65i[2], xyz_shift)); + dst.y = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift)); + } + + template static __device__ __forceinline__ uint RGB2XYZConvert(uint src) + { + const uint b = 0xffu & (src >> (bidx * 8)); + const uint g = 0xffu & (src >> 8); + const uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + + const uint x = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift)); + const uint y = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift)); + const uint z = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift)); + + uint dst = 0; + + dst |= x; + dst |= y << 8; + dst |= z << 16; + + return dst; + } + + template static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst) + { + dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2]; + dst.y = src[bidx^2] * c_RGB2XYZ_D65f[3] + src[1] * c_RGB2XYZ_D65f[4] + src[bidx] * c_RGB2XYZ_D65f[5]; + dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8]; + } + + template struct RGB2XYZ + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2XYZConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2XYZ() {} + __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} + }; + + template struct RGB2XYZ : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2XYZConvert(src); + } + __host__ __device__ __forceinline__ RGB2XYZ() {} + __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2XYZ functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f }; + __constant__ int c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 }; + + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, D* dst) + { + dst[bidx^2] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift)); + dst[1] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift)); + dst[bidx] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift)); + } + + template static __device__ __forceinline__ uint XYZ2RGBConvert(uint src) + { + const int x = 0xff & src; + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const uint r = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift)); + const uint g = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift)); + const uint b = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift)); + + uint dst = 0xffu << 24; + + dst |= b << (bidx * 8); + dst |= g << 8; + dst |= r << ((bidx ^ 2) * 8); + + return dst; + } + + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst) + { + dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2]; + dst[1] = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5]; + dst[bidx] = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8]; + } + + template struct XYZ2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + XYZ2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ XYZ2RGB() {} + __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} + }; + + template struct XYZ2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return XYZ2RGBConvert(src); + } + __host__ __device__ __forceinline__ XYZ2RGB() {} + __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::XYZ2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +////////////////////////////////////// RGB <-> HSV /////////////////////////////////////// + + namespace color_detail + { + __constant__ int c_HsvDivTable [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096}; + __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482}; + __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685}; + + template static __device__ void RGB2HSVConvert(const uchar* src, D& dst) + { + const int hsv_shift = 12; + const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; + + int b = src[bidx], g = src[1], r = src[bidx^2]; + int h, s, v = b; + int vmin = b, diff; + int vr, vg; + + v = ::max(v, g); + v = ::max(v, r); + vmin = ::min(vmin, g); + vmin = ::min(vmin, r); + + diff = v - vmin; + vr = (v == r) * -1; + vg = (v == g) * -1; + + s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; + h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); + h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; + h += (h < 0) * hr; + + dst.x = saturate_cast(h); + dst.y = (uchar)s; + dst.z = (uchar)v; + } + + template static __device__ uint RGB2HSVConvert(uint src) + { + const int hsv_shift = 12; + const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; + + const int b = 0xff & (src >> (bidx * 8)); + const int g = 0xff & (src >> 8); + const int r = 0xff & (src >> ((bidx ^ 2) * 8)); + + int h, s, v = b; + int vmin = b, diff; + int vr, vg; + + v = ::max(v, g); + v = ::max(v, r); + vmin = ::min(vmin, g); + vmin = ::min(vmin, r); + + diff = v - vmin; + vr = (v == r) * -1; + vg = (v == g) * -1; + + s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; + h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); + h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; + h += (h < 0) * hr; + + uint dst = 0; + + dst |= saturate_cast(h); + dst |= (0xffu & s) << 8; + dst |= (0xffu & v) << 16; + + return dst; + } + + template static __device__ void RGB2HSVConvert(const float* src, D& dst) + { + const float hscale = hr * (1.f / 360.f); + + float b = src[bidx], g = src[1], r = src[bidx^2]; + float h, s, v; + + float vmin, diff; + + v = vmin = r; + v = fmax(v, g); + v = fmax(v, b); + vmin = fmin(vmin, g); + vmin = fmin(vmin, b); + + diff = v - vmin; + s = diff / (float)(::fabs(v) + numeric_limits::epsilon()); + diff = (float)(60. / (diff + numeric_limits::epsilon())); + + h = (v == r) * (g - b) * diff; + h += (v != r && v == g) * ((b - r) * diff + 120.f); + h += (v != r && v != g) * ((r - g) * diff + 240.f); + h += (h < 0) * 360.f; + + dst.x = h * hscale; + dst.y = s; + dst.z = v; + } + + template struct RGB2HSV + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2HSVConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2HSV() {} + __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} + }; + + template struct RGB2HSV : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2HSVConvert(src); + } + __host__ __device__ __forceinline__ RGB2HSV() {} + __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; + + template static __device__ void HSV2RGBConvert(const T& src, float* dst) + { + const float hscale = 6.f / hr; + + float h = src.x, s = src.y, v = src.z; + float b = v, g = v, r = v; + + if (s != 0) + { + h *= hscale; + + if( h < 0 ) + do h += 6; while( h < 0 ); + else if( h >= 6 ) + do h -= 6; while( h >= 6 ); + + int sector = __float2int_rd(h); + h -= sector; + + if ( (unsigned)sector >= 6u ) + { + sector = 0; + h = 0.f; + } + + float tab[4]; + tab[0] = v; + tab[1] = v * (1.f - s); + tab[2] = v * (1.f - s * h); + tab[3] = v * (1.f - s * (1.f - h)); + + b = tab[c_HsvSectorData[sector][0]]; + g = tab[c_HsvSectorData[sector][1]]; + r = tab[c_HsvSectorData[sector][2]]; + } + + dst[bidx] = b; + dst[1] = g; + dst[bidx^2] = r; + } + + template static __device__ void HSV2RGBConvert(const T& src, uchar* dst) + { + float3 buf; + + buf.x = src.x; + buf.y = src.y * (1.f / 255.f); + buf.z = src.z * (1.f / 255.f); + + HSV2RGBConvert(buf, &buf.x); + + dst[0] = saturate_cast(buf.x * 255.f); + dst[1] = saturate_cast(buf.y * 255.f); + dst[2] = saturate_cast(buf.z * 255.f); + } + + template static __device__ uint HSV2RGBConvert(uint src) + { + float3 buf; + + buf.x = src & 0xff; + buf.y = ((src >> 8) & 0xff) * (1.f/255.f); + buf.z = ((src >> 16) & 0xff) * (1.f/255.f); + + HSV2RGBConvert(buf, &buf.x); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x * 255.f); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct HSV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + HSV2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ HSV2RGB() {} + __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} + }; + + template struct HSV2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return HSV2RGBConvert(src); + } + __host__ __device__ __forceinline__ HSV2RGB() {} + __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +/////////////////////////////////////// RGB <-> HLS //////////////////////////////////////// + + namespace color_detail + { + template static __device__ void RGB2HLSConvert(const float* src, D& dst) + { + const float hscale = hr * (1.f / 360.f); + + float b = src[bidx], g = src[1], r = src[bidx^2]; + float h = 0.f, s = 0.f, l; + float vmin, vmax, diff; + + vmax = vmin = r; + vmax = fmax(vmax, g); + vmax = fmax(vmax, b); + vmin = fmin(vmin, g); + vmin = fmin(vmin, b); + + diff = vmax - vmin; + l = (vmax + vmin) * 0.5f; + + if (diff > numeric_limits::epsilon()) + { + s = (l < 0.5f) * diff / (vmax + vmin); + s += (l >= 0.5f) * diff / (2.0f - vmax - vmin); + + diff = 60.f / diff; + + h = (vmax == r) * (g - b) * diff; + h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f); + h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f); + h += (h < 0.f) * 360.f; + } + + dst.x = h * hscale; + dst.y = l; + dst.z = s; + } + + template static __device__ void RGB2HLSConvert(const uchar* src, D& dst) + { + float3 buf; + + buf.x = src[0] * (1.f / 255.f); + buf.y = src[1] * (1.f / 255.f); + buf.z = src[2] * (1.f / 255.f); + + RGB2HLSConvert(&buf.x, buf); + + dst.x = saturate_cast(buf.x); + dst.y = saturate_cast(buf.y*255.f); + dst.z = saturate_cast(buf.z*255.f); + } + + template static __device__ uint RGB2HLSConvert(uint src) + { + float3 buf; + + buf.x = (0xff & src) * (1.f / 255.f); + buf.y = (0xff & (src >> 8)) * (1.f / 255.f); + buf.z = (0xff & (src >> 16)) * (1.f / 255.f); + + RGB2HLSConvert(&buf.x, buf); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct RGB2HLS + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2HLSConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2HLS() {} + __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} + }; + + template struct RGB2HLS : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2HLSConvert(src); + } + __host__ __device__ __forceinline__ RGB2HLS() {} + __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; + + template static __device__ void HLS2RGBConvert(const T& src, float* dst) + { + const float hscale = 6.0f / hr; + + float h = src.x, l = src.y, s = src.z; + float b = l, g = l, r = l; + + if (s != 0) + { + float p2 = (l <= 0.5f) * l * (1 + s); + p2 += (l > 0.5f) * (l + s - l * s); + float p1 = 2 * l - p2; + + h *= hscale; + + if( h < 0 ) + do h += 6; while( h < 0 ); + else if( h >= 6 ) + do h -= 6; while( h >= 6 ); + + int sector; + sector = __float2int_rd(h); + + h -= sector; + + float tab[4]; + tab[0] = p2; + tab[1] = p1; + tab[2] = p1 + (p2 - p1) * (1 - h); + tab[3] = p1 + (p2 - p1) * h; + + b = tab[c_HlsSectorData[sector][0]]; + g = tab[c_HlsSectorData[sector][1]]; + r = tab[c_HlsSectorData[sector][2]]; + } + + dst[bidx] = b; + dst[1] = g; + dst[bidx^2] = r; + } + + template static __device__ void HLS2RGBConvert(const T& src, uchar* dst) + { + float3 buf; + + buf.x = src.x; + buf.y = src.y * (1.f / 255.f); + buf.z = src.z * (1.f / 255.f); + + HLS2RGBConvert(buf, &buf.x); + + dst[0] = saturate_cast(buf.x * 255.f); + dst[1] = saturate_cast(buf.y * 255.f); + dst[2] = saturate_cast(buf.z * 255.f); + } + + template static __device__ uint HLS2RGBConvert(uint src) + { + float3 buf; + + buf.x = 0xff & src; + buf.y = (0xff & (src >> 8)) * (1.f / 255.f); + buf.z = (0xff & (src >> 16)) * (1.f / 255.f); + + HLS2RGBConvert(buf, &buf.x); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x * 255.f); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct HLS2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + HLS2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ HLS2RGB() {} + __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} + }; + + template struct HLS2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return HLS2RGBConvert(src); + } + __host__ __device__ __forceinline__ HLS2RGB() {} + __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> Lab ///////////////////////////////////// + + namespace color_detail + { + enum + { + LAB_CBRT_TAB_SIZE = 1024, + GAMMA_TAB_SIZE = 1024, + lab_shift = xyz_shift, + gamma_shift = 3, + lab_shift2 = (lab_shift + gamma_shift), + LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift)) + }; + + __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040}; + + __device__ __forceinline__ int LabCbrt_b(int i) + { + float x = i * (1.f / (255.f * (1 << gamma_shift))); + return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x)); + } + + template + __device__ __forceinline__ void RGB2LabConvert_b(const T& src, D& dst) + { + const int Lscale = (116 * 255 + 50) / 100; + const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100); + + int B = blueIdx == 0 ? src.x : src.z; + int G = src.y; + int R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = c_sRGBGammaTab_b[B]; + G = c_sRGBGammaTab_b[G]; + R = c_sRGBGammaTab_b[R]; + } + else + { + B <<= 3; + G <<= 3; + R <<= 3; + } + + int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift)); + int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift)); + int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift)); + + int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2); + int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2); + int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2); + + dst.x = saturate_cast(L); + dst.y = saturate_cast(a); + dst.z = saturate_cast(b); + } + + __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n) + { + int ix = ::min(::max(int(x), 0), n-1); + x -= ix; + tab += ix * 4; + return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0]; + } + + __constant__ float c_sRGBGammaTab[] = 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+ + template + __device__ __forceinline__ void RGB2LabConvert_f(const T& src, D& dst) + { + const float _1_3 = 1.0f / 3.0f; + const float _a = 16.0f / 116.0f; + + float B = blueIdx == 0 ? src.x : src.z; + float G = src.y; + float R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + } + + float X = B * 0.189828f + G * 0.376219f + R * 0.433953f; + float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f; + float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f; + + float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a); + float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a); + float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a); + + float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y); + float a = 500.f * (FX - FY); + float b = 200.f * (FY - FZ); + + dst.x = L; + dst.y = a; + dst.z = b; + } + + template struct RGB2Lab; + template + struct RGB2Lab + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LabConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Lab() {} + __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} + }; + template + struct RGB2Lab + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LabConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Lab() {} + __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Lab functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_sRGBInvGammaTab[] = 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+ + template + __device__ __forceinline__ void Lab2RGBConvert_f(const T& src, D& dst) + { + const float lThresh = 0.008856f * 903.3f; + const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f; + + float Y, fy; + + if (src.x <= lThresh) + { + Y = src.x / 903.3f; + fy = 7.787f * Y + 16.0f / 116.0f; + } + else + { + fy = (src.x + 16.0f) / 116.0f; + Y = fy * fy * fy; + } + + float X = src.y / 500.0f + fy; + float Z = fy - src.z / 200.0f; + + if (X <= fThresh) + X = (X - 16.0f / 116.0f) / 7.787f; + else + X = X * X * X; + + if (Z <= fThresh) + Z = (Z - 16.0f / 116.0f) / 7.787f; + else + Z = Z * Z * Z; + + float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z; + float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z; + float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + } + + dst.x = blueIdx == 0 ? B : R; + dst.y = G; + dst.z = blueIdx == 0 ? R : B; + setAlpha(dst, ColorChannel::max()); + } + + template + __device__ __forceinline__ void Lab2RGBConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (100.f / 255.f); + srcf.y = src.y - 128; + srcf.z = src.z - 128; + + Lab2RGBConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 255.f); + dst.y = saturate_cast(dstf.y * 255.f); + dst.z = saturate_cast(dstf.z * 255.f); + setAlpha(dst, ColorChannel::max()); + } + + template struct Lab2RGB; + template + struct Lab2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Lab2RGBConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Lab2RGB() {} + __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} + }; + template + struct Lab2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Lab2RGBConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Lab2RGB() {} + __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Lab2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> Luv ///////////////////////////////////// + + namespace color_detail + { + __constant__ float c_LabCbrtTab[] = 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+ + template + __device__ __forceinline__ void RGB2LuvConvert_f(const T& src, D& dst) + { + const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); + const float _un = 13 * (4 * 0.950456f * _d); + const float _vn = 13 * (9 * _d); + + float B = blueIdx == 0 ? src.x : src.z; + float G = src.y; + float R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + } + + float X = R * 0.412453f + G * 0.357580f + B * 0.180423f; + float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f; + float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f; + + float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE); + L = 116.f * L - 16.f; + + const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits::epsilon()); + float u = L * (X * d - _un); + float v = L * ((9 * 0.25f) * Y * d - _vn); + + dst.x = L; + dst.y = u; + dst.z = v; + } + + template + __device__ __forceinline__ void RGB2LuvConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (1.f / 255.f); + srcf.y = src.y * (1.f / 255.f); + srcf.z = src.z * (1.f / 255.f); + + RGB2LuvConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 2.55f); + dst.y = saturate_cast(dstf.y * 0.72033898305084743f + 96.525423728813564f); + dst.z = saturate_cast(dstf.z * 0.9732824427480916f + 136.259541984732824f); + } + + template struct RGB2Luv; + template + struct RGB2Luv + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LuvConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Luv() {} + __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} + }; + template + struct RGB2Luv + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LuvConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Luv() {} + __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Luv functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template + __device__ __forceinline__ void Luv2RGBConvert_f(const T& src, D& dst) + { + const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); + const float _un = 4 * 0.950456f * _d; + const float _vn = 9 * _d; + + float L = src.x; + float u = src.y; + float v = src.z; + + float Y = (L + 16.f) * (1.f / 116.f); + Y = Y * Y * Y; + + float d = (1.f / 13.f) / L; + u = u * d + _un; + v = v * d + _vn; + + float iv = 1.f / v; + float X = 2.25f * u * Y * iv; + float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; + + float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z; + float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z; + float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + } + + dst.x = blueIdx == 0 ? B : R; + dst.y = G; + dst.z = blueIdx == 0 ? R : B; + setAlpha(dst, ColorChannel::max()); + } + + template + __device__ __forceinline__ void Luv2RGBConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (100.f / 255.f); + srcf.y = src.y * 1.388235294117647f - 134.f; + srcf.z = src.z * 1.027450980392157f - 140.f; + + Luv2RGBConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 255.f); + dst.y = saturate_cast(dstf.y * 255.f); + dst.z = saturate_cast(dstf.z * 255.f); + setAlpha(dst, ColorChannel::max()); + } + + template struct Luv2RGB; + template + struct Luv2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Luv2RGBConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Luv2RGB() {} + __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} + }; + template + struct Luv2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Luv2RGBConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Luv2RGB() {} + __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Luv2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + #undef CV_DESCALE + +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_COLOR_DETAIL_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/detail/reduce.hpp b/thirdparty/linux/include/opencv2/core/cuda/detail/reduce.hpp new file mode 100644 index 0000000..ff82c3c --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/detail/reduce.hpp @@ -0,0 +1,365 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_REDUCE_DETAIL_HPP +#define OPENCV_CUDA_REDUCE_DETAIL_HPP + +#include +#include "../warp.hpp" +#include "../warp_shuffle.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace reduce_detail + { + template struct GetType; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) + { + thrust::get(smem)[tid] = thrust::get(val); + + For::loadToSmem(smem, val, tid); + } + template + static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) + { + thrust::get(val) = thrust::get(smem)[tid]; + + For::loadFromSmem(smem, val, tid); + } + + template + static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op) + { + typename GetType::type>::type reg = thrust::get(smem)[tid + delta]; + thrust::get(smem)[tid] = thrust::get(val) = thrust::get(op)(thrust::get(val), reg); + + For::merge(smem, val, tid, delta, op); + } + template + static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op) + { + typename GetType::type>::type reg = shfl_down(thrust::get(val), delta, width); + thrust::get(val) = thrust::get(op)(thrust::get(val), reg); + + For::mergeShfl(val, delta, width, op); + } + }; + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int) + { + } + template + static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int) + { + } + + template + static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&) + { + } + template + static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&) + { + } + }; + + template + __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid) + { + smem[tid] = val; + } + template + __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid) + { + val = smem[tid]; + } + template + __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadToSmem(smem, val, tid); + } + template + __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadFromSmem(smem, val, tid); + } + + template + __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op) + { + T reg = smem[tid + delta]; + smem[tid] = val = op(val, reg); + } + template + __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op) + { + T reg = shfl_down(val, delta, width); + val = op(val, reg); + } + template + __device__ __forceinline__ void merge(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid, + unsigned int delta, + const thrust::tuple& op) + { + For<0, thrust::tuple_size >::value>::merge(smem, val, tid, delta, op); + } + template + __device__ __forceinline__ void mergeShfl(const thrust::tuple& val, + unsigned int delta, + unsigned int width, + const thrust::tuple& op) + { + For<0, thrust::tuple_size >::value>::mergeShfl(val, delta, width, op); + } + + template struct Generic + { + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + loadToSmem(smem, val, tid); + if (N >= 32) + __syncthreads(); + + if (N >= 2048) + { + if (tid < 1024) + merge(smem, val, tid, 1024, op); + + __syncthreads(); + } + if (N >= 1024) + { + if (tid < 512) + merge(smem, val, tid, 512, op); + + __syncthreads(); + } + if (N >= 512) + { + if (tid < 256) + merge(smem, val, tid, 256, op); + + __syncthreads(); + } + if (N >= 256) + { + if (tid < 128) + merge(smem, val, tid, 128, op); + + __syncthreads(); + } + if (N >= 128) + { + if (tid < 64) + merge(smem, val, tid, 64, op); + + __syncthreads(); + } + if (N >= 64) + { + if (tid < 32) + merge(smem, val, tid, 32, op); + } + + if (tid < 16) + { + merge(smem, val, tid, 16, op); + merge(smem, val, tid, 8, op); + merge(smem, val, tid, 4, op); + merge(smem, val, tid, 2, op); + merge(smem, val, tid, 1, op); + } + } + }; + + template + struct Unroll + { + static __device__ void loopShfl(Reference val, Op op, unsigned int N) + { + mergeShfl(val, I, N, op); + Unroll::loopShfl(val, op, N); + } + static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op) + { + merge(smem, val, tid, I, op); + Unroll::loop(smem, val, tid, op); + } + }; + template + struct Unroll<0, Pointer, Reference, Op> + { + static __device__ void loopShfl(Reference, Op, unsigned int) + { + } + static __device__ void loop(Pointer, Reference, unsigned int, Op) + { + } + }; + + template struct WarpOptimized + { + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + (void) smem; + (void) tid; + + Unroll::loopShfl(val, op, N); + #else + loadToSmem(smem, val, tid); + + if (tid < N / 2) + Unroll::loop(smem, val, tid, op); + #endif + } + }; + + template struct GenericOptimized32 + { + enum { M = N / 32 }; + + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + const unsigned int laneId = Warp::laneId(); + + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize); + + if (laneId == 0) + loadToSmem(smem, val, tid / 32); + #else + loadToSmem(smem, val, tid); + + if (laneId < 16) + Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op); + + __syncthreads(); + + if (laneId == 0) + loadToSmem(smem, val, tid / 32); + #endif + + __syncthreads(); + + loadFromSmem(smem, val, tid); + + if (tid < 32) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + Unroll::loopShfl(val, op, M); + #else + Unroll::loop(smem, val, tid, op); + #endif + } + } + }; + + template struct StaticIf; + template struct StaticIf + { + typedef T1 type; + }; + template struct StaticIf + { + typedef T2 type; + }; + + template struct IsPowerOf2 + { + enum { value = ((N != 0) && !(N & (N - 1))) }; + }; + + template struct Dispatcher + { + typedef typename StaticIf< + (N <= 32) && IsPowerOf2::value, + WarpOptimized, + typename StaticIf< + (N <= 1024) && IsPowerOf2::value, + GenericOptimized32, + Generic + >::type + >::type reductor; + }; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_REDUCE_DETAIL_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/detail/reduce_key_val.hpp b/thirdparty/linux/include/opencv2/core/cuda/detail/reduce_key_val.hpp new file mode 100644 index 0000000..6a537c9 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/detail/reduce_key_val.hpp @@ -0,0 +1,502 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP +#define OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP + +#include +#include "../warp.hpp" +#include "../warp_shuffle.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace reduce_key_val_detail + { + template struct GetType; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) + { + thrust::get(smem)[tid] = thrust::get(data); + + For::loadToSmem(smem, data, tid); + } + template + static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) + { + thrust::get(data) = thrust::get(smem)[tid]; + + For::loadFromSmem(smem, data, tid); + } + + template + static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width) + { + thrust::get(val) = shfl_down(thrust::get(val), delta, width); + + For::copyShfl(val, delta, width); + } + template + static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta) + { + thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; + + For::copy(svals, val, tid, delta); + } + + template + static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width) + { + typename GetType::type>::type reg = shfl_down(thrust::get(key), delta, width); + + if (thrust::get(cmp)(reg, thrust::get(key))) + { + thrust::get(key) = reg; + thrust::get(val) = shfl_down(thrust::get(val), delta, width); + } + + For::mergeShfl(key, val, cmp, delta, width); + } + template + static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key, + const ValPointerTuple& svals, const ValReferenceTuple& val, + const CmpTuple& cmp, + unsigned int tid, unsigned int delta) + { + typename GetType::type>::type reg = thrust::get(skeys)[tid + delta]; + + if (thrust::get(cmp)(reg, thrust::get(key))) + { + thrust::get(skeys)[tid] = thrust::get(key) = reg; + thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; + } + + For::merge(skeys, key, svals, val, cmp, tid, delta); + } + }; + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) + { + } + template + static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) + { + } + + template + static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int) + { + } + template + static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int) + { + } + + template + static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int) + { + } + template + static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&, + const ValPointerTuple&, const ValReferenceTuple&, + const CmpTuple&, + unsigned int, unsigned int) + { + } + }; + + ////////////////////////////////////////////////////// + // loadToSmem + + template + __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid) + { + smem[tid] = data; + } + template + __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid) + { + data = smem[tid]; + } + template + __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, + const thrust::tuple& data, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadToSmem(smem, data, tid); + } + template + __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, + const thrust::tuple& data, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadFromSmem(smem, data, tid); + } + + ////////////////////////////////////////////////////// + // copyVals + + template + __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width) + { + val = shfl_down(val, delta, width); + } + template + __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta) + { + svals[tid] = val = svals[tid + delta]; + } + template + __device__ __forceinline__ void copyValsShfl(const thrust::tuple& val, + unsigned int delta, + int width) + { + For<0, thrust::tuple_size >::value>::copyShfl(val, delta, width); + } + template + __device__ __forceinline__ void copyVals(const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, unsigned int delta) + { + For<0, thrust::tuple_size >::value>::copy(svals, val, tid, delta); + } + + ////////////////////////////////////////////////////// + // merge + + template + __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width) + { + K reg = shfl_down(key, delta, width); + + if (cmp(reg, key)) + { + key = reg; + copyValsShfl(val, delta, width); + } + } + template + __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta) + { + K reg = skeys[tid + delta]; + + if (cmp(reg, key)) + { + skeys[tid] = key = reg; + copyVals(svals, val, tid, delta); + } + } + template + __device__ __forceinline__ void mergeShfl(K& key, + const thrust::tuple& val, + const Cmp& cmp, + unsigned int delta, int width) + { + K reg = shfl_down(key, delta, width); + + if (cmp(reg, key)) + { + key = reg; + copyValsShfl(val, delta, width); + } + } + template + __device__ __forceinline__ void merge(volatile K* skeys, K& key, + const thrust::tuple& svals, + const thrust::tuple& val, + const Cmp& cmp, unsigned int tid, unsigned int delta) + { + K reg = skeys[tid + delta]; + + if (cmp(reg, key)) + { + skeys[tid] = key = reg; + copyVals(svals, val, tid, delta); + } + } + template + __device__ __forceinline__ void mergeShfl(const thrust::tuple& key, + const thrust::tuple& val, + const thrust::tuple& cmp, + unsigned int delta, int width) + { + For<0, thrust::tuple_size >::value>::mergeShfl(key, val, cmp, delta, width); + } + template + __device__ __forceinline__ void merge(const thrust::tuple& skeys, + const thrust::tuple& key, + const thrust::tuple& svals, + const thrust::tuple& val, + const thrust::tuple& cmp, + unsigned int tid, unsigned int delta) + { + For<0, thrust::tuple_size >::value>::merge(skeys, key, svals, val, cmp, tid, delta); + } + + ////////////////////////////////////////////////////// + // Generic + + template struct Generic + { + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + loadToSmem(skeys, key, tid); + loadValsToSmem(svals, val, tid); + if (N >= 32) + __syncthreads(); + + if (N >= 2048) + { + if (tid < 1024) + merge(skeys, key, svals, val, cmp, tid, 1024); + + __syncthreads(); + } + if (N >= 1024) + { + if (tid < 512) + merge(skeys, key, svals, val, cmp, tid, 512); + + __syncthreads(); + } + if (N >= 512) + { + if (tid < 256) + merge(skeys, key, svals, val, cmp, tid, 256); + + __syncthreads(); + } + if (N >= 256) + { + if (tid < 128) + merge(skeys, key, svals, val, cmp, tid, 128); + + __syncthreads(); + } + if (N >= 128) + { + if (tid < 64) + merge(skeys, key, svals, val, cmp, tid, 64); + + __syncthreads(); + } + if (N >= 64) + { + if (tid < 32) + merge(skeys, key, svals, val, cmp, tid, 32); + } + + if (tid < 16) + { + merge(skeys, key, svals, val, cmp, tid, 16); + merge(skeys, key, svals, val, cmp, tid, 8); + merge(skeys, key, svals, val, cmp, tid, 4); + merge(skeys, key, svals, val, cmp, tid, 2); + merge(skeys, key, svals, val, cmp, tid, 1); + } + } + }; + + template + struct Unroll + { + static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N) + { + mergeShfl(key, val, cmp, I, N); + Unroll::loopShfl(key, val, cmp, N); + } + static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + merge(skeys, key, svals, val, cmp, tid, I); + Unroll::loop(skeys, key, svals, val, tid, cmp); + } + }; + template + struct Unroll<0, KP, KR, VP, VR, Cmp> + { + static __device__ void loopShfl(KR, VR, Cmp, unsigned int) + { + } + static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp) + { + } + }; + + template struct WarpOptimized + { + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + #if 0 // __CUDA_ARCH__ >= 300 + (void) skeys; + (void) svals; + (void) tid; + + Unroll::loopShfl(key, val, cmp, N); + #else + loadToSmem(skeys, key, tid); + loadToSmem(svals, val, tid); + + if (tid < N / 2) + Unroll::loop(skeys, key, svals, val, tid, cmp); + #endif + } + }; + + template struct GenericOptimized32 + { + enum { M = N / 32 }; + + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + const unsigned int laneId = Warp::laneId(); + + #if 0 // __CUDA_ARCH__ >= 300 + Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize); + + if (laneId == 0) + { + loadToSmem(skeys, key, tid / 32); + loadToSmem(svals, val, tid / 32); + } + #else + loadToSmem(skeys, key, tid); + loadToSmem(svals, val, tid); + + if (laneId < 16) + Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp); + + __syncthreads(); + + if (laneId == 0) + { + loadToSmem(skeys, key, tid / 32); + loadToSmem(svals, val, tid / 32); + } + #endif + + __syncthreads(); + + loadFromSmem(skeys, key, tid); + + if (tid < 32) + { + #if 0 // __CUDA_ARCH__ >= 300 + loadFromSmem(svals, val, tid); + + Unroll::loopShfl(key, val, cmp, M); + #else + Unroll::loop(skeys, key, svals, val, tid, cmp); + #endif + } + } + }; + + template struct StaticIf; + template struct StaticIf + { + typedef T1 type; + }; + template struct StaticIf + { + typedef T2 type; + }; + + template struct IsPowerOf2 + { + enum { value = ((N != 0) && !(N & (N - 1))) }; + }; + + template struct Dispatcher + { + typedef typename StaticIf< + (N <= 32) && IsPowerOf2::value, + WarpOptimized, + typename StaticIf< + (N <= 1024) && IsPowerOf2::value, + GenericOptimized32, + Generic + >::type + >::type reductor; + }; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/detail/transform_detail.hpp b/thirdparty/linux/include/opencv2/core/cuda/detail/transform_detail.hpp new file mode 100644 index 0000000..3b72b03 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/detail/transform_detail.hpp @@ -0,0 +1,399 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TRANSFORM_DETAIL_HPP +#define OPENCV_CUDA_TRANSFORM_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" +#include "../functional.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace transform_detail + { + //! Read Write Traits + + template struct UnaryReadWriteTraits + { + typedef typename TypeVec::vec_type read_type; + typedef typename TypeVec::vec_type write_type; + }; + + template struct BinaryReadWriteTraits + { + typedef typename TypeVec::vec_type read_type1; + typedef typename TypeVec::vec_type read_type2; + typedef typename TypeVec::vec_type write_type; + }; + + //! Transform kernels + + template struct OpUnroller; + template <> struct OpUnroller<1> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + } + }; + template <> struct OpUnroller<2> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + } + }; + template <> struct OpUnroller<3> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src.z); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src1.z, src2.z); + } + }; + template <> struct OpUnroller<4> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src.z); + if (mask(y, x_shifted + 3)) + dst.w = op(src.w); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src1.z, src2.z); + if (mask(y, x_shifted + 3)) + dst.w = op(src1.w, src2.w); + } + }; + template <> struct OpUnroller<8> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.a0 = op(src.a0); + if (mask(y, x_shifted + 1)) + dst.a1 = op(src.a1); + if (mask(y, x_shifted + 2)) + dst.a2 = op(src.a2); + if (mask(y, x_shifted + 3)) + dst.a3 = op(src.a3); + if (mask(y, x_shifted + 4)) + dst.a4 = op(src.a4); + if (mask(y, x_shifted + 5)) + dst.a5 = op(src.a5); + if (mask(y, x_shifted + 6)) + dst.a6 = op(src.a6); + if (mask(y, x_shifted + 7)) + dst.a7 = op(src.a7); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.a0 = op(src1.a0, src2.a0); + if (mask(y, x_shifted + 1)) + dst.a1 = op(src1.a1, src2.a1); + if (mask(y, x_shifted + 2)) + dst.a2 = op(src1.a2, src2.a2); + if (mask(y, x_shifted + 3)) + dst.a3 = op(src1.a3, src2.a3); + if (mask(y, x_shifted + 4)) + dst.a4 = op(src1.a4, src2.a4); + if (mask(y, x_shifted + 5)) + dst.a5 = op(src1.a5, src2.a5); + if (mask(y, x_shifted + 6)) + dst.a6 = op(src1.a6, src2.a6); + if (mask(y, x_shifted + 7)) + dst.a7 = op(src1.a7, src2.a7); + } + }; + + template + static __global__ void transformSmart(const PtrStepSz src_, PtrStep dst_, const Mask mask, const UnOp op) + { + typedef TransformFunctorTraits ft; + typedef typename UnaryReadWriteTraits::read_type read_type; + typedef typename UnaryReadWriteTraits::write_type write_type; + + const int x = threadIdx.x + blockIdx.x * blockDim.x; + const int y = threadIdx.y + blockIdx.y * blockDim.y; + const int x_shifted = x * ft::smart_shift; + + if (y < src_.rows) + { + const T* src = src_.ptr(y); + D* dst = dst_.ptr(y); + + if (x_shifted + ft::smart_shift - 1 < src_.cols) + { + const read_type src_n_el = ((const read_type*)src)[x]; + write_type dst_n_el = ((const write_type*)dst)[x]; + + OpUnroller::unroll(src_n_el, dst_n_el, mask, op, x_shifted, y); + + ((write_type*)dst)[x] = dst_n_el; + } + else + { + for (int real_x = x_shifted; real_x < src_.cols; ++real_x) + { + if (mask(y, real_x)) + dst[real_x] = op(src[real_x]); + } + } + } + } + + template + __global__ static void transformSimple(const PtrStepSz src, PtrStep dst, const Mask mask, const UnOp op) + { + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + + if (x < src.cols && y < src.rows && mask(y, x)) + { + dst.ptr(y)[x] = op(src.ptr(y)[x]); + } + } + + template + static __global__ void transformSmart(const PtrStepSz src1_, const PtrStep src2_, PtrStep dst_, + const Mask mask, const BinOp op) + { + typedef TransformFunctorTraits ft; + typedef typename BinaryReadWriteTraits::read_type1 read_type1; + typedef typename BinaryReadWriteTraits::read_type2 read_type2; + typedef typename BinaryReadWriteTraits::write_type write_type; + + const int x = threadIdx.x + blockIdx.x * blockDim.x; + const int y = threadIdx.y + blockIdx.y * blockDim.y; + const int x_shifted = x * ft::smart_shift; + + if (y < src1_.rows) + { + const T1* src1 = src1_.ptr(y); + const T2* src2 = src2_.ptr(y); + D* dst = dst_.ptr(y); + + if (x_shifted + ft::smart_shift - 1 < src1_.cols) + { + const read_type1 src1_n_el = ((const read_type1*)src1)[x]; + const read_type2 src2_n_el = ((const read_type2*)src2)[x]; + write_type dst_n_el = ((const write_type*)dst)[x]; + + OpUnroller::unroll(src1_n_el, src2_n_el, dst_n_el, mask, op, x_shifted, y); + + ((write_type*)dst)[x] = dst_n_el; + } + else + { + for (int real_x = x_shifted; real_x < src1_.cols; ++real_x) + { + if (mask(y, real_x)) + dst[real_x] = op(src1[real_x], src2[real_x]); + } + } + } + } + + template + static __global__ void transformSimple(const PtrStepSz src1, const PtrStep src2, PtrStep dst, + const Mask mask, const BinOp op) + { + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + + if (x < src1.cols && y < src1.rows && mask(y, x)) + { + const T1 src1_data = src1.ptr(y)[x]; + const T2 src2_data = src2.ptr(y)[x]; + dst.ptr(y)[x] = op(src1_data, src2_data); + } + } + + template struct TransformDispatcher; + template<> struct TransformDispatcher + { + template + static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); + const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1); + + transformSimple<<>>(src, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + + template + static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); + const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1); + + transformSimple<<>>(src1, src2, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + }; + template<> struct TransformDispatcher + { + template + static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + CV_StaticAssert(ft::smart_shift != 1, ""); + + if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) || + !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) + { + TransformDispatcher::call(src, dst, op, mask, stream); + return; + } + + const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); + const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1); + + transformSmart<<>>(src, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + + template + static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + CV_StaticAssert(ft::smart_shift != 1, ""); + + if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) || + !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) || + !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) + { + TransformDispatcher::call(src1, src2, dst, op, mask, stream); + return; + } + + const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); + const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1); + + transformSmart<<>>(src1, src2, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + }; + } // namespace transform_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_TRANSFORM_DETAIL_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/detail/type_traits_detail.hpp b/thirdparty/linux/include/opencv2/core/cuda/detail/type_traits_detail.hpp new file mode 100644 index 0000000..a78bd2c --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/detail/type_traits_detail.hpp @@ -0,0 +1,191 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP +#define OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace type_traits_detail + { + template struct Select { typedef T1 type; }; + template struct Select { typedef T2 type; }; + + template struct IsSignedIntergral { enum {value = 0}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + + template struct IsUnsignedIntegral { enum {value = 0}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + + template struct IsIntegral { enum {value = IsSignedIntergral::value || IsUnsignedIntegral::value}; }; + template <> struct IsIntegral { enum {value = 1}; }; + template <> struct IsIntegral { enum {value = 1}; }; + + template struct IsFloat { enum {value = 0}; }; + template <> struct IsFloat { enum {value = 1}; }; + template <> struct IsFloat { enum {value = 1}; }; + + template struct IsVec { enum {value = 0}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + + template struct AddParameterType { typedef const U& type; }; + template struct AddParameterType { typedef U& type; }; + template <> struct AddParameterType { typedef void type; }; + + template struct ReferenceTraits + { + enum { value = false }; + typedef U type; + }; + template struct ReferenceTraits + { + enum { value = true }; + typedef U type; + }; + + template struct PointerTraits + { + enum { value = false }; + typedef void type; + }; + template struct PointerTraits + { + enum { value = true }; + typedef U type; + }; + template struct PointerTraits + { + enum { value = true }; + typedef U type; + }; + + template struct UnConst + { + typedef U type; + enum { value = 0 }; + }; + template struct UnConst + { + typedef U type; + enum { value = 1 }; + }; + template struct UnConst + { + typedef U& type; + enum { value = 1 }; + }; + + template struct UnVolatile + { + typedef U type; + enum { value = 0 }; + }; + template struct UnVolatile + { + typedef U type; + enum { value = 1 }; + }; + template struct UnVolatile + { + typedef U& type; + enum { value = 1 }; + }; + } // namespace type_traits_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/detail/vec_distance_detail.hpp b/thirdparty/linux/include/opencv2/core/cuda/detail/vec_distance_detail.hpp new file mode 100644 index 0000000..8283a99 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/detail/vec_distance_detail.hpp @@ -0,0 +1,121 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP +#define OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP + +#include "../datamov_utils.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace vec_distance_detail + { + template struct UnrollVecDiffCached + { + template + static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind) + { + if (ind < len) + { + T1 val1 = *vecCached++; + + T2 val2; + ForceGlob::Load(vecGlob, ind, val2); + + dist.reduceIter(val1, val2); + + UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM); + } + } + + template + static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist) + { + T1 val1 = *vecCached++; + + T2 val2; + ForceGlob::Load(vecGlob, 0, val2); + vecGlob += THREAD_DIM; + + dist.reduceIter(val1, val2); + + UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob, dist); + } + }; + template struct UnrollVecDiffCached + { + template + static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int) + { + } + + template + static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&) + { + } + }; + + template struct VecDiffCachedCalculator; + template struct VecDiffCachedCalculator + { + template + static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) + { + UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, tid); + } + }; + template struct VecDiffCachedCalculator + { + template + static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) + { + UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob + tid, dist); + } + }; + } // namespace vec_distance_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/dynamic_smem.hpp b/thirdparty/linux/include/opencv2/core/cuda/dynamic_smem.hpp new file mode 100644 index 0000000..42570c6 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/dynamic_smem.hpp @@ -0,0 +1,88 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DYNAMIC_SMEM_HPP +#define OPENCV_CUDA_DYNAMIC_SMEM_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct DynamicSharedMem + { + __device__ __forceinline__ operator T*() + { + extern __shared__ int __smem[]; + return (T*)__smem; + } + + __device__ __forceinline__ operator const T*() const + { + extern __shared__ int __smem[]; + return (T*)__smem; + } + }; + + // specialize for double to avoid unaligned memory access compile errors + template<> struct DynamicSharedMem + { + __device__ __forceinline__ operator double*() + { + extern __shared__ double __smem_d[]; + return (double*)__smem_d; + } + + __device__ __forceinline__ operator const double*() const + { + extern __shared__ double __smem_d[]; + return (double*)__smem_d; + } + }; +}}} + +//! @endcond + +#endif // OPENCV_CUDA_DYNAMIC_SMEM_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/emulation.hpp b/thirdparty/linux/include/opencv2/core/cuda/emulation.hpp new file mode 100644 index 0000000..d346865 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/emulation.hpp @@ -0,0 +1,269 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_EMULATION_HPP_ +#define OPENCV_CUDA_EMULATION_HPP_ + +#include "common.hpp" +#include "warp_reduce.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Emulation + { + + static __device__ __forceinline__ int syncthreadsOr(int pred) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200) + // just campilation stab + return 0; +#else + return __syncthreads_or(pred); +#endif + } + + template + static __forceinline__ __device__ int Ballot(int predicate) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200) + return __ballot(predicate); +#else + __shared__ volatile int cta_buffer[CTA_SIZE]; + + int tid = threadIdx.x; + cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0; + return warp_reduce(cta_buffer); +#endif + } + + struct smem + { + enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U }; + + template + static __device__ __forceinline__ T atomicInc(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count; + unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); + do + { + count = *address & TAG_MASK; + count = tag | (count + 1); + *address = count; + } while (*address != count); + + return (count & TAG_MASK) - 1; +#else + return ::atomicInc(address, val); +#endif + } + + template + static __device__ __forceinline__ T atomicAdd(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count; + unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); + do + { + count = *address & TAG_MASK; + count = tag | (count + val); + *address = count; + } while (*address != count); + + return (count & TAG_MASK) - val; +#else + return ::atomicAdd(address, val); +#endif + } + + template + static __device__ __forceinline__ T atomicMin(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count = ::min(*address, val); + do + { + *address = count; + } while (*address > count); + + return count; +#else + return ::atomicMin(address, val); +#endif + } + }; // struct cmem + + struct glob + { + static __device__ __forceinline__ int atomicAdd(int* address, int val) + { + return ::atomicAdd(address, val); + } + static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val) + { + return ::atomicAdd(address, val); + } + static __device__ __forceinline__ float atomicAdd(float* address, float val) + { + #if __CUDA_ARCH__ >= 200 + return ::atomicAdd(address, val); + #else + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(val + __int_as_float(assumed))); + } while (assumed != old); + return __int_as_float(old); + #endif + } + static __device__ __forceinline__ double atomicAdd(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + __longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); + #else + (void) address; + (void) val; + return 0.0; + #endif + } + + static __device__ __forceinline__ int atomicMin(int* address, int val) + { + return ::atomicMin(address, val); + } + static __device__ __forceinline__ float atomicMin(float* address, float val) + { + #if __CUDA_ARCH__ >= 120 + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fminf(val, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + #else + (void) address; + (void) val; + return 0.0f; + #endif + } + static __device__ __forceinline__ double atomicMin(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(::fmin(val, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); + #else + (void) address; + (void) val; + return 0.0; + #endif + } + + static __device__ __forceinline__ int atomicMax(int* address, int val) + { + return ::atomicMax(address, val); + } + static __device__ __forceinline__ float atomicMax(float* address, float val) + { + #if __CUDA_ARCH__ >= 120 + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fmaxf(val, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + #else + (void) address; + (void) val; + return 0.0f; + #endif + } + static __device__ __forceinline__ double atomicMax(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(::fmax(val, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); + #else + (void) address; + (void) val; + return 0.0; + #endif + } + }; + }; //struct Emulation +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_EMULATION_HPP_ */ diff --git a/thirdparty/linux/include/opencv2/core/cuda/filters.hpp b/thirdparty/linux/include/opencv2/core/cuda/filters.hpp new file mode 100644 index 0000000..c2e24dd --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/filters.hpp @@ -0,0 +1,286 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_FILTERS_HPP +#define OPENCV_CUDA_FILTERS_HPP + +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "vec_math.hpp" +#include "type_traits.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct PointFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + (void)fx; + (void)fy; + } + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + return src(__float2int_rz(y), __float2int_rz(x)); + } + + Ptr2D src; + }; + + template struct LinearFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + (void)fx; + (void)fy; + } + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + typedef typename TypeVec::cn>::vec_type work_type; + + work_type out = VecTraits::all(0); + + const int x1 = __float2int_rd(x); + const int y1 = __float2int_rd(y); + const int x2 = x1 + 1; + const int y2 = y1 + 1; + + elem_type src_reg = src(y1, x1); + out = out + src_reg * ((x2 - x) * (y2 - y)); + + src_reg = src(y1, x2); + out = out + src_reg * ((x - x1) * (y2 - y)); + + src_reg = src(y2, x1); + out = out + src_reg * ((x2 - x) * (y - y1)); + + src_reg = src(y2, x2); + out = out + src_reg * ((x - x1) * (y - y1)); + + return saturate_cast(out); + } + + Ptr2D src; + }; + + template struct CubicFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + typedef typename TypeVec::cn>::vec_type work_type; + + explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + (void)fx; + (void)fy; + } + + static __device__ __forceinline__ float bicubicCoeff(float x_) + { + float x = fabsf(x_); + if (x <= 1.0f) + { + return x * x * (1.5f * x - 2.5f) + 1.0f; + } + else if (x < 2.0f) + { + return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f; + } + else + { + return 0.0f; + } + } + + __device__ elem_type operator ()(float y, float x) const + { + const float xmin = ::ceilf(x - 2.0f); + const float xmax = ::floorf(x + 2.0f); + + const float ymin = ::ceilf(y - 2.0f); + const float ymax = ::floorf(y + 2.0f); + + work_type sum = VecTraits::all(0); + float wsum = 0.0f; + + for (float cy = ymin; cy <= ymax; cy += 1.0f) + { + for (float cx = xmin; cx <= xmax; cx += 1.0f) + { + const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy); + sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx)); + wsum += w; + } + } + + work_type res = (!wsum)? VecTraits::all(0) : sum / wsum; + + return saturate_cast(res); + } + + Ptr2D src; + }; + // for integer scaling + template struct IntegerAreaFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) + : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {} + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + float fsx1 = x * scale_x; + float fsx2 = fsx1 + scale_x; + + int sx1 = __float2int_ru(fsx1); + int sx2 = __float2int_rd(fsx2); + + float fsy1 = y * scale_y; + float fsy2 = fsy1 + scale_y; + + int sy1 = __float2int_ru(fsy1); + int sy2 = __float2int_rd(fsy2); + + typedef typename TypeVec::cn>::vec_type work_type; + work_type out = VecTraits::all(0.f); + + for(int dy = sy1; dy < sy2; ++dy) + for(int dx = sx1; dx < sx2; ++dx) + { + out = out + src(dy, dx) * scale; + } + + return saturate_cast(out); + } + + Ptr2D src; + float scale_x, scale_y ,scale; + }; + + template struct AreaFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) + : src(src_), scale_x(scale_x_), scale_y(scale_y_){} + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + float fsx1 = x * scale_x; + float fsx2 = fsx1 + scale_x; + + int sx1 = __float2int_ru(fsx1); + int sx2 = __float2int_rd(fsx2); + + float fsy1 = y * scale_y; + float fsy2 = fsy1 + scale_y; + + int sy1 = __float2int_ru(fsy1); + int sy2 = __float2int_rd(fsy2); + + float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1)); + + typedef typename TypeVec::cn>::vec_type work_type; + work_type out = VecTraits::all(0.f); + + for (int dy = sy1; dy < sy2; ++dy) + { + for (int dx = sx1; dx < sx2; ++dx) + out = out + src(dy, dx) * scale; + + if (sx1 > fsx1) + out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale); + + if (sx2 < fsx2) + out = out + src(dy, sx2) * ((fsx2 -sx2) * scale); + } + + if (sy1 > fsy1) + for (int dx = sx1; dx < sx2; ++dx) + out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale); + + if (sy2 < fsy2) + for (int dx = sx1; dx < sx2; ++dx) + out = out + src(sy2, dx) * ((fsy2 -sy2) * scale); + + if ((sy1 > fsy1) && (sx1 > fsx1)) + out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale); + + if ((sy1 > fsy1) && (sx2 < fsx2)) + out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale); + + if ((sy2 < fsy2) && (sx2 < fsx2)) + out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale); + + if ((sy2 < fsy2) && (sx1 > fsx1)) + out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale); + + return saturate_cast(out); + } + + Ptr2D src; + float scale_x, scale_y; + int width, haight; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_FILTERS_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/funcattrib.hpp b/thirdparty/linux/include/opencv2/core/cuda/funcattrib.hpp new file mode 100644 index 0000000..f582080 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/funcattrib.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP +#define OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP + +#include + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + void printFuncAttrib(Func& func) + { + + cudaFuncAttributes attrs; + cudaFuncGetAttributes(&attrs, func); + + printf("=== Function stats ===\n"); + printf("Name: \n"); + printf("sharedSizeBytes = %d\n", attrs.sharedSizeBytes); + printf("constSizeBytes = %d\n", attrs.constSizeBytes); + printf("localSizeBytes = %d\n", attrs.localSizeBytes); + printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock); + printf("numRegs = %d\n", attrs.numRegs); + printf("ptxVersion = %d\n", attrs.ptxVersion); + printf("binaryVersion = %d\n", attrs.binaryVersion); + printf("\n"); + fflush(stdout); + } +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/cuda/functional.hpp b/thirdparty/linux/include/opencv2/core/cuda/functional.hpp new file mode 100644 index 0000000..5b8a7eb --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/functional.hpp @@ -0,0 +1,797 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_FUNCTIONAL_HPP +#define OPENCV_CUDA_FUNCTIONAL_HPP + +#include +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "type_traits.hpp" +#include "device_functions.h" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // Function Objects + template struct unary_function : public std::unary_function {}; + template struct binary_function : public std::binary_function {}; + + // Arithmetic Operations + template struct plus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a + b; + } + __host__ __device__ __forceinline__ plus() {} + __host__ __device__ __forceinline__ plus(const plus&) {} + }; + + template struct minus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a - b; + } + __host__ __device__ __forceinline__ minus() {} + __host__ __device__ __forceinline__ minus(const minus&) {} + }; + + template struct multiplies : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a * b; + } + __host__ __device__ __forceinline__ multiplies() {} + __host__ __device__ __forceinline__ multiplies(const multiplies&) {} + }; + + template struct divides : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a / b; + } + __host__ __device__ __forceinline__ divides() {} + __host__ __device__ __forceinline__ divides(const divides&) {} + }; + + template struct modulus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a % b; + } + __host__ __device__ __forceinline__ modulus() {} + __host__ __device__ __forceinline__ modulus(const modulus&) {} + }; + + template struct negate : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a) const + { + return -a; + } + __host__ __device__ __forceinline__ negate() {} + __host__ __device__ __forceinline__ negate(const negate&) {} + }; + + // Comparison Operations + template struct equal_to : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a == b; + } + __host__ __device__ __forceinline__ equal_to() {} + __host__ __device__ __forceinline__ equal_to(const equal_to&) {} + }; + + template struct not_equal_to : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a != b; + } + __host__ __device__ __forceinline__ not_equal_to() {} + __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {} + }; + + template struct greater : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a > b; + } + __host__ __device__ __forceinline__ greater() {} + __host__ __device__ __forceinline__ greater(const greater&) {} + }; + + template struct less : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a < b; + } + __host__ __device__ __forceinline__ less() {} + __host__ __device__ __forceinline__ less(const less&) {} + }; + + template struct greater_equal : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a >= b; + } + __host__ __device__ __forceinline__ greater_equal() {} + __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {} + }; + + template struct less_equal : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a <= b; + } + __host__ __device__ __forceinline__ less_equal() {} + __host__ __device__ __forceinline__ less_equal(const less_equal&) {} + }; + + // Logical Operations + template struct logical_and : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a && b; + } + __host__ __device__ __forceinline__ logical_and() {} + __host__ __device__ __forceinline__ logical_and(const logical_and&) {} + }; + + template struct logical_or : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a || b; + } + __host__ __device__ __forceinline__ logical_or() {} + __host__ __device__ __forceinline__ logical_or(const logical_or&) {} + }; + + template struct logical_not : unary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a) const + { + return !a; + } + __host__ __device__ __forceinline__ logical_not() {} + __host__ __device__ __forceinline__ logical_not(const logical_not&) {} + }; + + // Bitwise Operations + template struct bit_and : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a & b; + } + __host__ __device__ __forceinline__ bit_and() {} + __host__ __device__ __forceinline__ bit_and(const bit_and&) {} + }; + + template struct bit_or : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a | b; + } + __host__ __device__ __forceinline__ bit_or() {} + __host__ __device__ __forceinline__ bit_or(const bit_or&) {} + }; + + template struct bit_xor : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a ^ b; + } + __host__ __device__ __forceinline__ bit_xor() {} + __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {} + }; + + template struct bit_not : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType v) const + { + return ~v; + } + __host__ __device__ __forceinline__ bit_not() {} + __host__ __device__ __forceinline__ bit_not(const bit_not&) {} + }; + + // Generalized Identity Operations + template struct identity : unary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType x) const + { + return x; + } + __host__ __device__ __forceinline__ identity() {} + __host__ __device__ __forceinline__ identity(const identity&) {} + }; + + template struct project1st : binary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return lhs; + } + __host__ __device__ __forceinline__ project1st() {} + __host__ __device__ __forceinline__ project1st(const project1st&) {} + }; + + template struct project2nd : binary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return rhs; + } + __host__ __device__ __forceinline__ project2nd() {} + __host__ __device__ __forceinline__ project2nd(const project2nd&) {} + }; + + // Min/Max Operations + +#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \ + template <> struct name : binary_function \ + { \ + __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \ + __host__ __device__ __forceinline__ name() {}\ + __host__ __device__ __forceinline__ name(const name&) {}\ + }; + + template struct maximum : binary_function + { + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return max(lhs, rhs); + } + __host__ __device__ __forceinline__ maximum() {} + __host__ __device__ __forceinline__ maximum(const maximum&) {} + }; + + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax) + + template struct minimum : binary_function + { + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return min(lhs, rhs); + } + __host__ __device__ __forceinline__ minimum() {} + __host__ __device__ __forceinline__ minimum(const minimum&) {} + }; + + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin) + +#undef OPENCV_CUDA_IMPLEMENT_MINMAX + + // Math functions + + template struct abs_func : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType x) const + { + return abs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned char operator ()(unsigned char x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ signed char operator ()(signed char x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ char operator ()(char x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned short operator ()(unsigned short x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ short operator ()(short x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned int operator ()(unsigned int x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ int operator ()(int x) const + { + return ::abs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ float operator ()(float x) const + { + return ::fabsf(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ double operator ()(double x) const + { + return ::fabs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + +#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \ + template struct name ## _func : unary_function \ + { \ + __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v) const \ + { \ + return func ## f(v); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; \ + template <> struct name ## _func : unary_function \ + { \ + __device__ __forceinline__ double operator ()(double v) const \ + { \ + return func(v); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; + +#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \ + template struct name ## _func : binary_function \ + { \ + __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v1, typename TypeTraits::ParameterType v2) const \ + { \ + return func ## f(v1, v2); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; \ + template <> struct name ## _func : binary_function \ + { \ + __device__ __forceinline__ double operator ()(double v1, double v2) const \ + { \ + return func(v1, v2); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; + + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh) + + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot) + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2) + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow) + + #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR + #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE + #undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR + + template struct hypot_sqr_func : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType src1, typename TypeTraits::ParameterType src2) const + { + return src1 * src1 + src2 * src2; + } + __host__ __device__ __forceinline__ hypot_sqr_func() {} + __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {} + }; + + // Saturate Cast Functor + template struct saturate_cast_func : unary_function + { + __device__ __forceinline__ D operator ()(typename TypeTraits::ParameterType v) const + { + return saturate_cast(v); + } + __host__ __device__ __forceinline__ saturate_cast_func() {} + __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {} + }; + + // Threshold Functors + template struct thresh_binary_func : unary_function + { + __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src > thresh) * maxVal; + } + + __host__ __device__ __forceinline__ thresh_binary_func() {} + __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other) + : thresh(other.thresh), maxVal(other.maxVal) {} + + T thresh; + T maxVal; + }; + + template struct thresh_binary_inv_func : unary_function + { + __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src <= thresh) * maxVal; + } + + __host__ __device__ __forceinline__ thresh_binary_inv_func() {} + __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other) + : thresh(other.thresh), maxVal(other.maxVal) {} + + T thresh; + T maxVal; + }; + + template struct thresh_trunc_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return minimum()(src, thresh); + } + + __host__ __device__ __forceinline__ thresh_trunc_func() {} + __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + template struct thresh_to_zero_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src > thresh) * src; + } + + __host__ __device__ __forceinline__ thresh_to_zero_func() {} + __host__ __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + template struct thresh_to_zero_inv_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src <= thresh) * src; + } + + __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {} + __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + // Function Object Adaptors + template struct unary_negate : unary_function + { + explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {} + + __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x) const + { + return !pred(x); + } + + __host__ __device__ __forceinline__ unary_negate() {} + __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {} + + Predicate pred; + }; + + template __host__ __device__ __forceinline__ unary_negate not1(const Predicate& pred) + { + return unary_negate(pred); + } + + template struct binary_negate : binary_function + { + explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {} + + __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x, + typename TypeTraits::ParameterType y) const + { + return !pred(x,y); + } + + __host__ __device__ __forceinline__ binary_negate() {} + __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {} + + Predicate pred; + }; + + template __host__ __device__ __forceinline__ binary_negate not2(const BinaryPredicate& pred) + { + return binary_negate(pred); + } + + template struct binder1st : unary_function + { + __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {} + + __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const + { + return op(arg1, a); + } + + __host__ __device__ __forceinline__ binder1st() {} + __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {} + + Op op; + typename Op::first_argument_type arg1; + }; + + template __host__ __device__ __forceinline__ binder1st bind1st(const Op& op, const T& x) + { + return binder1st(op, typename Op::first_argument_type(x)); + } + + template struct binder2nd : unary_function + { + __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {} + + __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const + { + return op(a, arg2); + } + + __host__ __device__ __forceinline__ binder2nd() {} + __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {} + + Op op; + typename Op::second_argument_type arg2; + }; + + template __host__ __device__ __forceinline__ binder2nd bind2nd(const Op& op, const T& x) + { + return binder2nd(op, typename Op::second_argument_type(x)); + } + + // Functor Traits + template struct IsUnaryFunction + { + typedef char Yes; + struct No {Yes a[2];}; + + template static Yes check(unary_function); + static No check(...); + + static F makeF(); + + enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; + }; + + template struct IsBinaryFunction + { + typedef char Yes; + struct No {Yes a[2];}; + + template static Yes check(binary_function); + static No check(...); + + static F makeF(); + + enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; + }; + + namespace functional_detail + { + template struct UnOpShift { enum { shift = 1 }; }; + template struct UnOpShift { enum { shift = 4 }; }; + template struct UnOpShift { enum { shift = 2 }; }; + + template struct DefaultUnaryShift + { + enum { shift = UnOpShift::shift }; + }; + + template struct BinOpShift { enum { shift = 1 }; }; + template struct BinOpShift { enum { shift = 4 }; }; + template struct BinOpShift { enum { shift = 2 }; }; + + template struct DefaultBinaryShift + { + enum { shift = BinOpShift::shift }; + }; + + template ::value> struct ShiftDispatcher; + template struct ShiftDispatcher + { + enum { shift = DefaultUnaryShift::shift }; + }; + template struct ShiftDispatcher + { + enum { shift = DefaultBinaryShift::shift }; + }; + } + + template struct DefaultTransformShift + { + enum { shift = functional_detail::ShiftDispatcher::shift }; + }; + + template struct DefaultTransformFunctorTraits + { + enum { simple_block_dim_x = 16 }; + enum { simple_block_dim_y = 16 }; + + enum { smart_block_dim_x = 16 }; + enum { smart_block_dim_y = 16 }; + enum { smart_shift = DefaultTransformShift::shift }; + }; + + template struct TransformFunctorTraits : DefaultTransformFunctorTraits {}; + +#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \ + template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type > +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_FUNCTIONAL_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/limits.hpp b/thirdparty/linux/include/opencv2/core/cuda/limits.hpp new file mode 100644 index 0000000..7e15ed6 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/limits.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_LIMITS_HPP +#define OPENCV_CUDA_LIMITS_HPP + +#include +#include +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ +template struct numeric_limits; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static bool min() { return false; } + __device__ __forceinline__ static bool max() { return true; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static signed char min() { return SCHAR_MIN; } + __device__ __forceinline__ static signed char max() { return SCHAR_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned char min() { return 0; } + __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static short min() { return SHRT_MIN; } + __device__ __forceinline__ static short max() { return SHRT_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned short min() { return 0; } + __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static int min() { return INT_MIN; } + __device__ __forceinline__ static int max() { return INT_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned int min() { return 0; } + __device__ __forceinline__ static unsigned int max() { return UINT_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static float min() { return FLT_MIN; } + __device__ __forceinline__ static float max() { return FLT_MAX; } + __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static double min() { return DBL_MIN; } + __device__ __forceinline__ static double max() { return DBL_MAX; } + __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; } + static const bool is_signed = true; +}; +}}} // namespace cv { namespace cuda { namespace cudev { + +//! @endcond + +#endif // OPENCV_CUDA_LIMITS_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/reduce.hpp b/thirdparty/linux/include/opencv2/core/cuda/reduce.hpp new file mode 100644 index 0000000..5de3650 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/reduce.hpp @@ -0,0 +1,209 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_REDUCE_HPP +#define OPENCV_CUDA_REDUCE_HPP + +#ifndef THRUST_DEBUG // eliminate -Wundef warning +#define THRUST_DEBUG 0 +#endif + +#include +#include "detail/reduce.hpp" +#include "detail/reduce_key_val.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op) + { + reduce_detail::Dispatcher::reductor::template reduce(smem, val, tid, op); + } + template + __device__ __forceinline__ void reduce(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid, + const thrust::tuple& op) + { + reduce_detail::Dispatcher::reductor::template reduce< + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&>(smem, val, tid, op); + } + + template + __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce(skeys, key, svals, val, tid, cmp); + } + template + __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, + const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, const Cmp& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce&, + const thrust::tuple&, + const Cmp&>(skeys, key, svals, val, tid, cmp); + } + template + __device__ __forceinline__ void reduceKeyVal(const thrust::tuple& skeys, + const thrust::tuple& key, + const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, + const thrust::tuple& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce< + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple& + >(skeys, key, svals, val, tid, cmp); + } + + // smem_tuple + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0) + { + return thrust::make_tuple((volatile T0*) t0); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9); + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_REDUCE_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/saturate_cast.hpp b/thirdparty/linux/include/opencv2/core/cuda/saturate_cast.hpp new file mode 100644 index 0000000..c3a3d1c --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/saturate_cast.hpp @@ -0,0 +1,292 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_SATURATE_CAST_HPP +#define OPENCV_CUDA_SATURATE_CAST_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); } + + template<> __device__ __forceinline__ uchar saturate_cast(schar v) + { + uint res = 0; + int vi = v; + asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(ushort v) + { + uint res = 0; + asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(uint v) + { + uint res = 0; + asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(float v) + { + uint res = 0; + asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + uint res = 0; + asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ schar saturate_cast(uchar v) + { + uint res = 0; + uint vi = v; + asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(ushort v) + { + uint res = 0; + asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(uint v) + { + uint res = 0; + asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(float v) + { + uint res = 0; + asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + uint res = 0; + asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ ushort saturate_cast(schar v) + { + ushort res = 0; + int vi = v; + asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(short v) + { + ushort res = 0; + asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(int v) + { + ushort res = 0; + asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(uint v) + { + ushort res = 0; + asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(float v) + { + ushort res = 0; + asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + ushort res = 0; + asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ short saturate_cast(ushort v) + { + short res = 0; + asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(int v) + { + short res = 0; + asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(uint v) + { + short res = 0; + asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(float v) + { + short res = 0; + asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + short res = 0; + asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ int saturate_cast(uint v) + { + int res = 0; + asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ int saturate_cast(float v) + { + return __float2int_rn(v); + } + template<> __device__ __forceinline__ int saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + return __double2int_rn(v); + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ uint saturate_cast(schar v) + { + uint res = 0; + int vi = v; + asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(float v) + { + return __float2uint_rn(v); + } + template<> __device__ __forceinline__ uint saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + return __double2uint_rn(v); + #else + return saturate_cast((float)v); + #endif + } +}}} + +//! @endcond + +#endif /* OPENCV_CUDA_SATURATE_CAST_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/cuda/scan.hpp b/thirdparty/linux/include/opencv2/core/cuda/scan.hpp new file mode 100644 index 0000000..e07ee65 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/scan.hpp @@ -0,0 +1,258 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_SCAN_HPP +#define OPENCV_CUDA_SCAN_HPP + +#include "opencv2/core/cuda/common.hpp" +#include "opencv2/core/cuda/utility.hpp" +#include "opencv2/core/cuda/warp.hpp" +#include "opencv2/core/cuda/warp_shuffle.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 }; + + template struct WarpScan + { + __device__ __forceinline__ WarpScan() {} + __device__ __forceinline__ WarpScan(const WarpScan& other) { (void)other; } + + __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) + { + const unsigned int lane = idx & 31; + F op; + + if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]); + if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]); + if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]); + if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]); + if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]); + + if( Kind == INCLUSIVE ) + return ptr [idx]; + else + return (lane > 0) ? ptr [idx - 1] : 0; + } + + __device__ __forceinline__ unsigned int index(const unsigned int tid) + { + return tid; + } + + __device__ __forceinline__ void init(volatile T *ptr){} + + static const int warp_offset = 0; + + typedef WarpScan merge; + }; + + template struct WarpScanNoComp + { + __device__ __forceinline__ WarpScanNoComp() {} + __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { (void)other; } + + __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) + { + const unsigned int lane = threadIdx.x & 31; + F op; + + ptr [idx ] = op(ptr [idx - 1], ptr [idx]); + ptr [idx ] = op(ptr [idx - 2], ptr [idx]); + ptr [idx ] = op(ptr [idx - 4], ptr [idx]); + ptr [idx ] = op(ptr [idx - 8], ptr [idx]); + ptr [idx ] = op(ptr [idx - 16], ptr [idx]); + + if( Kind == INCLUSIVE ) + return ptr [idx]; + else + return (lane > 0) ? ptr [idx - 1] : 0; + } + + __device__ __forceinline__ unsigned int index(const unsigned int tid) + { + return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask); + } + + __device__ __forceinline__ void init(volatile T *ptr) + { + ptr[threadIdx.x] = 0; + } + + static const int warp_smem_stride = 32 + 16 + 1; + static const int warp_offset = 16; + static const int warp_log = 5; + static const int warp_mask = 31; + + typedef WarpScanNoComp merge; + }; + + template struct BlockScan + { + __device__ __forceinline__ BlockScan() {} + __device__ __forceinline__ BlockScan(const BlockScan& other) { (void)other; } + + __device__ __forceinline__ T operator()(volatile T *ptr) + { + const unsigned int tid = threadIdx.x; + const unsigned int lane = tid & warp_mask; + const unsigned int warp = tid >> warp_log; + + Sc scan; + typename Sc::merge merge_scan; + const unsigned int idx = scan.index(tid); + + T val = scan(ptr, idx); + __syncthreads (); + + if( warp == 0) + scan.init(ptr); + __syncthreads (); + + if( lane == 31 ) + ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx]; + __syncthreads (); + + if( warp == 0 ) + merge_scan(ptr, idx); + __syncthreads(); + + if ( warp > 0) + val = ptr [scan.warp_offset + warp - 1] + val; + __syncthreads (); + + ptr[idx] = val; + __syncthreads (); + + return val ; + } + + static const int warp_log = 5; + static const int warp_mask = 31; + }; + + template + __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid) + { + #if __CUDA_ARCH__ >= 300 + const unsigned int laneId = cv::cuda::device::Warp::laneId(); + + // scan on shuffl functions + #pragma unroll + for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2) + { + const T n = cv::cuda::device::shfl_up(idata, i); + if (laneId >= i) + idata += n; + } + + return idata; + #else + unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1)); + s_Data[pos] = 0; + pos += OPENCV_CUDA_WARP_SIZE; + s_Data[pos] = idata; + + s_Data[pos] += s_Data[pos - 1]; + s_Data[pos] += s_Data[pos - 2]; + s_Data[pos] += s_Data[pos - 4]; + s_Data[pos] += s_Data[pos - 8]; + s_Data[pos] += s_Data[pos - 16]; + + return s_Data[pos]; + #endif + } + + template + __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid) + { + return warpScanInclusive(idata, s_Data, tid) - idata; + } + + template + __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid) + { + if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE) + { + //Bottom-level inclusive warp scan + T warpResult = warpScanInclusive(idata, s_Data, tid); + + //Save top elements of each warp for exclusive warp scan + //sync to wait for warp scans to complete (because s_Data is being overwritten) + __syncthreads(); + if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1)) + { + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult; + } + + //wait for warp scans to complete + __syncthreads(); + + if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) ) + { + //grab top warp elements + T val = s_Data[tid]; + //calculate exclusive scan and write back to shared memory + s_Data[tid] = warpScanExclusive(val, s_Data, tid); + } + + //return updated warp scans with exclusive scan results + __syncthreads(); + + return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE]; + } + else + { + return warpScanInclusive(idata, s_Data, tid); + } + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_SCAN_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/simd_functions.hpp b/thirdparty/linux/include/opencv2/core/cuda/simd_functions.hpp new file mode 100644 index 0000000..3d8c2e0 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/simd_functions.hpp @@ -0,0 +1,869 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +/* + * Copyright (c) 2013 NVIDIA Corporation. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * Redistributions of source code must retain the above copyright notice, + * this list of conditions and the following disclaimer. + * + * Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * Neither the name of NVIDIA Corporation nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef OPENCV_CUDA_SIMD_FUNCTIONS_HPP +#define OPENCV_CUDA_SIMD_FUNCTIONS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // 2 + + static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = a ^ b; // sum bits + r = a + b; // actual sum + s = s ^ r; // determine carry-ins for each bit position + s = s & 0x00010000; // carry-in to high word (= carry-out from low word) + r = r - s; // subtract out carry-out from low word + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = a ^ b; // sum bits + r = a - b; // actual sum + s = s ^ r; // determine carry-ins for each bit position + s = s & 0x00010000; // borrow to high word + r = r + s; // compensate for borrow from low word + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u, v; + s = a & 0x0000ffff; // extract low halfword + r = b & 0x0000ffff; // extract low halfword + u = ::max(r, s); // maximum of low halfwords + v = ::min(r, s); // minimum of low halfwords + s = a & 0xffff0000; // extract high halfword + r = b & 0xffff0000; // extract high halfword + t = ::max(r, s); // maximum of high halfwords + s = ::min(r, s); // minimum of high halfwords + r = u | t; // maximum of both halfwords + s = v | s; // minimum of both halfwords + r = r - s; // |a - b| = max(a,b) - min(a,b); + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b) + { + unsigned int r, s; + + // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> + // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) + s = a ^ b; + r = a & b; + s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries + s = s >> 1; + s = r + s; + + return s; + } + + static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> + // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) + unsigned int s; + s = a ^ b; + r = a | b; + s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries + s = s >> 1; + r = r - s; + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r & ~c; // msb = 1, if r was 0x0000 + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vseteq2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r & ~c; // msb = 1, if r was 0x0000 + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetge2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80008000; // msbs = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetgt2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80008000; // msbs = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetle2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetlt2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r | c; // msb = 1, if r was not 0x0000 + c = c & 0x80008000; // extract msbs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetne2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r | c; // msb = 1, if r was not 0x0000 + c = c & 0x80008000; // extract msbs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u; + r = a & 0x0000ffff; // extract low halfword + s = b & 0x0000ffff; // extract low halfword + t = ::max(r, s); // maximum of low halfwords + r = a & 0xffff0000; // extract high halfword + s = b & 0xffff0000; // extract high halfword + u = ::max(r, s); // maximum of high halfwords + r = t | u; // combine halfword maximums + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u; + r = a & 0x0000ffff; // extract low halfword + s = b & 0x0000ffff; // extract low halfword + t = ::min(r, s); // minimum of low halfwords + r = a & 0xffff0000; // extract high halfword + s = b & 0xffff0000; // extract high halfword + u = ::min(r, s); // minimum of high halfwords + r = t | u; // combine halfword minimums + #endif + + return r; + } + + // 4 + + static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t; + s = a ^ b; // sum bits + r = a & 0x7f7f7f7f; // clear msbs + t = b & 0x7f7f7f7f; // clear msbs + s = s & 0x80808080; // msb sum bits + r = r + t; // add without msbs, record carry-out in msbs + r = r ^ s; // sum of msb sum and carry-in bits, w/o carry-out + #endif /* __CUDA_ARCH__ >= 300 */ + + return r; + } + + static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t; + s = a ^ ~b; // inverted sum bits + r = a | 0x80808080; // set msbs + t = b & 0x7f7f7f7f; // clear msbs + s = s & 0x80808080; // inverted msb sum bits + r = r - t; // subtract w/o msbs, record inverted borrows in msb + r = r ^ s; // combine inverted msb sum bits and borrows + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b) + { + unsigned int r, s; + + // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> + // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) + s = a ^ b; + r = a & b; + s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries + s = s >> 1; + s = r + s; + + return s; + } + + static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> + // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) + unsigned int c; + c = a ^ b; + r = a | b; + c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries + c = c >> 1; + r = r - c; + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x80 + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r & ~c; // msb = 1, if r was 0x00 + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b) + { + unsigned int r, t; + + #if __CUDA_ARCH__ >= 300 + r = vseteq4(a, b); + t = r << 8; // convert bool + r = t - r; // to mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + t = a ^ b; // 0x00 if a == b + r = t | 0x80808080; // set msbs, to catch carry out + t = t ^ r; // extract msbs, msb = 1 if t < 0x80 + r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80 + r = t & ~r; // msb = 1, if t was 0x00 + t = r >> 7; // build mask + t = r - t; // from + r = t | r; // msbs + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetle4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80808080; // msbs = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetlt4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80808080; // msbs = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg4(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b) + { + unsigned int r, s; + + #if __CUDA_ARCH__ >= 300 + r = vsetge4(a, b); + s = r << 8; // convert bool + r = s - r; // to mask + #else + asm ("not.b32 %0,%0;" : "+r"(b)); + r = vavrg4 (a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + r = r & 0x80808080; // msb = carry-outs + s = r >> 7; // build mask + s = r - s; // from + r = s | r; // msbs + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetgt4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r | c; // msb = 1, if r was not 0x00 + c = c & 0x80808080; // extract msbs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetne4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r | c; // msb = 1, if r was not 0x00 + c = c & 0x80808080; // extract msbs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(a, b); // mask = 0xff if a >= b + r = a ^ b; // + s = (r & s) ^ b; // select a when a >= b, else select b => max(a,b) + r = s ^ r; // select a when b >= a, else select b => min(a,b) + r = s - r; // |a - b| = max(a,b) - min(a,b); + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(a, b); // mask = 0xff if a >= b + r = a & s; // select a when b >= a + s = b & ~s; // select b when b < a + r = r | s; // combine byte selections + #endif + + return r; // byte-wise unsigned maximum + } + + static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(b, a); // mask = 0xff if a >= b + r = a & s; // select a when b >= a + s = b & ~s; // select b when b < a + r = r | s; // combine byte selections + #endif + + return r; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_SIMD_FUNCTIONS_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/transform.hpp b/thirdparty/linux/include/opencv2/core/cuda/transform.hpp new file mode 100644 index 0000000..42aa6ea --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/transform.hpp @@ -0,0 +1,75 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TRANSFORM_HPP +#define OPENCV_CUDA_TRANSFORM_HPP + +#include "common.hpp" +#include "utility.hpp" +#include "detail/transform_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + static inline void transform(PtrStepSz src, PtrStepSz dst, UnOp op, const Mask& mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream); + } + + template + static inline void transform(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, const Mask& mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream); + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_TRANSFORM_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/type_traits.hpp b/thirdparty/linux/include/opencv2/core/cuda/type_traits.hpp new file mode 100644 index 0000000..8b7a3fd --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/type_traits.hpp @@ -0,0 +1,90 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TYPE_TRAITS_HPP +#define OPENCV_CUDA_TYPE_TRAITS_HPP + +#include "detail/type_traits_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct IsSimpleParameter + { + enum {value = type_traits_detail::IsIntegral::value || type_traits_detail::IsFloat::value || + type_traits_detail::PointerTraits::type>::value}; + }; + + template struct TypeTraits + { + typedef typename type_traits_detail::UnConst::type NonConstType; + typedef typename type_traits_detail::UnVolatile::type NonVolatileType; + typedef typename type_traits_detail::UnVolatile::type>::type UnqualifiedType; + typedef typename type_traits_detail::PointerTraits::type PointeeType; + typedef typename type_traits_detail::ReferenceTraits::type ReferredType; + + enum { isConst = type_traits_detail::UnConst::value }; + enum { isVolatile = type_traits_detail::UnVolatile::value }; + + enum { isReference = type_traits_detail::ReferenceTraits::value }; + enum { isPointer = type_traits_detail::PointerTraits::type>::value }; + + enum { isUnsignedInt = type_traits_detail::IsUnsignedIntegral::value }; + enum { isSignedInt = type_traits_detail::IsSignedIntergral::value }; + enum { isIntegral = type_traits_detail::IsIntegral::value }; + enum { isFloat = type_traits_detail::IsFloat::value }; + enum { isArith = isIntegral || isFloat }; + enum { isVec = type_traits_detail::IsVec::value }; + + typedef typename type_traits_detail::Select::value, + T, typename type_traits_detail::AddParameterType::type>::type ParameterType; + }; +}}} + +//! @endcond + +#endif // OPENCV_CUDA_TYPE_TRAITS_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/utility.hpp b/thirdparty/linux/include/opencv2/core/cuda/utility.hpp new file mode 100644 index 0000000..7f5db48 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/utility.hpp @@ -0,0 +1,230 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_UTILITY_HPP +#define OPENCV_CUDA_UTILITY_HPP + +#include "saturate_cast.hpp" +#include "datamov_utils.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct CV_EXPORTS ThrustAllocator + { + typedef uchar value_type; + virtual ~ThrustAllocator(); + virtual __device__ __host__ uchar* allocate(size_t numBytes) = 0; + virtual __device__ __host__ void deallocate(uchar* ptr, size_t numBytes) = 0; + static ThrustAllocator& getAllocator(); + static void setAllocator(ThrustAllocator* allocator); + }; + #define OPENCV_CUDA_LOG_WARP_SIZE (5) + #define OPENCV_CUDA_WARP_SIZE (1 << OPENCV_CUDA_LOG_WARP_SIZE) + #define OPENCV_CUDA_LOG_MEM_BANKS ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla + #define OPENCV_CUDA_MEM_BANKS (1 << OPENCV_CUDA_LOG_MEM_BANKS) + + /////////////////////////////////////////////////////////////////////////////// + // swap + + template void __device__ __host__ __forceinline__ swap(T& a, T& b) + { + const T temp = a; + a = b; + b = temp; + } + + /////////////////////////////////////////////////////////////////////////////// + // Mask Reader + + struct SingleMask + { + explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {} + __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){} + + __device__ __forceinline__ bool operator()(int y, int x) const + { + return mask.ptr(y)[x] != 0; + } + + PtrStepb mask; + }; + + struct SingleMaskChannels + { + __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_) + : mask(mask_), channels(channels_) {} + __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_) + :mask(mask_.mask), channels(mask_.channels){} + + __device__ __forceinline__ bool operator()(int y, int x) const + { + return mask.ptr(y)[x / channels] != 0; + } + + PtrStepb mask; + int channels; + }; + + struct MaskCollection + { + explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_) + : maskCollection(maskCollection_) {} + + __device__ __forceinline__ MaskCollection(const MaskCollection& masks_) + : maskCollection(masks_.maskCollection), curMask(masks_.curMask){} + + __device__ __forceinline__ void next() + { + curMask = *maskCollection++; + } + __device__ __forceinline__ void setMask(int z) + { + curMask = maskCollection[z]; + } + + __device__ __forceinline__ bool operator()(int y, int x) const + { + uchar val; + return curMask.data == 0 || (ForceGlob::Load(curMask.ptr(y), x, val), (val != 0)); + } + + const PtrStepb* maskCollection; + PtrStepb curMask; + }; + + struct WithOutMask + { + __host__ __device__ __forceinline__ WithOutMask(){} + __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){} + + __device__ __forceinline__ void next() const + { + } + __device__ __forceinline__ void setMask(int) const + { + } + + __device__ __forceinline__ bool operator()(int, int) const + { + return true; + } + + __device__ __forceinline__ bool operator()(int, int, int) const + { + return true; + } + + static __device__ __forceinline__ bool check(int, int) + { + return true; + } + + static __device__ __forceinline__ bool check(int, int, int) + { + return true; + } + }; + + /////////////////////////////////////////////////////////////////////////////// + // Solve linear system + + // solve 2x2 linear system Ax=b + template __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2]) + { + T det = A[0][0] * A[1][1] - A[1][0] * A[0][1]; + + if (det != 0) + { + double invdet = 1.0 / det; + + x[0] = saturate_cast(invdet * (b[0] * A[1][1] - b[1] * A[0][1])); + + x[1] = saturate_cast(invdet * (A[0][0] * b[1] - A[1][0] * b[0])); + + return true; + } + + return false; + } + + // solve 3x3 linear system Ax=b + template __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3]) + { + T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) + - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]); + + if (det != 0) + { + double invdet = 1.0 / det; + + x[0] = saturate_cast(invdet * + (b[0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) - + A[0][1] * (b[1] * A[2][2] - A[1][2] * b[2] ) + + A[0][2] * (b[1] * A[2][1] - A[1][1] * b[2] ))); + + x[1] = saturate_cast(invdet * + (A[0][0] * (b[1] * A[2][2] - A[1][2] * b[2] ) - + b[0] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + + A[0][2] * (A[1][0] * b[2] - b[1] * A[2][0]))); + + x[2] = saturate_cast(invdet * + (A[0][0] * (A[1][1] * b[2] - b[1] * A[2][1]) - + A[0][1] * (A[1][0] * b[2] - b[1] * A[2][0]) + + b[0] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]))); + + return true; + } + + return false; + } +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_UTILITY_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/vec_distance.hpp b/thirdparty/linux/include/opencv2/core/cuda/vec_distance.hpp new file mode 100644 index 0000000..ef6e510 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/vec_distance.hpp @@ -0,0 +1,232 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_DISTANCE_HPP +#define OPENCV_CUDA_VEC_DISTANCE_HPP + +#include "reduce.hpp" +#include "functional.hpp" +#include "detail/vec_distance_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct L1Dist + { + typedef int value_type; + typedef int result_type; + + __device__ __forceinline__ L1Dist() : mySum(0) {} + + __device__ __forceinline__ void reduceIter(int val1, int val2) + { + mySum = __sad(val1, val2, mySum); + } + + template __device__ __forceinline__ void reduceAll(int* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator int() const + { + return mySum; + } + + int mySum; + }; + template <> struct L1Dist + { + typedef float value_type; + typedef float result_type; + + __device__ __forceinline__ L1Dist() : mySum(0.0f) {} + + __device__ __forceinline__ void reduceIter(float val1, float val2) + { + mySum += ::fabs(val1 - val2); + } + + template __device__ __forceinline__ void reduceAll(float* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator float() const + { + return mySum; + } + + float mySum; + }; + + struct L2Dist + { + typedef float value_type; + typedef float result_type; + + __device__ __forceinline__ L2Dist() : mySum(0.0f) {} + + __device__ __forceinline__ void reduceIter(float val1, float val2) + { + float reg = val1 - val2; + mySum += reg * reg; + } + + template __device__ __forceinline__ void reduceAll(float* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator float() const + { + return sqrtf(mySum); + } + + float mySum; + }; + + struct HammingDist + { + typedef int value_type; + typedef int result_type; + + __device__ __forceinline__ HammingDist() : mySum(0) {} + + __device__ __forceinline__ void reduceIter(int val1, int val2) + { + mySum += __popc(val1 ^ val2); + } + + template __device__ __forceinline__ void reduceAll(int* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator int() const + { + return mySum; + } + + int mySum; + }; + + // calc distance between two vectors in global memory + template + __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) + { + for (int i = tid; i < len; i += THREAD_DIM) + { + T1 val1; + ForceGlob::Load(vec1, i, val1); + + T2 val2; + ForceGlob::Load(vec2, i, val2); + + dist.reduceIter(val1, val2); + } + + dist.reduceAll(smem, tid); + } + + // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory + template + __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid) + { + vec_distance_detail::VecDiffCachedCalculator::calc(vecCached, vecGlob, len, dist, tid); + + dist.reduceAll(smem, tid); + } + + // calc distance between two vectors in global memory + template struct VecDiffGlobal + { + explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0) + { + vec1 = vec1_; + } + + template + __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const + { + calcVecDiffGlobal(vec1, vec2, len, dist, smem, tid); + } + + const T1* vec1; + }; + + // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory + template struct VecDiffCachedRegister + { + template __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid) + { + if (glob_tid < len) + smem[glob_tid] = vec1[glob_tid]; + __syncthreads(); + + U* vec1ValsPtr = vec1Vals; + + #pragma unroll + for (int i = tid; i < MAX_LEN; i += THREAD_DIM) + *vec1ValsPtr++ = smem[i]; + + __syncthreads(); + } + + template + __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const + { + calcVecDiffCached(vec1Vals, vec2, len, dist, smem, tid); + } + + U vec1Vals[MAX_LEN / THREAD_DIM]; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_DISTANCE_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/vec_math.hpp b/thirdparty/linux/include/opencv2/core/cuda/vec_math.hpp new file mode 100644 index 0000000..9085b92 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/vec_math.hpp @@ -0,0 +1,930 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VECMATH_HPP +#define OPENCV_CUDA_VECMATH_HPP + +#include "vec_traits.hpp" +#include "saturate_cast.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + +// saturate_cast + +namespace vec_math_detail +{ + template struct SatCastHelper; + template struct SatCastHelper<1, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x)); + } + }; + template struct SatCastHelper<2, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y)); + } + }; + template struct SatCastHelper<3, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z)); + } + }; + template struct SatCastHelper<4, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z), saturate_cast(v.w)); + } + }; + + template static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v) + { + return SatCastHelper::cn, VecD>::cast(v); + } +} + +template static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper(v);} + +// unary operators + +#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \ + { \ + return VecTraits::make(op (a.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y), op (a.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y), op (a.z), op (a.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP + +// unary functions + +#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \ + { \ + return VecTraits::make(func (a.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y), func (a.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y), func (a.z), func (a.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabs, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double) + +#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC + +// binary operators (vec & vec) + +#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \ + { \ + return VecTraits::make(a.x op b.x); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \ + } + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP + +// binary operators (vec & scalar) + +#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s); \ + } \ + __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \ + { \ + return VecTraits::make(s op b.x); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s, a.z op s); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y, s op b.z); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s, a.z op s, a.w op s); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y, s op b.z, s op b.w); \ + } + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP + +// binary function (vec & vec) + +#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \ + { \ + return VecTraits::make(func (a.x, b.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double) + +#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC + +// binary function (vec & scalar) + +#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \ + } + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double) + +#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC + +}}} // namespace cv { namespace cuda { namespace device + +//! @endcond + +#endif // OPENCV_CUDA_VECMATH_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/vec_traits.hpp b/thirdparty/linux/include/opencv2/core/cuda/vec_traits.hpp new file mode 100644 index 0000000..b5ff281 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/vec_traits.hpp @@ -0,0 +1,288 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_TRAITS_HPP +#define OPENCV_CUDA_VEC_TRAITS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct TypeVec; + + struct __align__(8) uchar8 + { + uchar a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7) + { + uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(8) char8 + { + schar a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) + { + char8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(16) ushort8 + { + ushort a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7) + { + ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(16) short8 + { + short a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7) + { + short8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) uint8 + { + uint a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7) + { + uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) int8 + { + int a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7) + { + int8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) float8 + { + float a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7) + { + float8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct double8 + { + double a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7) + { + double8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + +#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \ + template<> struct TypeVec { typedef type vec_type; }; \ + template<> struct TypeVec { typedef type ## 1 vec_type; }; \ + template<> struct TypeVec { typedef type ## 2 vec_type; }; \ + template<> struct TypeVec { typedef type ## 2 vec_type; }; \ + template<> struct TypeVec { typedef type ## 3 vec_type; }; \ + template<> struct TypeVec { typedef type ## 3 vec_type; }; \ + template<> struct TypeVec { typedef type ## 4 vec_type; }; \ + template<> struct TypeVec { typedef type ## 4 vec_type; }; \ + template<> struct TypeVec { typedef type ## 8 vec_type; }; \ + template<> struct TypeVec { typedef type ## 8 vec_type; }; + + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double) + + #undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC + + template<> struct TypeVec { typedef schar vec_type; }; + template<> struct TypeVec { typedef char2 vec_type; }; + template<> struct TypeVec { typedef char3 vec_type; }; + template<> struct TypeVec { typedef char4 vec_type; }; + template<> struct TypeVec { typedef char8 vec_type; }; + + template<> struct TypeVec { typedef uchar vec_type; }; + template<> struct TypeVec { typedef uchar2 vec_type; }; + template<> struct TypeVec { typedef uchar3 vec_type; }; + template<> struct TypeVec { typedef uchar4 vec_type; }; + template<> struct TypeVec { typedef uchar8 vec_type; }; + + template struct VecTraits; + +#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=1}; \ + static __device__ __host__ __forceinline__ type all(type v) {return v;} \ + static __device__ __host__ __forceinline__ type make(type x) {return x;} \ + static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=1}; \ + static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \ + static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \ + static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=2}; \ + static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \ + static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \ + static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=3}; \ + static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \ + static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=4}; \ + static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \ + static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=8}; \ + static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \ + static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \ + }; + + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double) + + #undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS + + template<> struct VecTraits + { + typedef char elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ char all(char v) {return v;} + static __device__ __host__ __forceinline__ char make(char x) {return x;} + static __device__ __host__ __forceinline__ char make(const char* x) {return *x;} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ schar all(schar v) {return v;} + static __device__ __host__ __forceinline__ schar make(schar x) {return x;} + static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);} + static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);} + static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=2}; + static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);} + static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);} + static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=3}; + static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);} + static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);} + static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=4}; + static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);} + static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);} + static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=8}; + static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);} + static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);} + static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_TRAITS_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda/warp.hpp b/thirdparty/linux/include/opencv2/core/cuda/warp.hpp new file mode 100644 index 0000000..ae1f8ea --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/warp.hpp @@ -0,0 +1,139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_WARP_HPP +#define OPENCV_CUDA_DEVICE_WARP_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Warp + { + enum + { + LOG_WARP_SIZE = 5, + WARP_SIZE = 1 << LOG_WARP_SIZE, + STRIDE = WARP_SIZE + }; + + /** \brief Returns the warp lane ID of the calling thread. */ + static __device__ __forceinline__ unsigned int laneId() + { + unsigned int ret; + asm("mov.u32 %0, %laneid;" : "=r"(ret) ); + return ret; + } + + template + static __device__ __forceinline__ void fill(It beg, It end, const T& value) + { + for(It t = beg + laneId(); t < end; t += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out) + { + for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) + *out = *t; + return out; + } + + template + static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op) + { + for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) + *out = op(*t); + return out; + } + + template + static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + { + unsigned int lane = laneId(); + + InIt1 t1 = beg1 + lane; + InIt2 t2 = beg2 + lane; + for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE) + *out = op(*t1, *t2); + return out; + } + + template + static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op) + { + const unsigned int lane = laneId(); + + if (lane < 16) + { + T partial = ptr[lane]; + + ptr[lane] = partial = op(partial, ptr[lane + 16]); + ptr[lane] = partial = op(partial, ptr[lane + 8]); + ptr[lane] = partial = op(partial, ptr[lane + 4]); + ptr[lane] = partial = op(partial, ptr[lane + 2]); + ptr[lane] = partial = op(partial, ptr[lane + 1]); + } + + return *ptr; + } + + template + static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) + { + unsigned int lane = laneId(); + value += lane; + + for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE) + *t = value; + } + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_WARP_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/cuda/warp_reduce.hpp b/thirdparty/linux/include/opencv2/core/cuda/warp_reduce.hpp new file mode 100644 index 0000000..530303d --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/warp_reduce.hpp @@ -0,0 +1,76 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_WARP_REDUCE_HPP__ +#define OPENCV_CUDA_WARP_REDUCE_HPP__ + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x) + { + const unsigned int lane = tid & 31; // index of thread in warp (0..31) + + if (lane < 16) + { + T partial = ptr[tid]; + + ptr[tid] = partial = partial + ptr[tid + 16]; + ptr[tid] = partial = partial + ptr[tid + 8]; + ptr[tid] = partial = partial + ptr[tid + 4]; + ptr[tid] = partial = partial + ptr[tid + 2]; + ptr[tid] = partial = partial + ptr[tid + 1]; + } + + return ptr[tid - lane]; + } +}}} // namespace cv { namespace cuda { namespace cudev { + +//! @endcond + +#endif /* OPENCV_CUDA_WARP_REDUCE_HPP__ */ diff --git a/thirdparty/linux/include/opencv2/core/cuda/warp_shuffle.hpp b/thirdparty/linux/include/opencv2/core/cuda/warp_shuffle.hpp new file mode 100644 index 0000000..14a9a4d --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda/warp_shuffle.hpp @@ -0,0 +1,153 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_WARP_SHUFFLE_HPP +#define OPENCV_CUDA_WARP_SHUFFLE_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl(val, srcLane, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl((int) val, srcLane, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl(lo, srcLane, width); + hi = __shfl(hi, srcLane, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } + + template + __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl_down(val, delta, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl_down((int) val, delta, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl_down(lo, delta, width); + hi = __shfl_down(hi, delta, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } + + template + __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl_up(val, delta, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl_up((int) val, delta, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl_up(lo, delta, width); + hi = __shfl_up(hi, delta, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_WARP_SHUFFLE_HPP diff --git a/thirdparty/linux/include/opencv2/core/cuda_stream_accessor.hpp b/thirdparty/linux/include/opencv2/core/cuda_stream_accessor.hpp new file mode 100644 index 0000000..deaf356 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda_stream_accessor.hpp @@ -0,0 +1,86 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP +#define OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP + +#ifndef __cplusplus +# error cuda_stream_accessor.hpp header must be compiled as C++ +#endif + +/** @file cuda_stream_accessor.hpp + * This is only header file that depends on CUDA Runtime API. All other headers are independent. + */ + +#include +#include "opencv2/core/cuda.hpp" + +namespace cv +{ + namespace cuda + { + +//! @addtogroup cudacore_struct +//! @{ + + /** @brief Class that enables getting cudaStream_t from cuda::Stream + */ + struct StreamAccessor + { + CV_EXPORTS static cudaStream_t getStream(const Stream& stream); + CV_EXPORTS static Stream wrapStream(cudaStream_t stream); + }; + + /** @brief Class that enables getting cudaEvent_t from cuda::Event + */ + struct EventAccessor + { + CV_EXPORTS static cudaEvent_t getEvent(const Event& event); + CV_EXPORTS static Event wrapEvent(cudaEvent_t event); + }; + +//! @} + + } +} + +#endif /* OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/cuda_types.hpp b/thirdparty/linux/include/opencv2/core/cuda_types.hpp new file mode 100644 index 0000000..f13a847 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cuda_types.hpp @@ -0,0 +1,135 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_TYPES_HPP +#define OPENCV_CORE_CUDA_TYPES_HPP + +#ifndef __cplusplus +# error cuda_types.hpp header must be compiled as C++ +#endif + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +#ifdef __CUDACC__ + #define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__ +#else + #define __CV_CUDA_HOST_DEVICE__ +#endif + +namespace cv +{ + namespace cuda + { + + // Simple lightweight structures that encapsulates information about an image on device. + // It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile + + template struct DevPtr + { + typedef T elem_type; + typedef int index_type; + + enum { elem_size = sizeof(elem_type) }; + + T* data; + + __CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {} + __CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {} + + __CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; } + __CV_CUDA_HOST_DEVICE__ operator T*() { return data; } + __CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; } + }; + + template struct PtrSz : public DevPtr + { + __CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {} + __CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr(data_), size(size_) {} + + size_t size; + }; + + template struct PtrStep : public DevPtr + { + __CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {} + __CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr(data_), step(step_) {} + + size_t step; + + __CV_CUDA_HOST_DEVICE__ T* ptr(int y = 0) { return ( T*)( ( char*)DevPtr::data + y * step); } + __CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)DevPtr::data + y * step); } + + __CV_CUDA_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; } + __CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; } + }; + + template struct PtrStepSz : public PtrStep + { + __CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {} + __CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_) + : PtrStep(data_, step_), cols(cols_), rows(rows_) {} + + template + explicit PtrStepSz(const PtrStepSz& d) : PtrStep((T*)d.data, d.step), cols(d.cols), rows(d.rows){} + + int cols; + int rows; + }; + + typedef PtrStepSz PtrStepSzb; + typedef PtrStepSz PtrStepSzf; + typedef PtrStepSz PtrStepSzi; + + typedef PtrStep PtrStepb; + typedef PtrStep PtrStepf; + typedef PtrStep PtrStepi; + + } +} + +//! @endcond + +#endif /* OPENCV_CORE_CUDA_TYPES_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/cvdef.h b/thirdparty/linux/include/opencv2/core/cvdef.h new file mode 100644 index 0000000..699b166 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cvdef.h @@ -0,0 +1,481 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVDEF_H +#define OPENCV_CORE_CVDEF_H + +//! @addtogroup core_utils +//! @{ + +#if !defined _CRT_SECURE_NO_DEPRECATE && defined _MSC_VER && _MSC_VER > 1300 +# define _CRT_SECURE_NO_DEPRECATE /* to avoid multiple Visual Studio warnings */ +#endif + +// undef problematic defines sometimes defined by system headers (windows.h in particular) +#undef small +#undef min +#undef max +#undef abs +#undef Complex + +#if !defined _CRT_SECURE_NO_DEPRECATE && defined _MSC_VER && _MSC_VER > 1300 +# define _CRT_SECURE_NO_DEPRECATE /* to avoid multiple Visual Studio warnings */ +#endif + +#include +#include "opencv2/core/hal/interface.h" + +#if defined __ICL +# define CV_ICC __ICL +#elif defined __ICC +# define CV_ICC __ICC +#elif defined __ECL +# define CV_ICC __ECL +#elif defined __ECC +# define CV_ICC __ECC +#elif defined __INTEL_COMPILER +# define CV_ICC __INTEL_COMPILER +#endif + +#ifndef CV_INLINE +# if defined __cplusplus +# define CV_INLINE static inline +# elif defined _MSC_VER +# define CV_INLINE __inline +# else +# define CV_INLINE static +# endif +#endif + +#if defined CV_ICC && !defined CV_ENABLE_UNROLLED +# define CV_ENABLE_UNROLLED 0 +#else +# define CV_ENABLE_UNROLLED 1 +#endif + +#ifdef __GNUC__ +# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x))) +#elif defined _MSC_VER +# define CV_DECL_ALIGNED(x) __declspec(align(x)) +#else +# define CV_DECL_ALIGNED(x) +#endif + +/* CPU features and intrinsics support */ +#define CV_CPU_NONE 0 +#define CV_CPU_MMX 1 +#define CV_CPU_SSE 2 +#define CV_CPU_SSE2 3 +#define CV_CPU_SSE3 4 +#define CV_CPU_SSSE3 5 +#define CV_CPU_SSE4_1 6 +#define CV_CPU_SSE4_2 7 +#define CV_CPU_POPCNT 8 +#define CV_CPU_FP16 9 +#define CV_CPU_AVX 10 +#define CV_CPU_AVX2 11 +#define CV_CPU_FMA3 12 + +#define CV_CPU_AVX_512F 13 +#define CV_CPU_AVX_512BW 14 +#define CV_CPU_AVX_512CD 15 +#define CV_CPU_AVX_512DQ 16 +#define CV_CPU_AVX_512ER 17 +#define CV_CPU_AVX_512IFMA512 18 +#define CV_CPU_AVX_512PF 19 +#define CV_CPU_AVX_512VBMI 20 +#define CV_CPU_AVX_512VL 21 + +#define CV_CPU_NEON 100 + +// when adding to this list remember to update the following enum +#define CV_HARDWARE_MAX_FEATURE 255 + +/** @brief Available CPU features. +*/ +enum CpuFeatures { + CPU_MMX = 1, + CPU_SSE = 2, + CPU_SSE2 = 3, + CPU_SSE3 = 4, + CPU_SSSE3 = 5, + CPU_SSE4_1 = 6, + CPU_SSE4_2 = 7, + CPU_POPCNT = 8, + CPU_FP16 = 9, + CPU_AVX = 10, + CPU_AVX2 = 11, + CPU_FMA3 = 12, + + CPU_AVX_512F = 13, + CPU_AVX_512BW = 14, + CPU_AVX_512CD = 15, + CPU_AVX_512DQ = 16, + CPU_AVX_512ER = 17, + CPU_AVX_512IFMA512 = 18, + CPU_AVX_512PF = 19, + CPU_AVX_512VBMI = 20, + CPU_AVX_512VL = 21, + + CPU_NEON = 100 +}; + +// do not include SSE/AVX/NEON headers for NVCC compiler +#ifndef __CUDACC__ + +#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2) +# include +# define CV_MMX 1 +# define CV_SSE 1 +# define CV_SSE2 1 +# if defined __SSE3__ || (defined _MSC_VER && _MSC_VER >= 1500) +# include +# define CV_SSE3 1 +# endif +# if defined __SSSE3__ || (defined _MSC_VER && _MSC_VER >= 1500) +# include +# define CV_SSSE3 1 +# endif +# if defined __SSE4_1__ || (defined _MSC_VER && _MSC_VER >= 1500) +# include +# define CV_SSE4_1 1 +# endif +# if defined __SSE4_2__ || (defined _MSC_VER && _MSC_VER >= 1500) +# include +# define CV_SSE4_2 1 +# endif +# if defined __POPCNT__ || (defined _MSC_VER && _MSC_VER >= 1500) +# ifdef _MSC_VER +# include +# else +# include +# endif +# define CV_POPCNT 1 +# endif +# if defined __AVX__ || (defined _MSC_VER && _MSC_VER >= 1600 && 0) +// MS Visual Studio 2010 (2012?) has no macro pre-defined to identify the use of /arch:AVX +// See: http://connect.microsoft.com/VisualStudio/feedback/details/605858/arch-avx-should-define-a-predefined-macro-in-x64-and-set-a-unique-value-for-m-ix86-fp-in-win32 +# include +# define CV_AVX 1 +# if defined(_XCR_XFEATURE_ENABLED_MASK) +# define __xgetbv() _xgetbv(_XCR_XFEATURE_ENABLED_MASK) +# else +# define __xgetbv() 0 +# endif +# endif +# if defined __AVX2__ || (defined _MSC_VER && _MSC_VER >= 1800 && 0) +# include +# define CV_AVX2 1 +# if defined __FMA__ +# define CV_FMA3 1 +# endif +# endif +#endif + +#if (defined WIN32 || defined _WIN32) && defined(_M_ARM) +# include +# include +# define CV_NEON 1 +# define CPU_HAS_NEON_FEATURE (true) +#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) +# include +# define CV_NEON 1 +#endif + +#if defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__ +# define CV_VFP 1 +#endif + +#endif // __CUDACC__ + +#ifndef CV_POPCNT +#define CV_POPCNT 0 +#endif +#ifndef CV_MMX +# define CV_MMX 0 +#endif +#ifndef CV_SSE +# define CV_SSE 0 +#endif +#ifndef CV_SSE2 +# define CV_SSE2 0 +#endif +#ifndef CV_SSE3 +# define CV_SSE3 0 +#endif +#ifndef CV_SSSE3 +# define CV_SSSE3 0 +#endif +#ifndef CV_SSE4_1 +# define CV_SSE4_1 0 +#endif +#ifndef CV_SSE4_2 +# define CV_SSE4_2 0 +#endif +#ifndef CV_AVX +# define CV_AVX 0 +#endif +#ifndef CV_AVX2 +# define CV_AVX2 0 +#endif +#ifndef CV_FMA3 +# define CV_FMA3 0 +#endif +#ifndef CV_AVX_512F +# define CV_AVX_512F 0 +#endif +#ifndef CV_AVX_512BW +# define CV_AVX_512BW 0 +#endif +#ifndef CV_AVX_512CD +# define CV_AVX_512CD 0 +#endif +#ifndef CV_AVX_512DQ +# define CV_AVX_512DQ 0 +#endif +#ifndef CV_AVX_512ER +# define CV_AVX_512ER 0 +#endif +#ifndef CV_AVX_512IFMA512 +# define CV_AVX_512IFMA512 0 +#endif +#ifndef CV_AVX_512PF +# define CV_AVX_512PF 0 +#endif +#ifndef CV_AVX_512VBMI +# define CV_AVX_512VBMI 0 +#endif +#ifndef CV_AVX_512VL +# define CV_AVX_512VL 0 +#endif + +#ifndef CV_NEON +# define CV_NEON 0 +#endif + +#ifndef CV_VFP +# define CV_VFP 0 +#endif + +/* fundamental constants */ +#define CV_PI 3.1415926535897932384626433832795 +#define CV_2PI 6.283185307179586476925286766559 +#define CV_LOG2 0.69314718055994530941723212145818 + +#if defined __ARM_FP16_FORMAT_IEEE \ + && !defined __CUDACC__ +# define CV_FP16_TYPE 1 +#else +# define CV_FP16_TYPE 0 +#endif + +typedef union Cv16suf +{ + short i; +#if CV_FP16_TYPE + __fp16 h; +#endif + struct _fp16Format + { + unsigned int significand : 10; + unsigned int exponent : 5; + unsigned int sign : 1; + } fmt; +} +Cv16suf; + +typedef union Cv32suf +{ + int i; + unsigned u; + float f; + struct _fp32Format + { + unsigned int significand : 23; + unsigned int exponent : 8; + unsigned int sign : 1; + } fmt; +} +Cv32suf; + +typedef union Cv64suf +{ + int64 i; + uint64 u; + double f; +} +Cv64suf; + +#define OPENCV_ABI_COMPATIBILITY 300 + +#ifdef __OPENCV_BUILD +# define DISABLE_OPENCV_24_COMPATIBILITY +#endif + +#if (defined WIN32 || defined _WIN32 || defined WINCE || defined __CYGWIN__) && defined CVAPI_EXPORTS +# define CV_EXPORTS __declspec(dllexport) +#elif defined __GNUC__ && __GNUC__ >= 4 +# define CV_EXPORTS __attribute__ ((visibility ("default"))) +#else +# define CV_EXPORTS +#endif + +#ifndef CV_EXTERN_C +# ifdef __cplusplus +# define CV_EXTERN_C extern "C" +# else +# define CV_EXTERN_C +# endif +#endif + +/* special informative macros for wrapper generators */ +#define CV_EXPORTS_W CV_EXPORTS +#define CV_EXPORTS_W_SIMPLE CV_EXPORTS +#define CV_EXPORTS_AS(synonym) CV_EXPORTS +#define CV_EXPORTS_W_MAP CV_EXPORTS +#define CV_IN_OUT +#define CV_OUT +#define CV_PROP +#define CV_PROP_RW +#define CV_WRAP +#define CV_WRAP_AS(synonym) + +/****************************************************************************************\ +* Matrix type (Mat) * +\****************************************************************************************/ + +#define CV_MAT_CN_MASK ((CV_CN_MAX - 1) << CV_CN_SHIFT) +#define CV_MAT_CN(flags) ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1) +#define CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX - 1) +#define CV_MAT_TYPE(flags) ((flags) & CV_MAT_TYPE_MASK) +#define CV_MAT_CONT_FLAG_SHIFT 14 +#define CV_MAT_CONT_FLAG (1 << CV_MAT_CONT_FLAG_SHIFT) +#define CV_IS_MAT_CONT(flags) ((flags) & CV_MAT_CONT_FLAG) +#define CV_IS_CONT_MAT CV_IS_MAT_CONT +#define CV_SUBMAT_FLAG_SHIFT 15 +#define CV_SUBMAT_FLAG (1 << CV_SUBMAT_FLAG_SHIFT) +#define CV_IS_SUBMAT(flags) ((flags) & CV_MAT_SUBMAT_FLAG) + +/** Size of each channel item, + 0x124489 = 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */ +#define CV_ELEM_SIZE1(type) \ + ((((sizeof(size_t)<<28)|0x8442211) >> CV_MAT_DEPTH(type)*4) & 15) + +/** 0x3a50 = 11 10 10 01 01 00 00 ~ array of log2(sizeof(arr_type_elem)) */ +#define CV_ELEM_SIZE(type) \ + (CV_MAT_CN(type) << ((((sizeof(size_t)/4+1)*16384|0x3a50) >> CV_MAT_DEPTH(type)*2) & 3)) + +#ifndef MIN +# define MIN(a,b) ((a) > (b) ? (b) : (a)) +#endif + +#ifndef MAX +# define MAX(a,b) ((a) < (b) ? (b) : (a)) +#endif + +/****************************************************************************************\ +* exchange-add operation for atomic operations on reference counters * +\****************************************************************************************/ + +#ifdef CV_XADD + // allow to use user-defined macro +#elif defined __GNUC__ +# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__) +# ifdef __ATOMIC_ACQ_REL +# define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL) +# else +# define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4) +# endif +# else +# if defined __ATOMIC_ACQ_REL && !defined __clang__ + // version for gcc >= 4.7 +# define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL) +# else +# define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta)) +# endif +# endif +#elif defined _MSC_VER && !defined RC_INVOKED +# include +# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta) +#else + CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; } +#endif + + +/****************************************************************************************\ +* CV_NORETURN attribute * +\****************************************************************************************/ + +#ifndef CV_NORETURN +# if defined(__GNUC__) +# define CV_NORETURN __attribute__((__noreturn__)) +# elif defined(_MSC_VER) && (_MSC_VER >= 1300) +# define CV_NORETURN __declspec(noreturn) +# else +# define CV_NORETURN /* nothing by default */ +# endif +#endif + + +/****************************************************************************************\ +* C++ Move semantics * +\****************************************************************************************/ + +#ifndef CV_CXX_MOVE_SEMANTICS +# if __cplusplus >= 201103L || defined(__GXX_EXPERIMENTAL_CXX0X__) || defined(_MSC_VER) && _MSC_VER >= 1600 +# define CV_CXX_MOVE_SEMANTICS 1 +# elif defined(__clang) +# if __has_feature(cxx_rvalue_references) +# define CV_CXX_MOVE_SEMANTICS 1 +# endif +# endif +#else +# if CV_CXX_MOVE_SEMANTICS == 0 +# undef CV_CXX_MOVE_SEMANTICS +# endif +#endif + +//! @} + +#endif // OPENCV_CORE_CVDEF_H diff --git a/thirdparty/linux/include/opencv2/core/cvstd.hpp b/thirdparty/linux/include/opencv2/core/cvstd.hpp new file mode 100644 index 0000000..2d40bd0 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cvstd.hpp @@ -0,0 +1,1066 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVSTD_HPP +#define OPENCV_CORE_CVSTD_HPP + +#ifndef __cplusplus +# error cvstd.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" + +#include +#include +#include + +#ifndef OPENCV_NOSTL +# include +#endif + +// import useful primitives from stl +#ifndef OPENCV_NOSTL_TRANSITIONAL +# include +# include +# include //for abs(int) +# include + +namespace cv +{ + static inline uchar abs(uchar a) { return a; } + static inline ushort abs(ushort a) { return a; } + static inline unsigned abs(unsigned a) { return a; } + static inline uint64 abs(uint64 a) { return a; } + + using std::min; + using std::max; + using std::abs; + using std::swap; + using std::sqrt; + using std::exp; + using std::pow; + using std::log; +} + +#else +namespace cv +{ + template static inline T min(T a, T b) { return a < b ? a : b; } + template static inline T max(T a, T b) { return a > b ? a : b; } + template static inline T abs(T a) { return a < 0 ? -a : a; } + template static inline void swap(T& a, T& b) { T tmp = a; a = b; b = tmp; } + + template<> inline uchar abs(uchar a) { return a; } + template<> inline ushort abs(ushort a) { return a; } + template<> inline unsigned abs(unsigned a) { return a; } + template<> inline uint64 abs(uint64 a) { return a; } +} +#endif + +namespace cv { + +//! @addtogroup core_utils +//! @{ + +//////////////////////////// memory management functions //////////////////////////// + +/** @brief Allocates an aligned memory buffer. + +The function allocates the buffer of the specified size and returns it. When the buffer size is 16 +bytes or more, the returned buffer is aligned to 16 bytes. +@param bufSize Allocated buffer size. + */ +CV_EXPORTS void* fastMalloc(size_t bufSize); + +/** @brief Deallocates a memory buffer. + +The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the +function does nothing. C version of the function clears the pointer *pptr* to avoid problems with +double memory deallocation. +@param ptr Pointer to the allocated buffer. + */ +CV_EXPORTS void fastFree(void* ptr); + +/*! + The STL-compilant memory Allocator based on cv::fastMalloc() and cv::fastFree() +*/ +template class Allocator +{ +public: + typedef _Tp value_type; + typedef value_type* pointer; + typedef const value_type* const_pointer; + typedef value_type& reference; + typedef const value_type& const_reference; + typedef size_t size_type; + typedef ptrdiff_t difference_type; + template class rebind { typedef Allocator other; }; + + explicit Allocator() {} + ~Allocator() {} + explicit Allocator(Allocator const&) {} + template + explicit Allocator(Allocator const&) {} + + // address + pointer address(reference r) { return &r; } + const_pointer address(const_reference r) { return &r; } + + pointer allocate(size_type count, const void* =0) { return reinterpret_cast(fastMalloc(count * sizeof (_Tp))); } + void deallocate(pointer p, size_type) { fastFree(p); } + + void construct(pointer p, const _Tp& v) { new(static_cast(p)) _Tp(v); } + void destroy(pointer p) { p->~_Tp(); } + + size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); } +}; + +//! @} core_utils + +//! @cond IGNORED + +namespace detail +{ + +// Metafunction to avoid taking a reference to void. +template +struct RefOrVoid { typedef T& type; }; + +template<> +struct RefOrVoid{ typedef void type; }; + +template<> +struct RefOrVoid{ typedef const void type; }; + +template<> +struct RefOrVoid{ typedef volatile void type; }; + +template<> +struct RefOrVoid{ typedef const volatile void type; }; + +// This class would be private to Ptr, if it didn't have to be a non-template. +struct PtrOwner; + +} + +template +struct DefaultDeleter +{ + void operator () (Y* p) const; +}; + +//! @endcond + +//! @addtogroup core_basic +//! @{ + +/** @brief Template class for smart pointers with shared ownership + +A Ptr\ pretends to be a pointer to an object of type T. Unlike an ordinary pointer, however, the +object will be automatically cleaned up once all Ptr instances pointing to it are destroyed. + +Ptr is similar to boost::shared_ptr that is part of the Boost library +() and std::shared_ptr from +the [C++11](http://en.wikipedia.org/wiki/C++11) standard. + +This class provides the following advantages: +- Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or C + structure. For some objects, like files, windows, mutexes, sockets, and others, a copy + constructor or an assignment operator are difficult to define. For some other objects, like + complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, + some of complex OpenCV and your own data structures may be written in C. However, copy + constructors and default constructors can simplify programming a lot. Besides, they are often + required (for example, by STL containers). By using a Ptr to such an object instead of the + object itself, you automatically get all of the necessary constructors and the assignment + operator. +- *O(1)* complexity of the above-mentioned operations. While some structures, like std::vector, + provide a copy constructor and an assignment operator, the operations may take a considerable + amount of time if the data structures are large. But if the structures are put into a Ptr, the + overhead is small and independent of the data size. +- Automatic and customizable cleanup, even for C structures. See the example below with FILE\*. +- Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers + can store only objects of the same type and the same size. The classical solution to store + objects of different types in the same container is to store pointers to the base class (Base\*) + instead but then you lose the automatic memory management. Again, by using Ptr\ instead + of raw pointers, you can solve the problem. + +A Ptr is said to *own* a pointer - that is, for each Ptr there is a pointer that will be deleted +once all Ptr instances that own it are destroyed. The owned pointer may be null, in which case +nothing is deleted. Each Ptr also *stores* a pointer. The stored pointer is the pointer the Ptr +pretends to be; that is, the one you get when you use Ptr::get or the conversion to T\*. It's +usually the same as the owned pointer, but if you use casts or the general shared-ownership +constructor, the two may diverge: the Ptr will still own the original pointer, but will itself point +to something else. + +The owned pointer is treated as a black box. The only thing Ptr needs to know about it is how to +delete it. This knowledge is encapsulated in the *deleter* - an auxiliary object that is associated +with the owned pointer and shared between all Ptr instances that own it. The default deleter is an +instance of DefaultDeleter, which uses the standard C++ delete operator; as such it will work with +any pointer allocated with the standard new operator. + +However, if the pointer must be deleted in a different way, you must specify a custom deleter upon +Ptr construction. A deleter is simply a callable object that accepts the pointer as its sole +argument. For example, if you want to wrap FILE, you may do so as follows: +@code + Ptr f(fopen("myfile.txt", "w"), fclose); + if(!f) throw ...; + fprintf(f, ....); + ... + // the file will be closed automatically by f's destructor. +@endcode +Alternatively, if you want all pointers of a particular type to be deleted the same way, you can +specialize DefaultDeleter::operator() for that type, like this: +@code + namespace cv { + template<> void DefaultDeleter::operator ()(FILE * obj) const + { + fclose(obj); + } + } +@endcode +For convenience, the following types from the OpenCV C API already have such a specialization that +calls the appropriate release function: +- CvCapture +- CvFileStorage +- CvHaarClassifierCascade +- CvMat +- CvMatND +- CvMemStorage +- CvSparseMat +- CvVideoWriter +- IplImage +@note The shared ownership mechanism is implemented with reference counting. As such, cyclic +ownership (e.g. when object a contains a Ptr to object b, which contains a Ptr to object a) will +lead to all involved objects never being cleaned up. Avoid such situations. +@note It is safe to concurrently read (but not write) a Ptr instance from multiple threads and +therefore it is normally safe to use it in multi-threaded applications. The same is true for Mat and +other C++ OpenCV classes that use internal reference counts. +*/ +template +struct Ptr +{ + /** Generic programming support. */ + typedef T element_type; + + /** The default constructor creates a null Ptr - one that owns and stores a null pointer. + */ + Ptr(); + + /** + If p is null, these are equivalent to the default constructor. + Otherwise, these constructors assume ownership of p - that is, the created Ptr owns and stores p + and assumes it is the sole owner of it. Don't use them if p is already owned by another Ptr, or + else p will get deleted twice. + With the first constructor, DefaultDeleter\() becomes the associated deleter (so p will + eventually be deleted with the standard delete operator). Y must be a complete type at the point + of invocation. + With the second constructor, d becomes the associated deleter. + Y\* must be convertible to T\*. + @param p Pointer to own. + @note It is often easier to use makePtr instead. + */ + template +#ifdef DISABLE_OPENCV_24_COMPATIBILITY + explicit +#endif + Ptr(Y* p); + + /** @overload + @param d Deleter to use for the owned pointer. + @param p Pointer to own. + */ + template + Ptr(Y* p, D d); + + /** + These constructors create a Ptr that shares ownership with another Ptr - that is, own the same + pointer as o. + With the first two, the same pointer is stored, as well; for the second, Y\* must be convertible + to T\*. + With the third, p is stored, and Y may be any type. This constructor allows to have completely + unrelated owned and stored pointers, and should be used with care to avoid confusion. A relatively + benign use is to create a non-owning Ptr, like this: + @code + ptr = Ptr(Ptr(), dont_delete_me); // owns nothing; will not delete the pointer. + @endcode + @param o Ptr to share ownership with. + */ + Ptr(const Ptr& o); + + /** @overload + @param o Ptr to share ownership with. + */ + template + Ptr(const Ptr& o); + + /** @overload + @param o Ptr to share ownership with. + @param p Pointer to store. + */ + template + Ptr(const Ptr& o, T* p); + + /** The destructor is equivalent to calling Ptr::release. */ + ~Ptr(); + + /** + Assignment replaces the current Ptr instance with one that owns and stores same pointers as o and + then destroys the old instance. + @param o Ptr to share ownership with. + */ + Ptr& operator = (const Ptr& o); + + /** @overload */ + template + Ptr& operator = (const Ptr& o); + + /** If no other Ptr instance owns the owned pointer, deletes it with the associated deleter. Then sets + both the owned and the stored pointers to NULL. + */ + void release(); + + /** + `ptr.reset(...)` is equivalent to `ptr = Ptr(...)`. + @param p Pointer to own. + */ + template + void reset(Y* p); + + /** @overload + @param d Deleter to use for the owned pointer. + @param p Pointer to own. + */ + template + void reset(Y* p, D d); + + /** + Swaps the owned and stored pointers (and deleters, if any) of this and o. + @param o Ptr to swap with. + */ + void swap(Ptr& o); + + /** Returns the stored pointer. */ + T* get() const; + + /** Ordinary pointer emulation. */ + typename detail::RefOrVoid::type operator * () const; + + /** Ordinary pointer emulation. */ + T* operator -> () const; + + /** Equivalent to get(). */ + operator T* () const; + + /** ptr.empty() is equivalent to `!ptr.get()`. */ + bool empty() const; + + /** Returns a Ptr that owns the same pointer as this, and stores the same + pointer as this, except converted via static_cast to Y*. + */ + template + Ptr staticCast() const; + + /** Ditto for const_cast. */ + template + Ptr constCast() const; + + /** Ditto for dynamic_cast. */ + template + Ptr dynamicCast() const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Ptr(Ptr&& o); + Ptr& operator = (Ptr&& o); +#endif + +private: + detail::PtrOwner* owner; + T* stored; + + template + friend struct Ptr; // have to do this for the cross-type copy constructor +}; + +/** Equivalent to ptr1.swap(ptr2). Provided to help write generic algorithms. */ +template +void swap(Ptr& ptr1, Ptr& ptr2); + +/** Return whether ptr1.get() and ptr2.get() are equal and not equal, respectively. */ +template +bool operator == (const Ptr& ptr1, const Ptr& ptr2); +template +bool operator != (const Ptr& ptr1, const Ptr& ptr2); + +/** `makePtr(...)` is equivalent to `Ptr(new T(...))`. It is shorter than the latter, and it's +marginally safer than using a constructor or Ptr::reset, since it ensures that the owned pointer +is new and thus not owned by any other Ptr instance. +Unfortunately, perfect forwarding is impossible to implement in C++03, and so makePtr is limited +to constructors of T that have up to 10 arguments, none of which are non-const references. + */ +template +Ptr makePtr(); +/** @overload */ +template +Ptr makePtr(const A1& a1); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10); + +//////////////////////////////// string class //////////////////////////////// + +class CV_EXPORTS FileNode; //for string constructor from FileNode + +class CV_EXPORTS String +{ +public: + typedef char value_type; + typedef char& reference; + typedef const char& const_reference; + typedef char* pointer; + typedef const char* const_pointer; + typedef ptrdiff_t difference_type; + typedef size_t size_type; + typedef char* iterator; + typedef const char* const_iterator; + + static const size_t npos = size_t(-1); + + explicit String(); + String(const String& str); + String(const String& str, size_t pos, size_t len = npos); + String(const char* s); + String(const char* s, size_t n); + String(size_t n, char c); + String(const char* first, const char* last); + template String(Iterator first, Iterator last); + explicit String(const FileNode& fn); + ~String(); + + String& operator=(const String& str); + String& operator=(const char* s); + String& operator=(char c); + + String& operator+=(const String& str); + String& operator+=(const char* s); + String& operator+=(char c); + + size_t size() const; + size_t length() const; + + char operator[](size_t idx) const; + char operator[](int idx) const; + + const char* begin() const; + const char* end() const; + + const char* c_str() const; + + bool empty() const; + void clear(); + + int compare(const char* s) const; + int compare(const String& str) const; + + void swap(String& str); + String substr(size_t pos = 0, size_t len = npos) const; + + size_t find(const char* s, size_t pos, size_t n) const; + size_t find(char c, size_t pos = 0) const; + size_t find(const String& str, size_t pos = 0) const; + size_t find(const char* s, size_t pos = 0) const; + + size_t rfind(const char* s, size_t pos, size_t n) const; + size_t rfind(char c, size_t pos = npos) const; + size_t rfind(const String& str, size_t pos = npos) const; + size_t rfind(const char* s, size_t pos = npos) const; + + size_t find_first_of(const char* s, size_t pos, size_t n) const; + size_t find_first_of(char c, size_t pos = 0) const; + size_t find_first_of(const String& str, size_t pos = 0) const; + size_t find_first_of(const char* s, size_t pos = 0) const; + + size_t find_last_of(const char* s, size_t pos, size_t n) const; + size_t find_last_of(char c, size_t pos = npos) const; + size_t find_last_of(const String& str, size_t pos = npos) const; + size_t find_last_of(const char* s, size_t pos = npos) const; + + friend String operator+ (const String& lhs, const String& rhs); + friend String operator+ (const String& lhs, const char* rhs); + friend String operator+ (const char* lhs, const String& rhs); + friend String operator+ (const String& lhs, char rhs); + friend String operator+ (char lhs, const String& rhs); + + String toLowerCase() const; + +#ifndef OPENCV_NOSTL + String(const std::string& str); + String(const std::string& str, size_t pos, size_t len = npos); + String& operator=(const std::string& str); + String& operator+=(const std::string& str); + operator std::string() const; + + friend String operator+ (const String& lhs, const std::string& rhs); + friend String operator+ (const std::string& lhs, const String& rhs); +#endif + +private: + char* cstr_; + size_t len_; + + char* allocate(size_t len); // len without trailing 0 + void deallocate(); + + String(int); // disabled and invalid. Catch invalid usages like, commandLineParser.has(0) problem +}; + +//! @} core_basic + +////////////////////////// cv::String implementation ///////////////////////// + +//! @cond IGNORED + +inline +String::String() + : cstr_(0), len_(0) +{} + +inline +String::String(const String& str) + : cstr_(str.cstr_), len_(str.len_) +{ + if (cstr_) + CV_XADD(((int*)cstr_)-1, 1); +} + +inline +String::String(const String& str, size_t pos, size_t len) + : cstr_(0), len_(0) +{ + pos = min(pos, str.len_); + len = min(str.len_ - pos, len); + if (!len) return; + if (len == str.len_) + { + CV_XADD(((int*)str.cstr_)-1, 1); + cstr_ = str.cstr_; + len_ = str.len_; + return; + } + memcpy(allocate(len), str.cstr_ + pos, len); +} + +inline +String::String(const char* s) + : cstr_(0), len_(0) +{ + if (!s) return; + size_t len = strlen(s); + memcpy(allocate(len), s, len); +} + +inline +String::String(const char* s, size_t n) + : cstr_(0), len_(0) +{ + if (!n) return; + memcpy(allocate(n), s, n); +} + +inline +String::String(size_t n, char c) + : cstr_(0), len_(0) +{ + memset(allocate(n), c, n); +} + +inline +String::String(const char* first, const char* last) + : cstr_(0), len_(0) +{ + size_t len = (size_t)(last - first); + memcpy(allocate(len), first, len); +} + +template inline +String::String(Iterator first, Iterator last) + : cstr_(0), len_(0) +{ + size_t len = (size_t)(last - first); + char* str = allocate(len); + while (first != last) + { + *str++ = *first; + ++first; + } +} + +inline +String::~String() +{ + deallocate(); +} + +inline +String& String::operator=(const String& str) +{ + if (&str == this) return *this; + + deallocate(); + if (str.cstr_) CV_XADD(((int*)str.cstr_)-1, 1); + cstr_ = str.cstr_; + len_ = str.len_; + return *this; +} + +inline +String& String::operator=(const char* s) +{ + deallocate(); + if (!s) return *this; + size_t len = strlen(s); + memcpy(allocate(len), s, len); + return *this; +} + +inline +String& String::operator=(char c) +{ + deallocate(); + allocate(1)[0] = c; + return *this; +} + +inline +String& String::operator+=(const String& str) +{ + *this = *this + str; + return *this; +} + +inline +String& String::operator+=(const char* s) +{ + *this = *this + s; + return *this; +} + +inline +String& String::operator+=(char c) +{ + *this = *this + c; + return *this; +} + +inline +size_t String::size() const +{ + return len_; +} + +inline +size_t String::length() const +{ + return len_; +} + +inline +char String::operator[](size_t idx) const +{ + return cstr_[idx]; +} + +inline +char String::operator[](int idx) const +{ + return cstr_[idx]; +} + +inline +const char* String::begin() const +{ + return cstr_; +} + +inline +const char* String::end() const +{ + return len_ ? cstr_ + 1 : 0; +} + +inline +bool String::empty() const +{ + return len_ == 0; +} + +inline +const char* String::c_str() const +{ + return cstr_ ? cstr_ : ""; +} + +inline +void String::swap(String& str) +{ + cv::swap(cstr_, str.cstr_); + cv::swap(len_, str.len_); +} + +inline +void String::clear() +{ + deallocate(); +} + +inline +int String::compare(const char* s) const +{ + if (cstr_ == s) return 0; + return strcmp(c_str(), s); +} + +inline +int String::compare(const String& str) const +{ + if (cstr_ == str.cstr_) return 0; + return strcmp(c_str(), str.c_str()); +} + +inline +String String::substr(size_t pos, size_t len) const +{ + return String(*this, pos, len); +} + +inline +size_t String::find(const char* s, size_t pos, size_t n) const +{ + if (n == 0 || pos + n > len_) return npos; + const char* lmax = cstr_ + len_ - n; + for (const char* i = cstr_ + pos; i <= lmax; ++i) + { + size_t j = 0; + while (j < n && s[j] == i[j]) ++j; + if (j == n) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find(char c, size_t pos) const +{ + return find(&c, pos, 1); +} + +inline +size_t String::find(const String& str, size_t pos) const +{ + return find(str.c_str(), pos, str.len_); +} + +inline +size_t String::find(const char* s, size_t pos) const +{ + if (pos >= len_ || !s[0]) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + size_t j = 0; + while (s[j] && s[j] == i[j]) + { if(i + j >= lmax) return npos; + ++j; + } + if (!s[j]) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::rfind(const char* s, size_t pos, size_t n) const +{ + if (n > len_) return npos; + if (pos > len_ - n) pos = len_ - n; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + size_t j = 0; + while (j < n && s[j] == i[j]) ++j; + if (j == n) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::rfind(char c, size_t pos) const +{ + return rfind(&c, pos, 1); +} + +inline +size_t String::rfind(const String& str, size_t pos) const +{ + return rfind(str.c_str(), pos, str.len_); +} + +inline +size_t String::rfind(const char* s, size_t pos) const +{ + return rfind(s, pos, strlen(s)); +} + +inline +size_t String::find_first_of(const char* s, size_t pos, size_t n) const +{ + if (n == 0 || pos + n > len_) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + for (size_t j = 0; j < n; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_first_of(char c, size_t pos) const +{ + return find_first_of(&c, pos, 1); +} + +inline +size_t String::find_first_of(const String& str, size_t pos) const +{ + return find_first_of(str.c_str(), pos, str.len_); +} + +inline +size_t String::find_first_of(const char* s, size_t pos) const +{ + if (len_ == 0) return npos; + if (pos >= len_ || !s[0]) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + for (size_t j = 0; s[j]; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_last_of(const char* s, size_t pos, size_t n) const +{ + if (len_ == 0) return npos; + if (pos >= len_) pos = len_ - 1; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + for (size_t j = 0; j < n; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_last_of(char c, size_t pos) const +{ + return find_last_of(&c, pos, 1); +} + +inline +size_t String::find_last_of(const String& str, size_t pos) const +{ + return find_last_of(str.c_str(), pos, str.len_); +} + +inline +size_t String::find_last_of(const char* s, size_t pos) const +{ + if (len_ == 0) return npos; + if (pos >= len_) pos = len_ - 1; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + for (size_t j = 0; s[j]; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +String String::toLowerCase() const +{ + String res(cstr_, len_); + + for (size_t i = 0; i < len_; ++i) + res.cstr_[i] = (char) ::tolower(cstr_[i]); + + return res; +} + +//! @endcond + +// ************************* cv::String non-member functions ************************* + +//! @relates cv::String +//! @{ + +inline +String operator + (const String& lhs, const String& rhs) +{ + String s; + s.allocate(lhs.len_ + rhs.len_); + memcpy(s.cstr_, lhs.cstr_, lhs.len_); + memcpy(s.cstr_ + lhs.len_, rhs.cstr_, rhs.len_); + return s; +} + +inline +String operator + (const String& lhs, const char* rhs) +{ + String s; + size_t rhslen = strlen(rhs); + s.allocate(lhs.len_ + rhslen); + memcpy(s.cstr_, lhs.cstr_, lhs.len_); + memcpy(s.cstr_ + lhs.len_, rhs, rhslen); + return s; +} + +inline +String operator + (const char* lhs, const String& rhs) +{ + String s; + size_t lhslen = strlen(lhs); + s.allocate(lhslen + rhs.len_); + memcpy(s.cstr_, lhs, lhslen); + memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); + return s; +} + +inline +String operator + (const String& lhs, char rhs) +{ + String s; + s.allocate(lhs.len_ + 1); + memcpy(s.cstr_, lhs.cstr_, lhs.len_); + s.cstr_[lhs.len_] = rhs; + return s; +} + +inline +String operator + (char lhs, const String& rhs) +{ + String s; + s.allocate(rhs.len_ + 1); + s.cstr_[0] = lhs; + memcpy(s.cstr_ + 1, rhs.cstr_, rhs.len_); + return s; +} + +static inline bool operator== (const String& lhs, const String& rhs) { return 0 == lhs.compare(rhs); } +static inline bool operator== (const char* lhs, const String& rhs) { return 0 == rhs.compare(lhs); } +static inline bool operator== (const String& lhs, const char* rhs) { return 0 == lhs.compare(rhs); } +static inline bool operator!= (const String& lhs, const String& rhs) { return 0 != lhs.compare(rhs); } +static inline bool operator!= (const char* lhs, const String& rhs) { return 0 != rhs.compare(lhs); } +static inline bool operator!= (const String& lhs, const char* rhs) { return 0 != lhs.compare(rhs); } +static inline bool operator< (const String& lhs, const String& rhs) { return lhs.compare(rhs) < 0; } +static inline bool operator< (const char* lhs, const String& rhs) { return rhs.compare(lhs) > 0; } +static inline bool operator< (const String& lhs, const char* rhs) { return lhs.compare(rhs) < 0; } +static inline bool operator<= (const String& lhs, const String& rhs) { return lhs.compare(rhs) <= 0; } +static inline bool operator<= (const char* lhs, const String& rhs) { return rhs.compare(lhs) >= 0; } +static inline bool operator<= (const String& lhs, const char* rhs) { return lhs.compare(rhs) <= 0; } +static inline bool operator> (const String& lhs, const String& rhs) { return lhs.compare(rhs) > 0; } +static inline bool operator> (const char* lhs, const String& rhs) { return rhs.compare(lhs) < 0; } +static inline bool operator> (const String& lhs, const char* rhs) { return lhs.compare(rhs) > 0; } +static inline bool operator>= (const String& lhs, const String& rhs) { return lhs.compare(rhs) >= 0; } +static inline bool operator>= (const char* lhs, const String& rhs) { return rhs.compare(lhs) <= 0; } +static inline bool operator>= (const String& lhs, const char* rhs) { return lhs.compare(rhs) >= 0; } + +//! @} relates cv::String + +} // cv + +#ifndef OPENCV_NOSTL_TRANSITIONAL +namespace std +{ + static inline void swap(cv::String& a, cv::String& b) { a.swap(b); } +} +#else +namespace cv +{ + template<> inline + void swap(cv::String& a, cv::String& b) + { + a.swap(b); + } +} +#endif + +#include "opencv2/core/ptr.inl.hpp" + +#endif //OPENCV_CORE_CVSTD_HPP diff --git a/thirdparty/linux/include/opencv2/core/cvstd.inl.hpp b/thirdparty/linux/include/opencv2/core/cvstd.inl.hpp new file mode 100644 index 0000000..876def8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/cvstd.inl.hpp @@ -0,0 +1,267 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVSTDINL_HPP +#define OPENCV_CORE_CVSTDINL_HPP + +#ifndef OPENCV_NOSTL +# include +# include +#endif + +//! @cond IGNORED + +namespace cv +{ +#ifndef OPENCV_NOSTL + +template class DataType< std::complex<_Tp> > +{ +public: + typedef std::complex<_Tp> value_type; + typedef value_type work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) }; + + typedef Vec vec_type; +}; + +inline +String::String(const std::string& str) + : cstr_(0), len_(0) +{ + if (!str.empty()) + { + size_t len = str.size(); + memcpy(allocate(len), str.c_str(), len); + } +} + +inline +String::String(const std::string& str, size_t pos, size_t len) + : cstr_(0), len_(0) +{ + size_t strlen = str.size(); + pos = min(pos, strlen); + len = min(strlen - pos, len); + if (!len) return; + memcpy(allocate(len), str.c_str() + pos, len); +} + +inline +String& String::operator = (const std::string& str) +{ + deallocate(); + if (!str.empty()) + { + size_t len = str.size(); + memcpy(allocate(len), str.c_str(), len); + } + return *this; +} + +inline +String& String::operator += (const std::string& str) +{ + *this = *this + str; + return *this; +} + +inline +String::operator std::string() const +{ + return std::string(cstr_, len_); +} + +inline +String operator + (const String& lhs, const std::string& rhs) +{ + String s; + size_t rhslen = rhs.size(); + s.allocate(lhs.len_ + rhslen); + memcpy(s.cstr_, lhs.cstr_, lhs.len_); + memcpy(s.cstr_ + lhs.len_, rhs.c_str(), rhslen); + return s; +} + +inline +String operator + (const std::string& lhs, const String& rhs) +{ + String s; + size_t lhslen = lhs.size(); + s.allocate(lhslen + rhs.len_); + memcpy(s.cstr_, lhs.c_str(), lhslen); + memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); + return s; +} + +inline +FileNode::operator std::string() const +{ + String value; + read(*this, value, value); + return value; +} + +template<> inline +void operator >> (const FileNode& n, std::string& value) +{ + String val; + read(n, val, val); + value = val; +} + +template<> inline +FileStorage& operator << (FileStorage& fs, const std::string& value) +{ + return fs << cv::String(value); +} + +static inline +std::ostream& operator << (std::ostream& os, const String& str) +{ + return os << str.c_str(); +} + +static inline +std::ostream& operator << (std::ostream& out, Ptr fmtd) +{ + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + out << str; + return out; +} + +static inline +std::ostream& operator << (std::ostream& out, const Mat& mtx) +{ + return out << Formatter::get()->format(mtx); +} + +template static inline +std::ostream& operator << (std::ostream& out, const std::vector >& vec) +{ + return out << Formatter::get()->format(Mat(vec)); +} + + +template static inline +std::ostream& operator << (std::ostream& out, const std::vector >& vec) +{ + return out << Formatter::get()->format(Mat(vec)); +} + + +template static inline +std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx) +{ + return out << Formatter::get()->format(Mat(matx)); +} + +template static inline +std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p) +{ + out << "[" << p.x << ", " << p.y << "]"; + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p) +{ + out << "[" << p.x << ", " << p.y << ", " << p.z << "]"; + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec) +{ + out << "["; +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable: 4127 ) +#endif + if(Vec<_Tp, n>::depth < CV_32F) +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + { + for (int i = 0; i < n - 1; ++i) { + out << (int)vec[i] << ", "; + } + out << (int)vec[n-1] << "]"; + } + else + { + for (int i = 0; i < n - 1; ++i) { + out << vec[i] << ", "; + } + out << vec[n-1] << "]"; + } + + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size) +{ + return out << "[" << size.width << " x " << size.height << "]"; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect) +{ + return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]"; +} + + +#endif // OPENCV_NOSTL +} // cv + +//! @endcond + +#endif // OPENCV_CORE_CVSTDINL_HPP diff --git a/thirdparty/linux/include/opencv2/core/directx.hpp b/thirdparty/linux/include/opencv2/core/directx.hpp new file mode 100644 index 0000000..056a85a --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/directx.hpp @@ -0,0 +1,184 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors as is and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the copyright holders or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_DIRECTX_HPP +#define OPENCV_CORE_DIRECTX_HPP + +#include "mat.hpp" +#include "ocl.hpp" + +#if !defined(__d3d11_h__) +struct ID3D11Device; +struct ID3D11Texture2D; +#endif + +#if !defined(__d3d10_h__) +struct ID3D10Device; +struct ID3D10Texture2D; +#endif + +#if !defined(_D3D9_H_) +struct IDirect3DDevice9; +struct IDirect3DDevice9Ex; +struct IDirect3DSurface9; +#endif + + +namespace cv { namespace directx { + +namespace ocl { +using namespace cv::ocl; + +//! @addtogroup core_directx +// This section describes OpenCL and DirectX interoperability. +// +// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is +// supported only on Windows. +// +// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource. +// +//! @{ + +// TODO static functions in the Context class +//! @brief Creates OpenCL context from D3D11 device +// +//! @param pD3D11Device - pointer to D3D11 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device); + +//! @brief Creates OpenCL context from D3D10 device +// +//! @param pD3D10Device - pointer to D3D10 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device); + +//! @brief Creates OpenCL context from Direct3DDevice9Ex device +// +//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex); + +//! @brief Creates OpenCL context from Direct3DDevice9 device +// +//! @param pDirect3DDevice9 - pointer to Direct3Device9 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9); + +//! @} + +} // namespace cv::directx::ocl + +//! @addtogroup core_directx +//! @{ + +//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then +//! input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12. +// +//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to +//! pD3D11Texture2D +// +//! @param src - source InputArray +//! @param pD3D11Texture2D - destination D3D11 texture +CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D); + +//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then +//! data will be upsampled and color-converted to BGR format. +// +//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size. +//! function does memory copy from pD3D11Texture2D to dst +// +//! @param pD3D11Texture2D - source D3D11 texture +//! @param dst - destination OutputArray +CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst); + +//! @brief Converts InputArray to ID3D10Texture2D +// +//! @note Note: function does memory copy from src to +//! pD3D10Texture2D +// +//! @param src - source InputArray +//! @param pD3D10Texture2D - destination D3D10 texture +CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D); + +//! @brief Converts ID3D10Texture2D to OutputArray +// +//! @note Note: function does memory copy from pD3D10Texture2D +//! to dst +// +//! @param pD3D10Texture2D - source D3D10 texture +//! @param dst - destination OutputArray +CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst); + +//! @brief Converts InputArray to IDirect3DSurface9 +// +//! @note Note: function does memory copy from src to +//! pDirect3DSurface9 +// +//! @param src - source InputArray +//! @param pDirect3DSurface9 - destination D3D10 texture +//! @param surfaceSharedHandle - shared handle +CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL); + +//! @brief Converts IDirect3DSurface9 to OutputArray +// +//! @note Note: function does memory copy from pDirect3DSurface9 +//! to dst +// +//! @param pDirect3DSurface9 - source D3D10 texture +//! @param dst - destination OutputArray +//! @param surfaceSharedHandle - shared handle +CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL); + +//! @brief Get OpenCV type from DirectX type +//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11 +//! @return OpenCV type or -1 if there is no equivalent +CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11 + +//! @brief Get OpenCV type from DirectX type +//! @param iD3DFORMAT - enum D3DTYPE for D3D9 +//! @return OpenCV type or -1 if there is no equivalent +CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9 + +//! @} + +} } // namespace cv::directx + +#endif // OPENCV_CORE_DIRECTX_HPP diff --git a/thirdparty/linux/include/opencv2/core/eigen.hpp b/thirdparty/linux/include/opencv2/core/eigen.hpp new file mode 100644 index 0000000..c2f1ee6 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/eigen.hpp @@ -0,0 +1,280 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_CORE_EIGEN_HPP +#define OPENCV_CORE_EIGEN_HPP + +#include "opencv2/core.hpp" + +#if defined _MSC_VER && _MSC_VER >= 1200 +#pragma warning( disable: 4714 ) //__forceinline is not inlined +#pragma warning( disable: 4127 ) //conditional expression is constant +#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data +#endif + +namespace cv +{ + +//! @addtogroup core_eigen +//! @{ + +template static inline +void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, Mat& dst ) +{ + if( !(src.Flags & Eigen::RowMajorBit) ) + { + Mat _src(src.cols(), src.rows(), DataType<_Tp>::type, + (void*)src.data(), src.stride()*sizeof(_Tp)); + transpose(_src, dst); + } + else + { + Mat _src(src.rows(), src.cols(), DataType<_Tp>::type, + (void*)src.data(), src.stride()*sizeof(_Tp)); + _src.copyTo(dst); + } +} + +// Matx case +template static inline +void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, + Matx<_Tp, _rows, _cols>& dst ) +{ + if( !(src.Flags & Eigen::RowMajorBit) ) + { + dst = Matx<_Tp, _cols, _rows>(static_cast(src.data())).t(); + } + else + { + dst = Matx<_Tp, _rows, _cols>(static_cast(src.data())); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) +{ + CV_DbgAssert(src.rows == _rows && src.cols == _cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else if( src.cols == src.rows ) + { + src.convertTo(_dst, _dst.type()); + transpose(_dst, _dst); + } + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, _cols>& src, + Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) +{ + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, _rows, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, _cols, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) +{ + dst.resize(src.rows, src.cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else if( src.cols == src.rows ) + { + src.convertTo(_dst, _dst.type()); + transpose(_dst, _dst); + } + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, _cols>& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) +{ + dst.resize(_rows, _cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, _rows, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, _cols, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) +{ + CV_Assert(src.cols == 1); + dst.resize(src.rows); + + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, 1>& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) +{ + dst.resize(_rows); + + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(1, _rows, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, 1, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + src.copyTo(_dst); + } +} + + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) +{ + CV_Assert(src.rows == 1); + dst.resize(src.cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +//Matx +template static inline +void cv2eigen( const Matx<_Tp, 1, _cols>& src, + Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) +{ + dst.resize(_cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, 1, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(1, _cols, DataType<_Tp>::type, + dst.data(), (size_t)(dst.stride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +//! @} + +} // cv + +#endif diff --git a/thirdparty/linux/include/opencv2/core/fast_math.hpp b/thirdparty/linux/include/opencv2/core/fast_math.hpp new file mode 100644 index 0000000..c76936a --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/fast_math.hpp @@ -0,0 +1,303 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_FAST_MATH_HPP +#define OPENCV_CORE_FAST_MATH_HPP + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils +//! @{ + +/****************************************************************************************\ +* fast math * +\****************************************************************************************/ + +#if defined __BORLANDC__ +# include +#elif defined __cplusplus +# include +#else +# include +#endif + +#ifdef HAVE_TEGRA_OPTIMIZATION +# include "tegra_round.hpp" +#endif + +#if CV_VFP + // 1. general scheme + #define ARM_ROUND(_value, _asm_string) \ + int res; \ + float temp; \ + (void)temp; \ + asm(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \ + return res + // 2. version for double + #ifdef __clang__ + #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]") + #else + #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]") + #endif + // 3. version for float + #define ARM_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]") +#endif // CV_VFP + +/** @brief Rounds floating-point number to the nearest integer + + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int +cvRound( double value ) +{ +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) + __m128d t = _mm_set_sd( value ); + return _mm_cvtsd_si32(t); +#elif defined _MSC_VER && defined _M_IX86 + int t; + __asm + { + fld value; + fistp t; + } + return t; +#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ + defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION + TEGRA_ROUND_DBL(value); +#elif defined CV_ICC || defined __GNUC__ +# if CV_VFP + ARM_ROUND_DBL(value); +# else + return (int)lrint(value); +# endif +#else + /* it's ok if round does not comply with IEEE754 standard; + the tests should allow +/-1 difference when the tested functions use round */ + return (int)(value + (value >= 0 ? 0.5 : -0.5)); +#endif +} + + +/** @brief Rounds floating-point number to the nearest integer not larger than the original. + + The function computes an integer i such that: + \f[i \le \texttt{value} < i+1\f] + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int cvFloor( double value ) +{ +#if (defined _MSC_VER && defined _M_X64 || (defined __GNUC__ && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) + __m128d t = _mm_set_sd( value ); + int i = _mm_cvtsd_si32(t); + return i - _mm_movemask_pd(_mm_cmplt_sd(t, _mm_cvtsi32_sd(t,i))); +#elif defined __GNUC__ + int i = (int)value; + return i - (i > value); +#else + int i = cvRound(value); + float diff = (float)(value - i); + return i - (diff < 0); +#endif +} + +/** @brief Rounds floating-point number to the nearest integer not smaller than the original. + + The function computes an integer i such that: + \f[i \le \texttt{value} < i+1\f] + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int cvCeil( double value ) +{ +#if (defined _MSC_VER && defined _M_X64 || (defined __GNUC__ && defined __SSE2__&& !defined __APPLE__)) && !defined(__CUDACC__) + __m128d t = _mm_set_sd( value ); + int i = _mm_cvtsd_si32(t); + return i + _mm_movemask_pd(_mm_cmplt_sd(_mm_cvtsi32_sd(t,i), t)); +#elif defined __GNUC__ + int i = (int)value; + return i + (i < value); +#else + int i = cvRound(value); + float diff = (float)(i - value); + return i + (diff < 0); +#endif +} + +/** @brief Determines if the argument is Not A Number. + + @param value The input floating-point value + + The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0 + otherwise. */ +CV_INLINE int cvIsNaN( double value ) +{ + Cv64suf ieee754; + ieee754.f = value; + return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) + + ((unsigned)ieee754.u != 0) > 0x7ff00000; +} + +/** @brief Determines if the argument is Infinity. + + @param value The input floating-point value + + The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard) + and 0 otherwise. */ +CV_INLINE int cvIsInf( double value ) +{ + Cv64suf ieee754; + ieee754.f = value; + return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 && + (unsigned)ieee754.u == 0; +} + +#ifdef __cplusplus + +/** @overload */ +CV_INLINE int cvRound(float value) +{ +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ && \ + defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) + __m128 t = _mm_set_ss( value ); + return _mm_cvtss_si32(t); +#elif defined _MSC_VER && defined _M_IX86 + int t; + __asm + { + fld value; + fistp t; + } + return t; +#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ + defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION + TEGRA_ROUND_FLT(value); +#elif defined CV_ICC || defined __GNUC__ +# if CV_VFP + ARM_ROUND_FLT(value); +# else + return (int)lrintf(value); +# endif +#else + /* it's ok if round does not comply with IEEE754 standard; + the tests should allow +/-1 difference when the tested functions use round */ + return (int)(value + (value >= 0 ? 0.5f : -0.5f)); +#endif +} + +/** @overload */ +CV_INLINE int cvRound( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvFloor( float value ) +{ +#if (defined _MSC_VER && defined _M_X64 || (defined __GNUC__ && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) + __m128 t = _mm_set_ss( value ); + int i = _mm_cvtss_si32(t); + return i - _mm_movemask_ps(_mm_cmplt_ss(t, _mm_cvtsi32_ss(t,i))); +#elif defined __GNUC__ + int i = (int)value; + return i - (i > value); +#else + int i = cvRound(value); + float diff = (float)(value - i); + return i - (diff < 0); +#endif +} + +/** @overload */ +CV_INLINE int cvFloor( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvCeil( float value ) +{ +#if (defined _MSC_VER && defined _M_X64 || (defined __GNUC__ && defined __SSE2__&& !defined __APPLE__)) && !defined(__CUDACC__) + __m128 t = _mm_set_ss( value ); + int i = _mm_cvtss_si32(t); + return i + _mm_movemask_ps(_mm_cmplt_ss(_mm_cvtsi32_ss(t,i), t)); +#elif defined __GNUC__ + int i = (int)value; + return i + (i < value); +#else + int i = cvRound(value); + float diff = (float)(i - value); + return i + (diff < 0); +#endif +} + +/** @overload */ +CV_INLINE int cvCeil( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvIsNaN( float value ) +{ + Cv32suf ieee754; + ieee754.f = value; + return (ieee754.u & 0x7fffffff) > 0x7f800000; +} + +/** @overload */ +CV_INLINE int cvIsInf( float value ) +{ + Cv32suf ieee754; + ieee754.f = value; + return (ieee754.u & 0x7fffffff) == 0x7f800000; +} + +#endif // __cplusplus + +//! @} core_utils + +#endif diff --git a/thirdparty/linux/include/opencv2/core/hal/hal.hpp b/thirdparty/linux/include/opencv2/core/hal/hal.hpp new file mode 100644 index 0000000..68900ec --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/hal/hal.hpp @@ -0,0 +1,250 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_HPP +#define OPENCV_HAL_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/hal/interface.h" + +namespace cv { namespace hal { + +//! @addtogroup core_hal_functions +//! @{ + +CV_EXPORTS int normHamming(const uchar* a, int n); +CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n); + +CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize); +CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize); + +CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags); +CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags); +CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors); +CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors); + +CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step, + float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step, + double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step, + float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step, + double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); + +CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n); +CV_EXPORTS float normL1_(const float* a, const float* b, int n); +CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n); + +CV_EXPORTS void exp32f(const float* src, float* dst, int n); +CV_EXPORTS void exp64f(const double* src, double* dst, int n); +CV_EXPORTS void log32f(const float* src, float* dst, int n); +CV_EXPORTS void log64f(const double* src, double* dst, int n); + +CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees); +CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees); +CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n); +CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n); +CV_EXPORTS void sqrt32f(const float* src, float* dst, int len); +CV_EXPORTS void sqrt64f(const double* src, double* dst, int len); +CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len); +CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len); + +CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn ); +CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn ); +CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn ); +CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn ); + +CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn ); +CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn ); +CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn ); +CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn ); + +CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); + +CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars ); +CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars ); + +struct CV_EXPORTS DFT1D +{ + static Ptr create(int len, int count, int depth, int flags, bool * useBuffer = 0); + virtual void apply(const uchar *src, uchar *dst) = 0; + virtual ~DFT1D() {} +}; + +struct CV_EXPORTS DFT2D +{ + static Ptr create(int width, int height, int depth, + int src_channels, int dst_channels, + int flags, int nonzero_rows = 0); + virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; + virtual ~DFT2D() {} +}; + +struct CV_EXPORTS DCT2D +{ + static Ptr create(int width, int height, int depth, int flags); + virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; + virtual ~DCT2D() {} +}; + +//! @} core_hal + +//============================================================================= +// for binary compatibility with 3.0 + +//! @cond IGNORED + +CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); + +CV_EXPORTS void exp(const float* src, float* dst, int n); +CV_EXPORTS void exp(const double* src, double* dst, int n); +CV_EXPORTS void log(const float* src, float* dst, int n); +CV_EXPORTS void log(const double* src, double* dst, int n); + +CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees); +CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n); +CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n); +CV_EXPORTS void sqrt(const float* src, float* dst, int len); +CV_EXPORTS void sqrt(const double* src, double* dst, int len); +CV_EXPORTS void invSqrt(const float* src, float* dst, int len); +CV_EXPORTS void invSqrt(const double* src, double* dst, int len); + +//! @endcond + +}} //cv::hal + +#endif //OPENCV_HAL_HPP diff --git a/thirdparty/linux/include/opencv2/core/hal/interface.h b/thirdparty/linux/include/opencv2/core/hal/interface.h new file mode 100644 index 0000000..4a97e65 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/hal/interface.h @@ -0,0 +1,178 @@ +#ifndef OPENCV_CORE_HAL_INTERFACE_H +#define OPENCV_CORE_HAL_INTERFACE_H + +//! @addtogroup core_hal_interface +//! @{ + +//! @name Return codes +//! @{ +#define CV_HAL_ERROR_OK 0 +#define CV_HAL_ERROR_NOT_IMPLEMENTED 1 +#define CV_HAL_ERROR_UNKNOWN -1 +//! @} + +#ifdef __cplusplus +#include +#else +#include +#include +#endif + +//! @name Data types +//! primitive types +//! - schar - signed 1 byte integer +//! - uchar - unsigned 1 byte integer +//! - short - signed 2 byte integer +//! - ushort - unsigned 2 byte integer +//! - int - signed 4 byte integer +//! - uint - unsigned 4 byte integer +//! - int64 - signed 8 byte integer +//! - uint64 - unsigned 8 byte integer +//! @{ +#if !defined _MSC_VER && !defined __BORLANDC__ +# if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__ +# include + typedef std::uint32_t uint; +# else +# include + typedef uint32_t uint; +# endif +#else + typedef unsigned uint; +#endif + +typedef signed char schar; + +#ifndef __IPL_H__ + typedef unsigned char uchar; + typedef unsigned short ushort; +#endif + +#if defined _MSC_VER || defined __BORLANDC__ + typedef __int64 int64; + typedef unsigned __int64 uint64; +# define CV_BIG_INT(n) n##I64 +# define CV_BIG_UINT(n) n##UI64 +#else + typedef int64_t int64; + typedef uint64_t uint64; +# define CV_BIG_INT(n) n##LL +# define CV_BIG_UINT(n) n##ULL +#endif + +#define CV_CN_MAX 512 +#define CV_CN_SHIFT 3 +#define CV_DEPTH_MAX (1 << CV_CN_SHIFT) + +#define CV_8U 0 +#define CV_8S 1 +#define CV_16U 2 +#define CV_16S 3 +#define CV_32S 4 +#define CV_32F 5 +#define CV_64F 6 +#define CV_USRTYPE1 7 + +#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1) +#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK) + +#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT)) +#define CV_MAKE_TYPE CV_MAKETYPE + +#define CV_8UC1 CV_MAKETYPE(CV_8U,1) +#define CV_8UC2 CV_MAKETYPE(CV_8U,2) +#define CV_8UC3 CV_MAKETYPE(CV_8U,3) +#define CV_8UC4 CV_MAKETYPE(CV_8U,4) +#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n)) + +#define CV_8SC1 CV_MAKETYPE(CV_8S,1) +#define CV_8SC2 CV_MAKETYPE(CV_8S,2) +#define CV_8SC3 CV_MAKETYPE(CV_8S,3) +#define CV_8SC4 CV_MAKETYPE(CV_8S,4) +#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n)) + +#define CV_16UC1 CV_MAKETYPE(CV_16U,1) +#define CV_16UC2 CV_MAKETYPE(CV_16U,2) +#define CV_16UC3 CV_MAKETYPE(CV_16U,3) +#define CV_16UC4 CV_MAKETYPE(CV_16U,4) +#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n)) + +#define CV_16SC1 CV_MAKETYPE(CV_16S,1) +#define CV_16SC2 CV_MAKETYPE(CV_16S,2) +#define CV_16SC3 CV_MAKETYPE(CV_16S,3) +#define CV_16SC4 CV_MAKETYPE(CV_16S,4) +#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n)) + +#define CV_32SC1 CV_MAKETYPE(CV_32S,1) +#define CV_32SC2 CV_MAKETYPE(CV_32S,2) +#define CV_32SC3 CV_MAKETYPE(CV_32S,3) +#define CV_32SC4 CV_MAKETYPE(CV_32S,4) +#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n)) + +#define CV_32FC1 CV_MAKETYPE(CV_32F,1) +#define CV_32FC2 CV_MAKETYPE(CV_32F,2) +#define CV_32FC3 CV_MAKETYPE(CV_32F,3) +#define CV_32FC4 CV_MAKETYPE(CV_32F,4) +#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n)) + +#define CV_64FC1 CV_MAKETYPE(CV_64F,1) +#define CV_64FC2 CV_MAKETYPE(CV_64F,2) +#define CV_64FC3 CV_MAKETYPE(CV_64F,3) +#define CV_64FC4 CV_MAKETYPE(CV_64F,4) +#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n)) +//! @} + +//! @name Comparison operation +//! @sa cv::CmpTypes +//! @{ +#define CV_HAL_CMP_EQ 0 +#define CV_HAL_CMP_GT 1 +#define CV_HAL_CMP_GE 2 +#define CV_HAL_CMP_LT 3 +#define CV_HAL_CMP_LE 4 +#define CV_HAL_CMP_NE 5 +//! @} + +//! @name Border processing modes +//! @sa cv::BorderTypes +//! @{ +#define CV_HAL_BORDER_CONSTANT 0 +#define CV_HAL_BORDER_REPLICATE 1 +#define CV_HAL_BORDER_REFLECT 2 +#define CV_HAL_BORDER_WRAP 3 +#define CV_HAL_BORDER_REFLECT_101 4 +#define CV_HAL_BORDER_TRANSPARENT 5 +#define CV_HAL_BORDER_ISOLATED 16 +//! @} + +//! @name DFT flags +//! @{ +#define CV_HAL_DFT_INVERSE 1 +#define CV_HAL_DFT_SCALE 2 +#define CV_HAL_DFT_ROWS 4 +#define CV_HAL_DFT_COMPLEX_OUTPUT 16 +#define CV_HAL_DFT_REAL_OUTPUT 32 +#define CV_HAL_DFT_TWO_STAGE 64 +#define CV_HAL_DFT_STAGE_COLS 128 +#define CV_HAL_DFT_IS_CONTINUOUS 512 +#define CV_HAL_DFT_IS_INPLACE 1024 +//! @} + +//! @name SVD flags +//! @{ +#define CV_HAL_SVD_NO_UV 1 +#define CV_HAL_SVD_SHORT_UV 2 +#define CV_HAL_SVD_MODIFY_A 4 +#define CV_HAL_SVD_FULL_UV 8 +//! @} + +//! @name Gemm flags +//! @{ +#define CV_HAL_GEMM_1_T 1 +#define CV_HAL_GEMM_2_T 2 +#define CV_HAL_GEMM_3_T 4 +//! @} + +//! @} + +#endif diff --git a/thirdparty/linux/include/opencv2/core/hal/intrin.hpp b/thirdparty/linux/include/opencv2/core/hal/intrin.hpp new file mode 100644 index 0000000..34075e3 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/hal/intrin.hpp @@ -0,0 +1,414 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_HPP +#define OPENCV_HAL_INTRIN_HPP + +#include +#include +#include +#include "opencv2/core/cvdef.h" + +#define OPENCV_HAL_ADD(a, b) ((a) + (b)) +#define OPENCV_HAL_AND(a, b) ((a) & (b)) +#define OPENCV_HAL_NOP(a) (a) +#define OPENCV_HAL_1ST(a, b) (a) + +// unlike HAL API, which is in cv::hal, +// we put intrinsics into cv namespace to make its +// access from within opencv code more accessible +namespace cv { + +//! @addtogroup core_hal_intrin +//! @{ + +//! @cond IGNORED +template struct V_TypeTraits +{ + typedef _Tp int_type; + typedef _Tp uint_type; + typedef _Tp abs_type; + typedef _Tp sum_type; + + enum { delta = 0, shift = 0 }; + + static int_type reinterpret_int(_Tp x) { return x; } + static uint_type reinterpet_uint(_Tp x) { return x; } + static _Tp reinterpret_from_int(int_type x) { return (_Tp)x; } +}; + +template<> struct V_TypeTraits +{ + typedef uchar value_type; + typedef schar int_type; + typedef uchar uint_type; + typedef uchar abs_type; + typedef int sum_type; + + typedef ushort w_type; + typedef unsigned q_type; + + enum { delta = 128, shift = 8 }; + + static int_type reinterpret_int(value_type x) { return (int_type)x; } + static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } + static value_type reinterpret_from_int(int_type x) { return (value_type)x; } +}; + +template<> struct V_TypeTraits +{ + typedef schar value_type; + typedef schar int_type; + typedef uchar uint_type; + typedef uchar abs_type; + typedef int sum_type; + + typedef short w_type; + typedef int q_type; + + enum { delta = 128, shift = 8 }; + + static int_type reinterpret_int(value_type x) { return (int_type)x; } + static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } + static value_type reinterpret_from_int(int_type x) { return (value_type)x; } +}; + +template<> struct V_TypeTraits +{ + typedef ushort value_type; + typedef short int_type; + typedef ushort uint_type; + typedef ushort abs_type; + typedef int sum_type; + + typedef unsigned w_type; + typedef uchar nu_type; + + enum { delta = 32768, shift = 16 }; + + static int_type reinterpret_int(value_type x) { return (int_type)x; } + static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } + static value_type reinterpret_from_int(int_type x) { return (value_type)x; } +}; + +template<> struct V_TypeTraits +{ + typedef short value_type; + typedef short int_type; + typedef ushort uint_type; + typedef ushort abs_type; + typedef int sum_type; + + typedef int w_type; + typedef uchar nu_type; + typedef schar n_type; + + enum { delta = 128, shift = 8 }; + + static int_type reinterpret_int(value_type x) { return (int_type)x; } + static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } + static value_type reinterpret_from_int(int_type x) { return (value_type)x; } +}; + +template<> struct V_TypeTraits +{ + typedef unsigned value_type; + typedef int int_type; + typedef unsigned uint_type; + typedef unsigned abs_type; + typedef unsigned sum_type; + + typedef uint64 w_type; + typedef ushort nu_type; + + static int_type reinterpret_int(value_type x) { return (int_type)x; } + static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } + static value_type reinterpret_from_int(int_type x) { return (value_type)x; } +}; + +template<> struct V_TypeTraits +{ + typedef int value_type; + typedef int int_type; + typedef unsigned uint_type; + typedef unsigned abs_type; + typedef int sum_type; + + typedef int64 w_type; + typedef short n_type; + typedef ushort nu_type; + + static int_type reinterpret_int(value_type x) { return (int_type)x; } + static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } + static value_type reinterpret_from_int(int_type x) { return (value_type)x; } +}; + +template<> struct V_TypeTraits +{ + typedef uint64 value_type; + typedef int64 int_type; + typedef uint64 uint_type; + typedef uint64 abs_type; + typedef uint64 sum_type; + + typedef unsigned nu_type; + + static int_type reinterpret_int(value_type x) { return (int_type)x; } + static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } + static value_type reinterpret_from_int(int_type x) { return (value_type)x; } +}; + +template<> struct V_TypeTraits +{ + typedef int64 value_type; + typedef int64 int_type; + typedef uint64 uint_type; + typedef uint64 abs_type; + typedef int64 sum_type; + + typedef int nu_type; + + static int_type reinterpret_int(value_type x) { return (int_type)x; } + static uint_type reinterpret_uint(value_type x) { return (uint_type)x; } + static value_type reinterpret_from_int(int_type x) { return (value_type)x; } +}; + + +template<> struct V_TypeTraits +{ + typedef float value_type; + typedef int int_type; + typedef unsigned uint_type; + typedef float abs_type; + typedef float sum_type; + + typedef double w_type; + + static int_type reinterpret_int(value_type x) + { + Cv32suf u; + u.f = x; + return u.i; + } + static uint_type reinterpet_uint(value_type x) + { + Cv32suf u; + u.f = x; + return u.u; + } + static value_type reinterpret_from_int(int_type x) + { + Cv32suf u; + u.i = x; + return u.f; + } +}; + +template<> struct V_TypeTraits +{ + typedef double value_type; + typedef int64 int_type; + typedef uint64 uint_type; + typedef double abs_type; + typedef double sum_type; + static int_type reinterpret_int(value_type x) + { + Cv64suf u; + u.f = x; + return u.i; + } + static uint_type reinterpet_uint(value_type x) + { + Cv64suf u; + u.f = x; + return u.u; + } + static value_type reinterpret_from_int(int_type x) + { + Cv64suf u; + u.i = x; + return u.f; + } +}; + +template struct V_SIMD128Traits +{ + enum { nlanes = 16 / sizeof(T) }; +}; + +//! @endcond + +//! @} + +} + +#ifdef CV_DOXYGEN +# undef CV_SSE2 +# undef CV_NEON +#endif + +#if CV_SSE2 + +#include "opencv2/core/hal/intrin_sse.hpp" + +#elif CV_NEON + +#include "opencv2/core/hal/intrin_neon.hpp" + +#else + +#include "opencv2/core/hal/intrin_cpp.hpp" + +#endif + +//! @addtogroup core_hal_intrin +//! @{ + +#ifndef CV_SIMD128 +//! Set to 1 if current compiler supports vector extensions (NEON or SSE is enabled) +#define CV_SIMD128 0 +#endif + +#ifndef CV_SIMD128_64F +//! Set to 1 if current intrinsics implementation supports 64-bit float vectors +#define CV_SIMD128_64F 0 +#endif + +//! @} + +//================================================================================================== + +//! @cond IGNORED + +namespace cv { + +template struct V_RegTrait128; + +template <> struct V_RegTrait128 { + typedef v_uint8x16 reg; + typedef v_uint16x8 w_reg; + typedef v_uint32x4 q_reg; + typedef v_uint8x16 u_reg; + static v_uint8x16 zero() { return v_setzero_u8(); } + static v_uint8x16 all(uchar val) { return v_setall_u8(val); } +}; + +template <> struct V_RegTrait128 { + typedef v_int8x16 reg; + typedef v_int16x8 w_reg; + typedef v_int32x4 q_reg; + typedef v_uint8x16 u_reg; + static v_int8x16 zero() { return v_setzero_s8(); } + static v_int8x16 all(schar val) { return v_setall_s8(val); } +}; + +template <> struct V_RegTrait128 { + typedef v_uint16x8 reg; + typedef v_uint32x4 w_reg; + typedef v_int16x8 int_reg; + typedef v_uint16x8 u_reg; + static v_uint16x8 zero() { return v_setzero_u16(); } + static v_uint16x8 all(ushort val) { return v_setall_u16(val); } +}; + +template <> struct V_RegTrait128 { + typedef v_int16x8 reg; + typedef v_int32x4 w_reg; + typedef v_uint16x8 u_reg; + static v_int16x8 zero() { return v_setzero_s16(); } + static v_int16x8 all(short val) { return v_setall_s16(val); } +}; + +template <> struct V_RegTrait128 { + typedef v_uint32x4 reg; + typedef v_uint64x2 w_reg; + typedef v_int32x4 int_reg; + typedef v_uint32x4 u_reg; + static v_uint32x4 zero() { return v_setzero_u32(); } + static v_uint32x4 all(unsigned val) { return v_setall_u32(val); } +}; + +template <> struct V_RegTrait128 { + typedef v_int32x4 reg; + typedef v_int64x2 w_reg; + typedef v_uint32x4 u_reg; + static v_int32x4 zero() { return v_setzero_s32(); } + static v_int32x4 all(int val) { return v_setall_s32(val); } +}; + +template <> struct V_RegTrait128 { + typedef v_uint64x2 reg; + static v_uint64x2 zero() { return v_setzero_u64(); } + static v_uint64x2 all(uint64 val) { return v_setall_u64(val); } +}; + +template <> struct V_RegTrait128 { + typedef v_int64x2 reg; + static v_int64x2 zero() { return v_setzero_s64(); } + static v_int64x2 all(int64 val) { return v_setall_s64(val); } +}; + +template <> struct V_RegTrait128 { + typedef v_float32x4 reg; + typedef v_int32x4 int_reg; + typedef v_float32x4 u_reg; + static v_float32x4 zero() { return v_setzero_f32(); } + static v_float32x4 all(float val) { return v_setall_f32(val); } +}; + +#if CV_SIMD128_64F +template <> struct V_RegTrait128 { + typedef v_float64x2 reg; + typedef v_int32x4 int_reg; + typedef v_float64x2 u_reg; + static v_float64x2 zero() { return v_setzero_f64(); } + static v_float64x2 all(double val) { return v_setall_f64(val); } +}; +#endif + +} // cv:: + +//! @endcond + +#endif diff --git a/thirdparty/linux/include/opencv2/core/hal/intrin_cpp.hpp b/thirdparty/linux/include/opencv2/core/hal/intrin_cpp.hpp new file mode 100644 index 0000000..93ca397 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/hal/intrin_cpp.hpp @@ -0,0 +1,1790 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_CPP_HPP +#define OPENCV_HAL_INTRIN_CPP_HPP + +#include +#include +#include +#include "opencv2/core/saturate.hpp" + +namespace cv +{ + +/** @addtogroup core_hal_intrin + +"Universal intrinsics" is a types and functions set intended to simplify vectorization of code on +different platforms. Currently there are two supported SIMD extensions: __SSE/SSE2__ on x86 +architectures and __NEON__ on ARM architectures, both allow working with 128 bit registers +containing packed values of different types. In case when there is no SIMD extension available +during compilation, fallback C++ implementation of intrinsics will be chosen and code will work as +expected although it could be slower. + +### Types + +There are several types representing 128-bit register as a vector of packed values, each type is +implemented as a structure based on a one SIMD register. + +- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char +- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short +- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int +- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64 +- cv::v_float32x4: four 32-bit floating point values (signed) - float +- cv::v_float64x2: two 64-bit floating point valies (signed) - double + +@note +cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to +check the CV_SIMD128_64F preprocessor definition: +@code +#if CV_SIMD128_64F +//... +#endif +@endcode + +### Load and store operations + +These operations allow to set contents of the register explicitly or by loading it from some memory +block and to save contents of the register to memory block. + +- Constructors: +@ref v_reg::v_reg(const _Tp *ptr) "from memory", +@ref v_reg::v_reg(_Tp s0, _Tp s1) "from two values", ... +- Other create methods: +@ref v_setall_s8, @ref v_setall_u8, ..., +@ref v_setzero_u8, @ref v_setzero_s8, ... +- Memory operations: +@ref v_load, @ref v_load_aligned, @ref v_load_halves, +@ref v_store, @ref v_store_aligned, +@ref v_store_high, @ref v_store_low + +### Value reordering + +These operations allow to reorder or recombine elements in one or multiple vectors. + +- Interleave, deinterleave (2, 3 and 4 channels): @ref v_load_deinterleave, @ref v_store_interleave +- Expand: @ref v_load_expand, @ref v_load_expand_q, @ref v_expand +- Pack: @ref v_pack, @ref v_pack_u, @ref v_rshr_pack, @ref v_rshr_pack_u, +@ref v_pack_store, @ref v_pack_u_store, @ref v_rshr_pack_store, @ref v_rshr_pack_u_store +- Recombine: @ref v_zip, @ref v_recombine, @ref v_combine_low, @ref v_combine_high +- Extract: @ref v_extract + + +### Arithmetic, bitwise and comparison operations + +Element-wise binary and unary operations. + +- Arithmetics: +@ref operator +(const v_reg &a, const v_reg &b) "+", +@ref operator -(const v_reg &a, const v_reg &b) "-", +@ref operator *(const v_reg &a, const v_reg &b) "*", +@ref operator /(const v_reg &a, const v_reg &b) "/", +@ref v_mul_expand + +- Non-saturating arithmetics: @ref v_add_wrap, @ref v_sub_wrap + +- Bitwise shifts: +@ref operator <<(const v_reg &a, int s) "<<", +@ref operator >>(const v_reg &a, int s) ">>", +@ref v_shl, @ref v_shr + +- Bitwise logic: +@ref operator&(const v_reg &a, const v_reg &b) "&", +@ref operator |(const v_reg &a, const v_reg &b) "|", +@ref operator ^(const v_reg &a, const v_reg &b) "^", +@ref operator ~(const v_reg &a) "~" + +- Comparison: +@ref operator >(const v_reg &a, const v_reg &b) ">", +@ref operator >=(const v_reg &a, const v_reg &b) ">=", +@ref operator <(const v_reg &a, const v_reg &b) "<", +@ref operator <=(const v_reg &a, const v_reg &b) "<=", +@ref operator==(const v_reg &a, const v_reg &b) "==", +@ref operator !=(const v_reg &a, const v_reg &b) "!=" + +- min/max: @ref v_min, @ref v_max + +### Reduce and mask + +Most of these operations return only one value. + +- Reduce: @ref v_reduce_min, @ref v_reduce_max, @ref v_reduce_sum +- Mask: @ref v_signmask, @ref v_check_all, @ref v_check_any, @ref v_select + +### Other math + +- Some frequent operations: @ref v_sqrt, @ref v_invsqrt, @ref v_magnitude, @ref v_sqr_magnitude +- Absolute values: @ref v_abs, @ref v_absdiff + +### Conversions + +Different type conversions and casts: + +- Rounding: @ref v_round, @ref v_floor, @ref v_ceil, @ref v_trunc, +- To float: @ref v_cvt_f32, @ref v_cvt_f64 +- Reinterpret: @ref v_reinterpret_as_u8, @ref v_reinterpret_as_s8, ... + +### Matrix operations + +In these operations vectors represent matrix rows/columns: @ref v_dotprod, @ref v_matmul, @ref v_transpose4x4 + +### Usability + +Most operations are implemented only for some subset of the available types, following matrices +shows the applicability of different operations to the types. + +Regular integers: + +| Operations\\Types | uint 8x16 | int 8x16 | uint 16x8 | int 16x8 | uint 32x4 | int 32x4 | +|-------------------|:-:|:-:|:-:|:-:|:-:|:-:| +|load, store | x | x | x | x | x | x | +|interleave | x | x | x | x | x | x | +|expand | x | x | x | x | x | x | +|expand_q | x | x | | | | | +|add, sub | x | x | x | x | x | x | +|add_wrap, sub_wrap | x | x | x | x | | | +|mul | | | x | x | x | x | +|mul_expand | | | x | x | x | | +|compare | x | x | x | x | x | x | +|shift | | | x | x | x | x | +|dotprod | | | | x | | | +|logical | x | x | x | x | x | x | +|min, max | x | x | x | x | x | x | +|absdiff | x | x | x | x | x | x | +|reduce | | | | | x | x | +|mask | x | x | x | x | x | x | +|pack | x | x | x | x | x | x | +|pack_u | x | | x | | | | +|unpack | x | x | x | x | x | x | +|extract | x | x | x | x | x | x | +|cvt_flt32 | | | | | | x | +|cvt_flt64 | | | | | | x | +|transpose4x4 | | | | | x | x | + +Big integers: + +| Operations\\Types | uint 64x2 | int 64x2 | +|-------------------|:-:|:-:| +|load, store | x | x | +|add, sub | x | x | +|shift | x | x | +|logical | x | x | +|extract | x | x | + +Floating point: + +| Operations\\Types | float 32x4 | float 64x2 | +|-------------------|:-:|:-:| +|load, store | x | x | +|interleave | x | | +|add, sub | x | x | +|mul | x | x | +|div | x | x | +|compare | x | x | +|min, max | x | x | +|absdiff | x | x | +|reduce | x | | +|mask | x | x | +|unpack | x | x | +|cvt_flt32 | | x | +|cvt_flt64 | x | | +|sqrt, abs | x | x | +|float math | x | x | +|transpose4x4 | x | | + + + @{ */ + +template struct v_reg +{ +//! @cond IGNORED + typedef _Tp lane_type; + typedef v_reg::int_type, n> int_vec; + typedef v_reg::abs_type, n> abs_vec; + enum { nlanes = n }; +// !@endcond + + /** @brief Constructor + + Initializes register with data from memory + @param ptr pointer to memory block with data for register */ + explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; } + + /** @brief Constructor + + Initializes register with two 64-bit values */ + v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; } + + /** @brief Constructor + + Initializes register with four 32-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; } + + /** @brief Constructor + + Initializes register with eight 16-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, + _Tp s4, _Tp s5, _Tp s6, _Tp s7) + { + s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; + s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; + } + + /** @brief Constructor + + Initializes register with sixteen 8-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, + _Tp s4, _Tp s5, _Tp s6, _Tp s7, + _Tp s8, _Tp s9, _Tp s10, _Tp s11, + _Tp s12, _Tp s13, _Tp s14, _Tp s15) + { + s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; + s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; + s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11; + s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15; + } + + /** @brief Default constructor + + Does not initialize anything*/ + v_reg() {} + + /** @brief Copy constructor */ + v_reg(const v_reg<_Tp, n> & r) + { + for( int i = 0; i < n; i++ ) + s[i] = r.s[i]; + } + /** @brief Access first value + + Returns value of the first lane according to register type, for example: + @code{.cpp} + v_int32x4 r(1, 2, 3, 4); + int v = r.get0(); // returns 1 + v_uint64x2 r(1, 2); + uint64_t v = r.get0(); // returns 1 + @endcode + */ + _Tp get0() const { return s[0]; } + +//! @cond IGNORED + _Tp get(const int i) const { return s[i]; } + v_reg<_Tp, n> high() const + { + v_reg<_Tp, n> c; + int i; + for( i = 0; i < n/2; i++ ) + { + c.s[i] = s[i+(n/2)]; + c.s[i+(n/2)] = 0; + } + return c; + } + + static v_reg<_Tp, n> zero() + { + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = (_Tp)0; + return c; + } + + static v_reg<_Tp, n> all(_Tp s) + { + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = s; + return c; + } + + template v_reg<_Tp2, n2> reinterpret_as() const + { + size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n); + v_reg<_Tp2, n2> c; + std::memcpy(&c.s[0], &s[0], bytes); + return c; + } + + _Tp s[n]; +//! @endcond +}; + +/** @brief Sixteen 8-bit unsigned integer values */ +typedef v_reg v_uint8x16; +/** @brief Sixteen 8-bit signed integer values */ +typedef v_reg v_int8x16; +/** @brief Eight 16-bit unsigned integer values */ +typedef v_reg v_uint16x8; +/** @brief Eight 16-bit signed integer values */ +typedef v_reg v_int16x8; +/** @brief Four 32-bit unsigned integer values */ +typedef v_reg v_uint32x4; +/** @brief Four 32-bit signed integer values */ +typedef v_reg v_int32x4; +/** @brief Four 32-bit floating point values (single precision) */ +typedef v_reg v_float32x4; +/** @brief Two 64-bit floating point values (double precision) */ +typedef v_reg v_float64x2; +/** @brief Two 64-bit unsigned integer values */ +typedef v_reg v_uint64x2; +/** @brief Two 64-bit signed integer values */ +typedef v_reg v_int64x2; + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_BIN_OP(bin_op) \ +template inline v_reg<_Tp, n> \ + operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ + return c; \ +} \ +template inline v_reg<_Tp, n>& \ + operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + for( int i = 0; i < n; i++ ) \ + a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ + return a; \ +} + +/** @brief Add values + +For all types. */ +OPENCV_HAL_IMPL_BIN_OP(+) + +/** @brief Subtract values + +For all types. */ +OPENCV_HAL_IMPL_BIN_OP(-) + +/** @brief Multiply values + +For 16- and 32-bit integer types and floating types. */ +OPENCV_HAL_IMPL_BIN_OP(*) + +/** @brief Divide values + +For floating types only. */ +OPENCV_HAL_IMPL_BIN_OP(/) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_BIT_OP(bit_op) \ +template inline v_reg<_Tp, n> operator bit_op \ + (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ + V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ + return c; \ +} \ +template inline v_reg<_Tp, n>& operator \ + bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + for( int i = 0; i < n; i++ ) \ + a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ + V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ + return a; \ +} + +/** @brief Bitwise AND + +Only for integer types. */ +OPENCV_HAL_IMPL_BIT_OP(&) + +/** @brief Bitwise OR + +Only for integer types. */ +OPENCV_HAL_IMPL_BIT_OP(|) + +/** @brief Bitwise XOR + +Only for integer types.*/ +OPENCV_HAL_IMPL_BIT_OP(^) + +/** @brief Bitwise NOT + +Only for integer types.*/ +template inline v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i])); + } + return c; +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \ +template inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \ +{ \ + v_reg<_Tp2, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cfunc(a.s[i]); \ + return c; \ +} + +/** @brief Square root of elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp) + +//! @cond IGNORED +OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp) +//! @endcond + +/** @brief Absolute value of elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs, + typename V_TypeTraits<_Tp>::abs_type) + +/** @brief Round elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_round, cvRound, int) + +/** @brief Floor elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_floor, cvFloor, int) + +/** @brief Ceil elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_ceil, cvCeil, int) + +/** @brief Truncate elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_trunc, int, int) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \ +template inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cfunc(a.s[i], b.s[i]); \ + return c; \ +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \ +template inline _Tp func(const v_reg<_Tp, n>& a) \ +{ \ + _Tp c = a.s[0]; \ + for( int i = 1; i < n; i++ ) \ + c = cfunc(c, a.s[i]); \ + return c; \ +} + +/** @brief Choose min values for each pair + +Scheme: +@code +{A1 A2 ...} +{B1 B2 ...} +-------------- +{min(A1,B1) min(A2,B2) ...} +@endcode +For all types except 64-bit integer. */ +OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min) + +/** @brief Choose max values for each pair + +Scheme: +@code +{A1 A2 ...} +{B1 B2 ...} +-------------- +{max(A1,B1) max(A2,B2) ...} +@endcode +For all types except 64-bit integer. */ +OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max) + +/** @brief Find one min value + +Scheme: +@code +{A1 A2 A3 ...} => min(A1,A2,A3,...) +@endcode +For 32-bit integer and 32-bit floating point types. */ +OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min) + +/** @brief Find one max value + +Scheme: +@code +{A1 A2 A3 ...} => max(A1,A2,A3,...) +@endcode +For 32-bit integer and 32-bit floating point types. */ +OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max) + +//! @cond IGNORED +template +inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval ) +{ + for( int i = 0; i < n; i++ ) + { + minval.s[i] = std::min(a.s[i], b.s[i]); + maxval.s[i] = std::max(a.s[i], b.s[i]); + } +} +//! @endcond + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \ +template \ +inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \ + return c; \ +} + +/** @brief Less-than comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(<) + +/** @brief Greater-than comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(>) + +/** @brief Less-than or equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(<=) + +/** @brief Greater-than or equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(>=) + +/** @brief Equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(==) + +/** @brief Not equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(!=) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_ADD_SUB_OP(func, bin_op, cast_op, _Tp2) \ +template \ +inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef _Tp2 rtype; \ + v_reg c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \ + return c; \ +} + +/** @brief Add values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ADD_SUB_OP(v_add_wrap, +, (_Tp), _Tp) + +/** @brief Subtract values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ADD_SUB_OP(v_sub_wrap, -, (_Tp), _Tp) + +//! @cond IGNORED +template inline T _absdiff(T a, T b) +{ + return a > b ? a - b : b - a; +} +//! @endcond + +/** @brief Absolute difference + +Returns \f$ |a - b| \f$ converted to corresponding unsigned type. +Example: +@code{.cpp} +v_int32x4 a, b; // {1, 2, 3, 4} and {4, 3, 2, 1} +v_uint32x4 c = v_absdiff(a, b); // result is {3, 1, 1, 3} +@endcode +For 8-, 16-, 32-bit integer source types. */ +template +inline v_reg::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b) +{ + typedef typename V_TypeTraits<_Tp>::abs_type rtype; + v_reg c; + const rtype mask = std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0; + for( int i = 0; i < n; i++ ) + { + rtype ua = a.s[i] ^ mask; + rtype ub = b.s[i] ^ mask; + c.s[i] = _absdiff(ua, ub); + } + return c; +} + +/** @overload + +For 32-bit floating point values */ +inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) +{ + v_float32x4 c; + for( int i = 0; i < c.nlanes; i++ ) + c.s[i] = _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @overload + +For 64-bit floating point values */ +inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) +{ + v_float64x2 c; + for( int i = 0; i < c.nlanes; i++ ) + c.s[i] = _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @brief Inversed square root + +Returns \f$ 1/sqrt(a) \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = 1.f/std::sqrt(a.s[i]); + return c; +} + +/** @brief Magnitude + +Returns \f$ sqrt(a^2 + b^2) \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]); + return c; +} + +/** @brief Square of the magnitude + +Returns \f$ a^2 + b^2 \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i]; + return c; +} + +/** @brief Multiply and add + +Returns \f$ a*b + c \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + const v_reg<_Tp, n>& c) +{ + v_reg<_Tp, n> d; + for( int i = 0; i < n; i++ ) + d.s[i] = a.s[i]*b.s[i] + c.s[i]; + return d; +} + +/** @brief Dot product of elements + +Multiply values in two registers and sum adjacent result pairs. +Scheme: +@code + {A1 A2 ...} // 16-bit +x {B1 B2 ...} // 16-bit +------------- +{A1B1+A2B2 ...} // 32-bit +@endcode +Implemented only for 16-bit signed source type (v_int16x8). +*/ +template inline v_reg::w_type, n/2> + v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg c; + for( int i = 0; i < (n/2); i++ ) + c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1]; + return c; +} + +/** @brief Multiply and expand + +Multiply values two registers and store results in two registers with wider pack type. +Scheme: +@code + {A B C D} // 32-bit +x {E F G H} // 32-bit +--------------- +{AE BF} // 64-bit + {CG DH} // 64-bit +@endcode +Example: +@code{.cpp} +v_uint32x4 a, b; // {1,2,3,4} and {2,2,2,2} +v_uint64x2 c, d; // results +v_mul_expand(a, b, c, d); // c, d = {2,4}, {6, 8} +@endcode +Implemented only for 16- and unsigned 32-bit source types (v_int16x8, v_uint16x8, v_uint32x4). +*/ +template inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg::w_type, n/2>& c, + v_reg::w_type, n/2>& d) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = (w_type)a.s[i]*b.s[i]; + d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)]; + } +} + +//! @cond IGNORED +template inline void v_hsum(const v_reg<_Tp, n>& a, + v_reg::w_type, n/2>& c) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1]; + } +} +//! @endcond + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \ +template inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = (_Tp)(a.s[i] shift_op imm); \ + return c; \ +} + +/** @brief Bitwise shift left + +For 16-, 32- and 64-bit integer values. */ +OPENCV_HAL_IMPL_SHIFT_OP(<<) + +/** @brief Bitwise shift right + +For 16-, 32- and 64-bit integer values. */ +OPENCV_HAL_IMPL_SHIFT_OP(>>) + +/** @brief Sum packed values + +Scheme: +@code +{A1 A2 A3 ...} => sum{A1,A2,A3,...} +@endcode +For 32-bit integer and 32-bit floating point types.*/ +template inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a) +{ + typename V_TypeTraits<_Tp>::sum_type c = a.s[0]; + for( int i = 1; i < n; i++ ) + c += a.s[i]; + return c; +} + +/** @brief Get negative values mask + +Returned value is a bit mask with bits set to 1 on places corresponding to negative packed values indexes. +Example: +@code{.cpp} +v_int32x4 r; // set to {-1, -1, 1, 1} +int mask = v_signmask(r); // mask = 3 <== 00000000 00000000 00000000 00000011 +@endcode +For all types except 64-bit. */ +template inline int v_signmask(const v_reg<_Tp, n>& a) +{ + int mask = 0; + for( int i = 0; i < n; i++ ) + mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i; + return mask; +} + +/** @brief Check if all packed values are less than zero + +Unsigned values will be casted to signed: `uchar 254 => char -2`. +For all types except 64-bit. */ +template inline bool v_check_all(const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 ) + return false; + return true; +} + +/** @brief Check if any of packed values is less than zero + +Unsigned values will be casted to signed: `uchar 254 => char -2`. +For all types except 64-bit. */ +template inline bool v_check_any(const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 ) + return true; + return false; +} + +/** @brief Bitwise select + +Return value will be built by combining values a and b using the following scheme: +If the i-th bit in _mask_ is 1 + select i-th bit from _a_ +else + select i-th bit from _b_ */ +template inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask, + const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef V_TypeTraits<_Tp> Traits; + typedef typename Traits::int_type int_type; + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + { + int_type m = Traits::reinterpret_int(mask.s[i]); + c.s[i] = Traits::reinterpret_from_int((Traits::reinterpret_int(a.s[i]) & m) + | (Traits::reinterpret_int(b.s[i]) & ~m)); + } + return c; +} + +/** @brief Expand values to the wider pack type + +Copy contents of register to two registers with 2x wider pack type. +Scheme: +@code + int32x4 int64x2 int64x2 +{A B C D} ==> {A B} , {C D} +@endcode */ +template inline void v_expand(const v_reg<_Tp, n>& a, + v_reg::w_type, n/2>& b0, + v_reg::w_type, n/2>& b1) +{ + for( int i = 0; i < (n/2); i++ ) + { + b0.s[i] = a.s[i]; + b1.s[i] = a.s[i+(n/2)]; + } +} + +//! @cond IGNORED +template inline v_reg::int_type, n> + v_reinterpret_as_int(const v_reg<_Tp, n>& a) +{ + v_reg::int_type, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]); + return c; +} + +template inline v_reg::uint_type, n> + v_reinterpret_as_uint(const v_reg<_Tp, n>& a) +{ + v_reg::uint_type, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]); + return c; +} +//! @endcond + +/** @brief Interleave two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A1 B1 A2 B2} and {A3 B3 A4 B4} +@endcode +For all types except 64-bit. +*/ +template inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1, + v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 ) +{ + int i; + for( i = 0; i < n/2; i++ ) + { + b0.s[i*2] = a0.s[i]; + b0.s[i*2+1] = a1.s[i]; + } + for( ; i < n; i++ ) + { + b1.s[i*2-n] = a0.s[i]; + b1.s[i*2-n+1] = a1.s[i]; + } +} + +/** @brief Load register contents from memory + +@param ptr pointer to memory block with data +@return register object + +@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x16, int ==> cv::v_int32x4, etc. + */ +template +inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load(const _Tp* ptr) +{ + return v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes>(ptr); +} + +/** @brief Load register contents from memory (aligned) + +similar to cv::v_load, but source memory block should be aligned (to 16-byte boundary) + */ +template +inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load_aligned(const _Tp* ptr) +{ + return v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes>(ptr); +} + +/** @brief Load register contents from two memory blocks + +@param loptr memory block containing data for first half (0..n/2) +@param hiptr memory block containing data for second half (n/2..n) + +@code{.cpp} +int lo[2] = { 1, 2 }, hi[2] = { 3, 4 }; +v_int32x4 r = v_load_halves(lo, hi); +@endcode + */ +template +inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load_halves(const _Tp* loptr, const _Tp* hiptr) +{ + v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> c; + for( int i = 0; i < c.nlanes/2; i++ ) + { + c.s[i] = loptr[i]; + c.s[i+c.nlanes/2] = hiptr[i]; + } + return c; +} + +/** @brief Load register contents from memory with double expand + +Same as cv::v_load, but result pack type will be 2x wider than memory type. + +@code{.cpp} +short buf[4] = {1, 2, 3, 4}; // type is int16 +v_int32x4 r = v_load_expand(buf); // r = {1, 2, 3, 4} - type is int32 +@endcode +For 8-, 16-, 32-bit integer source types. */ +template +inline v_reg::w_type, V_SIMD128Traits<_Tp>::nlanes / 2> +v_load_expand(const _Tp* ptr) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg::nlanes> c; + for( int i = 0; i < c.nlanes; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load register contents from memory with quad expand + +Same as cv::v_load_expand, but result type is 4 times wider than source. +@code{.cpp} +char buf[4] = {1, 2, 3, 4}; // type is int8 +v_int32x4 r = v_load_q(buf); // r = {1, 2, 3, 4} - type is int32 +@endcode +For 8-bit integer source types. */ +template +inline v_reg::q_type, V_SIMD128Traits<_Tp>::nlanes / 4> +v_load_expand_q(const _Tp* ptr) +{ + typedef typename V_TypeTraits<_Tp>::q_type q_type; + v_reg::nlanes> c; + for( int i = 0; i < c.nlanes; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load and deinterleave (2 channels) + +Load data from memory deinterleave and store to 2 registers. +Scheme: +@code +{A1 B1 A2 B2 ...} ==> {A1 A2 ...}, {B1 B2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b) +{ + int i, i2; + for( i = i2 = 0; i < n; i++, i2 += 2 ) + { + a.s[i] = ptr[i2]; + b.s[i] = ptr[i2+1]; + } +} + +/** @brief Load and deinterleave (3 channels) + +Load data from memory deinterleave and store to 3 registers. +Scheme: +@code +{A1 B1 C1 A2 B2 C2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b, v_reg<_Tp, n>& c) +{ + int i, i3; + for( i = i3 = 0; i < n; i++, i3 += 3 ) + { + a.s[i] = ptr[i3]; + b.s[i] = ptr[i3+1]; + c.s[i] = ptr[i3+2]; + } +} + +/** @brief Load and deinterleave (4 channels) + +Load data from memory deinterleave and store to 4 registers. +Scheme: +@code +{A1 B1 C1 D1 A2 B2 C2 D2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b, v_reg<_Tp, n>& c, + v_reg<_Tp, n>& d) +{ + int i, i4; + for( i = i4 = 0; i < n; i++, i4 += 4 ) + { + a.s[i] = ptr[i4]; + b.s[i] = ptr[i4+1]; + c.s[i] = ptr[i4+2]; + d.s[i] = ptr[i4+3]; + } +} + +/** @brief Interleave and store (2 channels) + +Interleave and store data from 2 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...} ==> {A1 B1 A2 B2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b) +{ + int i, i2; + for( i = i2 = 0; i < n; i++, i2 += 2 ) + { + ptr[i2] = a.s[i]; + ptr[i2+1] = b.s[i]; + } +} + +/** @brief Interleave and store (3 channels) + +Interleave and store data from 3 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} ==> {A1 B1 C1 A2 B2 C2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c) +{ + int i, i3; + for( i = i3 = 0; i < n; i++, i3 += 3 ) + { + ptr[i3] = a.s[i]; + ptr[i3+1] = b.s[i]; + ptr[i3+2] = c.s[i]; + } +} + +/** @brief Interleave and store (4 channels) + +Interleave and store data from 4 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, + const v_reg<_Tp, n>& d) +{ + int i, i4; + for( i = i4 = 0; i < n; i++, i4 += 4 ) + { + ptr[i4] = a.s[i]; + ptr[i4+1] = b.s[i]; + ptr[i4+2] = c.s[i]; + ptr[i4+3] = d.s[i]; + } +} + +/** @brief Store data to memory + +Store register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B C D} +@endcode +Pointer can be unaligned. */ +template +inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Store data to memory (lower half) + +Store lower half of register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B} +@endcode */ +template +inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < (n/2); i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Store data to memory (higher half) + +Store higher half of register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {C D} +@endcode */ +template +inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < (n/2); i++ ) + ptr[i] = a.s[i+(n/2)]; +} + +/** @brief Store data to memory (aligned) + +Store register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B C D} +@endcode +Pointer __should__ be aligned by 16-byte boundary. */ +template +inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Combine vector from first elements of two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A1 A2 B1 B2} +@endcode +For all types except 64-bit. */ +template +inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = a.s[i]; + c.s[i+(n/2)] = b.s[i]; + } + return c; +} + +/** @brief Combine vector from last elements of two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A3 A4 B3 B4} +@endcode +For all types except 64-bit. */ +template +inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = a.s[i+(n/2)]; + c.s[i+(n/2)] = b.s[i+(n/2)]; + } + return c; +} + +/** @brief Combine two vectors from lower and higher parts of two other vectors + +@code{.cpp} +low = cv::v_combine_low(a, b); +high = cv::v_combine_high(a, b); +@endcode */ +template +inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg<_Tp, n>& low, v_reg<_Tp, n>& high) +{ + for( int i = 0; i < (n/2); i++ ) + { + low.s[i] = a.s[i]; + low.s[i+(n/2)] = b.s[i]; + high.s[i] = a.s[i+(n/2)]; + high.s[i+(n/2)] = b.s[i+(n/2)]; + } +} + +/** @brief Vector extract + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +======================== +shift = 1 {A2 A3 A4 B1} +shift = 2 {A3 A4 B1 B2} +shift = 3 {A4 B1 B2 B3} +@endcode +Restriction: 0 <= shift < nlanes + +Usage: +@code +v_int32x4 a, b, c; +c = v_extract<2>(a, b); +@endcode +For integer types only. */ +template +inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> r; + const int shift = n - s; + int i = 0; + for (; i < shift; ++i) + r.s[i] = a.s[i+s]; + for (; i < n; ++i) + r.s[i] = b.s[i-shift]; + return r; +} + +/** @brief Round + +Rounds each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_round(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvRound(a.s[i]); + return c; +} + +/** @brief Floor + +Floor each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_floor(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvFloor(a.s[i]); + return c; +} + +/** @brief Ceil + +Ceil each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_ceil(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvCeil(a.s[i]); + return c; +} + +/** @brief Trunc + +Truncate each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_trunc(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (int)(a.s[i]); + return c; +} + +/** @overload */ +template inline v_reg v_round(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvRound(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_floor(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvFloor(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_ceil(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvCeil(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_trunc(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvCeil(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @brief Convert to float + +Supported input type is cv::v_int32x4. */ +template inline v_reg v_cvt_f32(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (float)a.s[i]; + return c; +} + +/** @brief Convert to double + +Supported input type is cv::v_int32x4. */ +template inline v_reg v_cvt_f64(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (double)a.s[i]; + return c; +} + +/** @brief Convert to double + +Supported input type is cv::v_float32x4. */ +template inline v_reg v_cvt_f64(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (double)a.s[i]; + return c; +} + +/** @brief Transpose 4x4 matrix + +Scheme: +@code +a0 {A1 A2 A3 A4} +a1 {B1 B2 B3 B4} +a2 {C1 C2 C3 C4} +a3 {D1 D2 D3 D4} +=============== +b0 {A1 B1 C1 D1} +b1 {A2 B2 C2 D2} +b2 {A3 B3 C3 D3} +b3 {A4 B4 C4 D4} +@endcode +*/ +template +inline void v_transpose4x4( v_reg<_Tp, 4>& a0, const v_reg<_Tp, 4>& a1, + const v_reg<_Tp, 4>& a2, const v_reg<_Tp, 4>& a3, + v_reg<_Tp, 4>& b0, v_reg<_Tp, 4>& b1, + v_reg<_Tp, 4>& b2, v_reg<_Tp, 4>& b3 ) +{ + b0 = v_reg<_Tp, 4>(a0.s[0], a1.s[0], a2.s[0], a3.s[0]); + b1 = v_reg<_Tp, 4>(a0.s[1], a1.s[1], a2.s[1], a3.s[1]); + b2 = v_reg<_Tp, 4>(a0.s[2], a1.s[2], a2.s[2], a3.s[2]); + b3 = v_reg<_Tp, 4>(a0.s[3], a1.s[3], a2.s[3], a3.s[3]); +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec::zero(); } + +//! @name Init with zero +//! @{ +//! @brief Create new vector with zero elements +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_setall_##suffix(_Tp val) { return _Tpvec::all(val); } + +//! @name Init with value +//! @{ +//! @brief Create new vector with elements set to a specific value +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tpvec, _Tp, suffix) \ +template inline _Tpvec \ + v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \ +{ return a.template reinterpret_as<_Tp, _Tpvec::nlanes>(); } + +//! @name Reinterpret +//! @{ +//! @brief Convert vector to different type without modifying underlying data. +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_SHIFTL(_Tpvec, _Tp) \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return a << n; } + +//! @name Left shift +//! @{ +//! @brief Shift left +OPENCV_HAL_IMPL_C_SHIFTL(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_SHIFTL(v_int16x8, short) +OPENCV_HAL_IMPL_C_SHIFTL(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_SHIFTL(v_int32x4, int) +OPENCV_HAL_IMPL_C_SHIFTL(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_SHIFTL(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_SHIFTR(_Tpvec, _Tp) \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return a >> n; } + +//! @name Right shift +//! @{ +//! @brief Shift right +OPENCV_HAL_IMPL_C_SHIFTR(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_SHIFTR(v_int16x8, short) +OPENCV_HAL_IMPL_C_SHIFTR(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_SHIFTR(v_int32x4, int) +OPENCV_HAL_IMPL_C_SHIFTR(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_SHIFTR(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tpvec, _Tp) \ +template inline _Tpvec v_rshr(const _Tpvec& a) \ +{ \ + _Tpvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + return c; \ +} + +//! @name Rounding shift +//! @{ +//! @brief Rounding shift right +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int16x8, short) +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int32x4, int) +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_PACK(_Tpvec, _Tpnvec, _Tpn, pack_suffix) \ +inline _Tpnvec v_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpnvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + { \ + c.s[i] = saturate_cast<_Tpn>(a.s[i]); \ + c.s[i+_Tpvec::nlanes] = saturate_cast<_Tpn>(b.s[i]); \ + } \ + return c; \ +} + +//! @name Pack +//! @{ +//! @brief Pack values from two vectors to one +//! +//! Return vector type have twice more elements than input vector types. Variant with _u_ suffix also +//! converts to corresponding unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +OPENCV_HAL_IMPL_C_PACK(v_uint16x8, v_uint8x16, uchar, pack) +OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_int8x16, schar, pack) +OPENCV_HAL_IMPL_C_PACK(v_uint32x4, v_uint16x8, ushort, pack) +OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_int16x8, short, pack) +OPENCV_HAL_IMPL_C_PACK(v_uint64x2, v_uint32x4, unsigned, pack) +OPENCV_HAL_IMPL_C_PACK(v_int64x2, v_int32x4, int, pack) +OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_uint8x16, uchar, pack_u) +OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_uint16x8, ushort, pack_u) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix) \ +template inline _Tpnvec v_rshr_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpnvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + { \ + c.s[i] = saturate_cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + c.s[i+_Tpvec::nlanes] = saturate_cast<_Tpn>((b.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + } \ + return c; \ +} + +//! @name Pack with rounding shift +//! @{ +//! @brief Pack values from two vectors to one with rounding shift +//! +//! Values from the input vectors will be shifted right by _n_ bits with rounding, converted to narrower +//! type and returned in the result vector. Variant with _u_ suffix converts to unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint16x8, ushort, v_uint8x16, uchar, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_int8x16, schar, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint32x4, unsigned, v_uint16x8, ushort, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_int16x8, short, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint64x2, uint64, v_uint32x4, unsigned, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int64x2, int64, v_int32x4, int, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_uint8x16, uchar, pack_u) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_uint16x8, ushort, pack_u) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix) \ +inline void v_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ +{ \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + ptr[i] = saturate_cast<_Tpn>(a.s[i]); \ +} + +//! @name Pack and store +//! @{ +//! @brief Store values from the input vector into memory with pack +//! +//! Values will be stored into memory with saturating conversion to narrower type. +//! Variant with _u_ suffix converts to corresponding unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack) +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_int16x8, short, pack) +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix) \ +template inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ +{ \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + ptr[i] = saturate_cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ +} + +//! @name Pack and store with rounding shift +//! @{ +//! @brief Store values from the input vector into memory with pack +//! +//! Values will be shifted _n_ bits right with rounding, converted to narrower type and stored into +//! memory. Variant with _u_ suffix converts to unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_int16x8, short, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u) +//! @} + +/** @brief Matrix multiplication + +Scheme: +@code +{A0 A1 A2 A3} |V0| +{B0 B1 B2 B3} |V1| +{C0 C1 C2 C3} |V2| +{D0 D1 D2 D3} x |V3| +==================== +{R0 R1 R2 R3}, where: +R0 = A0V0 + A1V1 + A2V2 + A3V3, +R1 = B0V0 + B1V1 + B2V2 + B3V3 +... +@endcode +*/ +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + v.s[3]*m3.s[0], + v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + v.s[3]*m3.s[1], + v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + v.s[3]*m3.s[2], + v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + v.s[3]*m3.s[3]); +} + +//! @} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return false; +} + +//! @} + + +} + +#endif diff --git a/thirdparty/linux/include/opencv2/core/hal/intrin_neon.hpp b/thirdparty/linux/include/opencv2/core/hal/intrin_neon.hpp new file mode 100644 index 0000000..b000733 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/hal/intrin_neon.hpp @@ -0,0 +1,1234 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_NEON_HPP +#define OPENCV_HAL_INTRIN_NEON_HPP + +#include +#include "opencv2/core/utility.hpp" + +namespace cv +{ + +//! @cond IGNORED + +#define CV_SIMD128 1 +#if defined(__aarch64__) +#define CV_SIMD128_64F 1 +#else +#define CV_SIMD128_64F 0 +#endif + +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) \ +template static inline \ +_Tpv vreinterpretq_##suffix##_f64(T a) { return (_Tpv) a; } \ +template static inline \ +float64x2_t vreinterpretq_f64_##suffix(T a) { return (float64x2_t) a; } +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint8x16_t, u8) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int8x16_t, s8) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint16x8_t, u16) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int16x8_t, s16) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint32x4_t, u32) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int32x4_t, s32) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint64x2_t, u64) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int64x2_t, s64) +OPENCV_HAL_IMPL_NEON_REINTERPRET(float32x4_t, f32) +#endif + +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + + v_uint8x16() {} + explicit v_uint8x16(uint8x16_t v) : val(v) {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + val = vld1q_u8(v); + } + uchar get0() const + { + return vgetq_lane_u8(val, 0); + } + + uint8x16_t val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + + v_int8x16() {} + explicit v_int8x16(int8x16_t v) : val(v) {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + val = vld1q_s8(v); + } + schar get0() const + { + return vgetq_lane_s8(val, 0); + } + + int8x16_t val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + + v_uint16x8() {} + explicit v_uint16x8(uint16x8_t v) : val(v) {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + val = vld1q_u16(v); + } + ushort get0() const + { + return vgetq_lane_u16(val, 0); + } + + uint16x8_t val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + + v_int16x8() {} + explicit v_int16x8(int16x8_t v) : val(v) {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + val = vld1q_s16(v); + } + short get0() const + { + return vgetq_lane_s16(val, 0); + } + + int16x8_t val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + + v_uint32x4() {} + explicit v_uint32x4(uint32x4_t v) : val(v) {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + unsigned v[] = {v0, v1, v2, v3}; + val = vld1q_u32(v); + } + unsigned get0() const + { + return vgetq_lane_u32(val, 0); + } + + uint32x4_t val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + + v_int32x4() {} + explicit v_int32x4(int32x4_t v) : val(v) {} + v_int32x4(int v0, int v1, int v2, int v3) + { + int v[] = {v0, v1, v2, v3}; + val = vld1q_s32(v); + } + int get0() const + { + return vgetq_lane_s32(val, 0); + } + int32x4_t val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + + v_float32x4() {} + explicit v_float32x4(float32x4_t v) : val(v) {} + v_float32x4(float v0, float v1, float v2, float v3) + { + float v[] = {v0, v1, v2, v3}; + val = vld1q_f32(v); + } + float get0() const + { + return vgetq_lane_f32(val, 0); + } + float32x4_t val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + + v_uint64x2() {} + explicit v_uint64x2(uint64x2_t v) : val(v) {} + v_uint64x2(unsigned v0, unsigned v1) + { + uint64 v[] = {v0, v1}; + val = vld1q_u64(v); + } + uint64 get0() const + { + return vgetq_lane_u64(val, 0); + } + uint64x2_t val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + + v_int64x2() {} + explicit v_int64x2(int64x2_t v) : val(v) {} + v_int64x2(int v0, int v1) + { + int64 v[] = {v0, v1}; + val = vld1q_s64(v); + } + int64 get0() const + { + return vgetq_lane_s64(val, 0); + } + int64x2_t val; +}; + +#if CV_SIMD128_64F +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + + v_float64x2() {} + explicit v_float64x2(float64x2_t v) : val(v) {} + v_float64x2(double v0, double v1) + { + double v[] = {v0, v1}; + val = vld1q_f64(v); + } + double get0() const + { + return vgetq_lane_f64(val, 0); + } + float64x2_t val; +}; +#endif + +#if defined (HAVE_FP16) +// Workaround for old comiplers +template static inline int16x4_t vreinterpret_s16_f16(T a) +{ return (int16x4_t)a; } +template static inline float16x4_t vreinterpret_f16_s16(T a) +{ return (float16x4_t)a; } +template static inline float16x4_t vld1_f16(const T* ptr) +{ return vreinterpret_f16_s16(vld1_s16((const short*)ptr)); } +template static inline void vst1_f16(T* ptr, float16x4_t a) +{ vst1_s16((short*)ptr, vreinterpret_s16_f16(a)); } + +struct v_float16x4 +{ + typedef short lane_type; + enum { nlanes = 4 }; + + v_float16x4() {} + explicit v_float16x4(float16x4_t v) : val(v) {} + v_float16x4(short v0, short v1, short v2, short v3) + { + short v[] = {v0, v1, v2, v3}; + val = vld1_f16(v); + } + short get0() const + { + return vget_lane_s16(vreinterpret_s16_f16(val), 0); + } + float16x4_t val; +}; +#endif + +#define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \ +inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \ +inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(vdupq_n_##suffix(v)); } \ +inline _Tpv##_t vreinterpretq_##suffix##_##suffix(_Tpv##_t v) { return v; } \ +inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(vreinterpretq_u8_##suffix(v.val)); } \ +inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(vreinterpretq_s8_##suffix(v.val)); } \ +inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(vreinterpretq_u16_##suffix(v.val)); } \ +inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(vreinterpretq_s16_##suffix(v.val)); } \ +inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(vreinterpretq_u32_##suffix(v.val)); } \ +inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(vreinterpretq_s32_##suffix(v.val)); } \ +inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(vreinterpretq_u64_##suffix(v.val)); } \ +inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(vreinterpretq_s64_##suffix(v.val)); } \ +inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(vreinterpretq_f32_##suffix(v.val)); } + +OPENCV_HAL_IMPL_NEON_INIT(uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_INIT(int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_INIT(uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_INIT(int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_INIT(uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_INIT(int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_INIT(uint64x2, uint64, u64) +OPENCV_HAL_IMPL_NEON_INIT(int64x2, int64, s64) +OPENCV_HAL_IMPL_NEON_INIT(float32x4, float, f32) +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_INIT_64(_Tpv, suffix) \ +inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(vreinterpretq_f64_##suffix(v.val)); } +OPENCV_HAL_IMPL_NEON_INIT(float64x2, double, f64) +OPENCV_HAL_IMPL_NEON_INIT_64(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_INIT_64(int8x16, s8) +OPENCV_HAL_IMPL_NEON_INIT_64(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_INIT_64(int16x8, s16) +OPENCV_HAL_IMPL_NEON_INIT_64(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_INIT_64(int32x4, s32) +OPENCV_HAL_IMPL_NEON_INIT_64(uint64x2, u64) +OPENCV_HAL_IMPL_NEON_INIT_64(int64x2, s64) +OPENCV_HAL_IMPL_NEON_INIT_64(float32x4, f32) +OPENCV_HAL_IMPL_NEON_INIT_64(float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_PACK(_Tpvec, _Tp, hreg, suffix, _Tpwvec, wsuffix, pack, op) \ +inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + hreg a1 = vqmov##op##_##wsuffix(a.val), b1 = vqmov##op##_##wsuffix(b.val); \ + return _Tpvec(vcombine_##suffix(a1, b1)); \ +} \ +inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + hreg a1 = vqmov##op##_##wsuffix(a.val); \ + vst1_##suffix(ptr, a1); \ +} \ +template inline \ +_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + hreg a1 = vqrshr##op##_n_##wsuffix(a.val, n); \ + hreg b1 = vqrshr##op##_n_##wsuffix(b.val, n); \ + return _Tpvec(vcombine_##suffix(a1, b1)); \ +} \ +template inline \ +void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + hreg a1 = vqrshr##op##_n_##wsuffix(a.val, n); \ + vst1_##suffix(ptr, a1); \ +} + +OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_uint16x8, u16, pack, n) +OPENCV_HAL_IMPL_NEON_PACK(v_int8x16, schar, int8x8_t, s8, v_int16x8, s16, pack, n) +OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_uint32x4, u32, pack, n) +OPENCV_HAL_IMPL_NEON_PACK(v_int16x8, short, int16x4_t, s16, v_int32x4, s32, pack, n) +OPENCV_HAL_IMPL_NEON_PACK(v_uint32x4, unsigned, uint32x2_t, u32, v_uint64x2, u64, pack, n) +OPENCV_HAL_IMPL_NEON_PACK(v_int32x4, int, int32x2_t, s32, v_int64x2, s64, pack, n) + +OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_int16x8, s16, pack_u, un) +OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_int32x4, s32, pack_u, un) + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); + float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); + res = vmlaq_lane_f32(res, m1.val, vl, 1); + res = vmlaq_lane_f32(res, m2.val, vh, 0); + res = vmlaq_lane_f32(res, m3.val, vh, 1); + return v_float32x4(res); +} + +#define OPENCV_HAL_IMPL_NEON_BIN_OP(bin_op, _Tpvec, intrin) \ +inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} \ +inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ +{ \ + a.val = intrin(a.val, b.val); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint8x16, vqaddq_u8) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint8x16, vqsubq_u8) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int8x16, vqaddq_s8) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int8x16, vqsubq_s8) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint16x8, vqaddq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint16x8, vqsubq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint16x8, vmulq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int16x8, vqaddq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int16x8, vqsubq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int16x8, vmulq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int32x4, vaddq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int32x4, vsubq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int32x4, vmulq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint32x4, vaddq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint32x4, vsubq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint32x4, vmulq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float32x4, vaddq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float32x4, vsubq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float32x4, vmulq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int64x2, vaddq_s64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int64x2, vsubq_s64) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint64x2, vaddq_u64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint64x2, vsubq_u64) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float32x4, vdivq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float64x2, vaddq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float64x2, vsubq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float64x2, vmulq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float64x2, vdivq_f64) +#else +inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b) +{ + float32x4_t reciprocal = vrecpeq_f32(b.val); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + return v_float32x4(vmulq_f32(a.val, reciprocal)); +} +inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b) +{ + float32x4_t reciprocal = vrecpeq_f32(b.val); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + a.val = vmulq_f32(a.val, reciprocal); + return a; +} +#endif + +inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, + v_int32x4& c, v_int32x4& d) +{ + c.val = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); + d.val = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); +} + +inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, + v_uint32x4& c, v_uint32x4& d) +{ + c.val = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val)); + d.val = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, + v_uint64x2& c, v_uint64x2& d) +{ + c.val = vmull_u32(vget_low_u32(a.val), vget_low_u32(b.val)); + d.val = vmull_u32(vget_high_u32(a.val), vget_high_u32(b.val)); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ + int32x4_t c = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); + int32x4_t d = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); + int32x4x2_t cd = vuzpq_s32(c, d); + return v_int32x4(vaddq_s32(cd.val[0], cd.val[1])); +} + +#define OPENCV_HAL_IMPL_NEON_LOGIC_OP(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(&, _Tpvec, vandq_##suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(|, _Tpvec, vorrq_##suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(^, _Tpvec, veorq_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { \ + return _Tpvec(vreinterpretq_##suffix##_u8(vmvnq_u8(vreinterpretq_u8_##suffix(a.val)))); \ + } + +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint8x16, u8) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int8x16, s8) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint16x8, u16) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int16x8, s16) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint32x4, u32) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int32x4, s32) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint64x2, u64) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int64x2, s64) + +#define OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(bin_op, intrin) \ +inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \ +{ \ + return v_float32x4(vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val)))); \ +} \ +inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \ +{ \ + a.val = vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val))); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(&, vandq_s32) +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(|, vorrq_s32) +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(^, veorq_s32) + +inline v_float32x4 operator ~ (const v_float32x4& a) +{ + return v_float32x4(vreinterpretq_f32_s32(vmvnq_s32(vreinterpretq_s32_f32(a.val)))); +} + +#if CV_SIMD128_64F +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ + return v_float32x4(vsqrtq_f32(x.val)); +} + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + v_float32x4 one = v_setall_f32(1.0f); + return one / v_sqrt(x); +} +#else +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ + float32x4_t x1 = vmaxq_f32(x.val, vdupq_n_f32(FLT_MIN)); + float32x4_t e = vrsqrteq_f32(x1); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); + return v_float32x4(vmulq_f32(x.val, e)); +} + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + float32x4_t e = vrsqrteq_f32(x.val); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); + return v_float32x4(e); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \ +inline _Tpuvec v_abs(const _Tpsvec& a) { return v_reinterpret_as_##usuffix(_Tpsvec(vabsq_##ssuffix(a.val))); } + +OPENCV_HAL_IMPL_NEON_ABS(v_uint8x16, v_int8x16, u8, s8) +OPENCV_HAL_IMPL_NEON_ABS(v_uint16x8, v_int16x8, u16, s16) +OPENCV_HAL_IMPL_NEON_ABS(v_uint32x4, v_int32x4, u32, s32) + +inline v_float32x4 v_abs(v_float32x4 x) +{ return v_float32x4(vabsq_f32(x.val)); } + +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(bin_op, intrin) \ +inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \ +{ \ + return v_float64x2(vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val)))); \ +} \ +inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \ +{ \ + a.val = vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val))); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(&, vandq_s64) +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(|, vorrq_s64) +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(^, veorq_s64) + +inline v_float64x2 operator ~ (const v_float64x2& a) +{ + return v_float64x2(vreinterpretq_f64_s32(vmvnq_s32(vreinterpretq_s32_f64(a.val)))); +} + +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ + return v_float64x2(vsqrtq_f64(x.val)); +} + +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ + v_float64x2 one = v_setall_f64(1.0f); + return one / v_sqrt(x); +} + +inline v_float64x2 v_abs(v_float64x2 x) +{ return v_float64x2(vabsq_f64(x.val)); } +#endif + +// TODO: exp, log, sin, cos + +#define OPENCV_HAL_IMPL_NEON_BIN_FUNC(_Tpvec, func, intrin) \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_min, vminq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_max, vmaxq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_min, vminq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_max, vmaxq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_min, vminq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_max, vmaxq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_min, vminq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_max, vmaxq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_min, vminq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_max, vmaxq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_min, vminq_s32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_max, vmaxq_s32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_min, vminq_f32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_max, vmaxq_f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_min, vminq_f64) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_max, vmaxq_f64) +#endif + +#if CV_SIMD128_64F +inline int64x2_t vmvnq_s64(int64x2_t a) +{ + int64x2_t vx = vreinterpretq_s64_u32(vdupq_n_u32(0xFFFFFFFF)); + return veorq_s64(a, vx); +} +inline uint64x2_t vmvnq_u64(uint64x2_t a) +{ + uint64x2_t vx = vreinterpretq_u64_u32(vdupq_n_u32(0xFFFFFFFF)); + return veorq_u64(a, vx); +} +#endif +#define OPENCV_HAL_IMPL_NEON_INT_CMP_OP(_Tpvec, cast, suffix, not_suffix) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vceqq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vmvnq_##not_suffix(vceqq_##suffix(a.val, b.val)))); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcltq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcgtq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcleq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcgeq_##suffix(a.val, b.val))); } + +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint8x16, OPENCV_HAL_NOP, u8, u8) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int8x16, vreinterpretq_s8_u8, s8, u8) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint16x8, OPENCV_HAL_NOP, u16, u16) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int16x8, vreinterpretq_s16_u16, s16, u16) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint32x4, OPENCV_HAL_NOP, u32, u32) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int32x4, vreinterpretq_s32_u32, s32, u32) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float32x4, vreinterpretq_f32_u32, f32, u32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint64x2, OPENCV_HAL_NOP, u64, u64) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int64x2, vreinterpretq_s64_u64, s64, u64) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float64x2, vreinterpretq_f64_u64, f64, u64) +#endif + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_add_wrap, vaddq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_sub_wrap, vsubq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_sub_wrap, vsubq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_sub_wrap, vsubq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_sub_wrap, vsubq_s16) + +// TODO: absdiff for signed integers +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_absdiff, vabdq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_absdiff, vabdq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_absdiff, vabdq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_absdiff, vabdq_f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_absdiff, vabdq_f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin) \ +inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec2(cast(intrin(a.val, b.val))); \ +} + +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int8x16, v_uint8x16, vreinterpretq_u8_s8, v_absdiff, vabdq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int16x8, v_uint16x8, vreinterpretq_u16_s16, v_absdiff, vabdq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int32x4, v_uint32x4, vreinterpretq_u32_s32, v_absdiff, vabdq_s32) + +inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b) +{ + v_float32x4 x(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); + return v_sqrt(x); +} + +inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b) +{ + return v_float32x4(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); +} + +inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ + return v_float32x4(vmlaq_f32(c.val, a.val, b.val)); +} + +#if CV_SIMD128_64F +inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b) +{ + v_float64x2 x(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); + return v_sqrt(x); +} + +inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b) +{ + return v_float64x2(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); +} + +inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ + return v_float64x2(vaddq_f64(c.val, vmulq_f64(a.val, b.val))); +} +#endif + +// trade efficiency for convenience +#define OPENCV_HAL_IMPL_NEON_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \ +inline _Tpvec operator << (const _Tpvec& a, int n) \ +{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)n))); } \ +inline _Tpvec operator >> (const _Tpvec& a, int n) \ +{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)-n))); } \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return _Tpvec(vshlq_n_##suffix(a.val, n)); } \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return _Tpvec(vshrq_n_##suffix(a.val, n)); } \ +template inline _Tpvec v_rshr(const _Tpvec& a) \ +{ return _Tpvec(vrshrq_n_##suffix(a.val, n)); } + +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint8x16, u8, schar, s8) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int8x16, s8, schar, s8) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint16x8, u16, short, s16) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int16x8, s16, short, s16) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint32x4, u32, int, s32) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int32x4, s32, int, s32) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint64x2, u64, int64, s64) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int64x2, s64, int64, s64) + +#define OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(vld1q_##suffix(ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(vld1q_##suffix(ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr0), vld1_##suffix(ptr1))); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ vst1_##suffix(ptr, vget_low_##suffix(a.val)); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ vst1_##suffix(ptr, vget_high_##suffix(a.val)); } + +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int64x2, int64, s64) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float64x2, double, f64) +#endif + +#if defined (HAVE_FP16) +// Workaround for old comiplers +inline v_float16x4 v_load_f16(const short* ptr) +{ return v_float16x4(vld1_f16(ptr)); } +inline void v_store_f16(short* ptr, v_float16x4& a) +{ vst1_f16(ptr, a.val); } +#endif + +#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ + a0 = vp##vectorfunc##_##suffix(a0, a0); \ + return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \ +} + +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, sum, add, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, max, max, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, min, min, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, sum, add, s16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, max, max, s16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, min, min, s16) + +#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ + return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, vget_high_##suffix(a.val)),0); \ +} + +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, sum, add, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, max, max, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, min, min, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, sum, add, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, max, max, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, min, min, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, sum, add, f32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, max, max, f32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, min, min, f32) + +inline int v_signmask(const v_uint8x16& a) +{ + int8x8_t m0 = vcreate_s8(CV_BIG_UINT(0x0706050403020100)); + uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), vcombine_s8(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(v0))); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 8); +} +inline int v_signmask(const v_int8x16& a) +{ return v_signmask(v_reinterpret_as_u8(a)); } + +inline int v_signmask(const v_uint16x8& a) +{ + int16x4_t m0 = vcreate_s16(CV_BIG_UINT(0x0003000200010000)); + uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), vcombine_s16(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(v0)); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 4); +} +inline int v_signmask(const v_int16x8& a) +{ return v_signmask(v_reinterpret_as_u16(a)); } + +inline int v_signmask(const v_uint32x4& a) +{ + int32x2_t m0 = vcreate_s32(CV_BIG_UINT(0x0000000100000000)); + uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), vcombine_s32(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(v0); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 2); +} +inline int v_signmask(const v_int32x4& a) +{ return v_signmask(v_reinterpret_as_u32(a)); } +inline int v_signmask(const v_float32x4& a) +{ return v_signmask(v_reinterpret_as_u32(a)); } +#if CV_SIMD128_64F +inline int v_signmask(const v_uint64x2& a) +{ + int64x1_t m0 = vdup_n_s64(0); + uint64x2_t v0 = vshlq_u64(vshrq_n_u64(a.val, 63), vcombine_s64(m0, m0)); + return (int)vgetq_lane_u64(v0, 0) + ((int)vgetq_lane_u64(v0, 1) << 1); +} +inline int v_signmask(const v_float64x2& a) +{ return v_signmask(v_reinterpret_as_u64(a)); } +#endif + +#define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \ +inline bool v_check_all(const v_##_Tpvec& a) \ +{ \ + _Tpvec##_t v0 = vshrq_n_##suffix(vmvnq_##suffix(a.val), shift); \ + uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ + return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) == 0; \ +} \ +inline bool v_check_any(const v_##_Tpvec& a) \ +{ \ + _Tpvec##_t v0 = vshrq_n_##suffix(a.val, shift); \ + uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ + return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) != 0; \ +} + +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint8x16, u8, 7) +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint16x8, u16, 15) +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint32x4, u32, 31) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint64x2, u64, 63) +#endif + +inline bool v_check_all(const v_int8x16& a) +{ return v_check_all(v_reinterpret_as_u8(a)); } +inline bool v_check_all(const v_int16x8& a) +{ return v_check_all(v_reinterpret_as_u16(a)); } +inline bool v_check_all(const v_int32x4& a) +{ return v_check_all(v_reinterpret_as_u32(a)); } +inline bool v_check_all(const v_float32x4& a) +{ return v_check_all(v_reinterpret_as_u32(a)); } + +inline bool v_check_any(const v_int8x16& a) +{ return v_check_any(v_reinterpret_as_u8(a)); } +inline bool v_check_any(const v_int16x8& a) +{ return v_check_any(v_reinterpret_as_u16(a)); } +inline bool v_check_any(const v_int32x4& a) +{ return v_check_any(v_reinterpret_as_u32(a)); } +inline bool v_check_any(const v_float32x4& a) +{ return v_check_any(v_reinterpret_as_u32(a)); } + +#if CV_SIMD128_64F +inline bool v_check_all(const v_int64x2& a) +{ return v_check_all(v_reinterpret_as_u64(a)); } +inline bool v_check_all(const v_float64x2& a) +{ return v_check_all(v_reinterpret_as_u64(a)); } +inline bool v_check_any(const v_int64x2& a) +{ return v_check_any(v_reinterpret_as_u64(a)); } +inline bool v_check_any(const v_float64x2& a) +{ return v_check_any(v_reinterpret_as_u64(a)); } +#endif + +#define OPENCV_HAL_IMPL_NEON_SELECT(_Tpvec, suffix, usuffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(vbslq_##suffix(vreinterpretq_##usuffix##_##suffix(mask.val), a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_NEON_SELECT(v_uint8x16, u8, u8) +OPENCV_HAL_IMPL_NEON_SELECT(v_int8x16, s8, u8) +OPENCV_HAL_IMPL_NEON_SELECT(v_uint16x8, u16, u16) +OPENCV_HAL_IMPL_NEON_SELECT(v_int16x8, s16, u16) +OPENCV_HAL_IMPL_NEON_SELECT(v_uint32x4, u32, u32) +OPENCV_HAL_IMPL_NEON_SELECT(v_int32x4, s32, u32) +OPENCV_HAL_IMPL_NEON_SELECT(v_float32x4, f32, u32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_SELECT(v_float64x2, f64, u64) +#endif + +#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \ +inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ +{ \ + b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \ + b1.val = vmovl_##suffix(vget_high_##suffix(a.val)); \ +} \ +inline _Tpwvec v_load_expand(const _Tp* ptr) \ +{ \ + return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \ +} + +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint8x16, v_uint16x8, uchar, u8) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int8x16, v_int16x8, schar, s8) +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint16x8, v_uint32x4, ushort, u16) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int16x8, v_int32x4, short, s16) +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint32x4, v_uint64x2, uint, u32) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int32x4, v_int64x2, int, s32) + +inline v_uint32x4 v_load_expand_q(const uchar* ptr) +{ + uint8x8_t v0 = vcreate_u8(*(unsigned*)ptr); + uint16x4_t v1 = vget_low_u16(vmovl_u8(v0)); + return v_uint32x4(vmovl_u16(v1)); +} + +inline v_int32x4 v_load_expand_q(const schar* ptr) +{ + int8x8_t v0 = vcreate_s8(*(unsigned*)ptr); + int16x4_t v1 = vget_low_s16(vmovl_s8(v0)); + return v_int32x4(vmovl_s16(v1)); +} + +#if defined(__aarch64__) +#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ +inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ +{ \ + b0.val = vzip1q_##suffix(a0.val, a1.val); \ + b1.val = vzip2q_##suffix(a0.val, a1.val); \ +} \ +inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ +} \ +inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ +} \ +inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ + d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ +} +#else +#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ +inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ +{ \ + _Tpvec##x2_t p = vzipq_##suffix(a0.val, a1.val); \ + b0.val = p.val[0]; \ + b1.val = p.val[1]; \ +} \ +inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ +} \ +inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ +} \ +inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ + d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ +} +#endif + +OPENCV_HAL_IMPL_NEON_UNPACKS(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_UNPACKS(int8x16, s8) +OPENCV_HAL_IMPL_NEON_UNPACKS(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_UNPACKS(int16x8, s16) +OPENCV_HAL_IMPL_NEON_UNPACKS(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_UNPACKS(int32x4, s32) +OPENCV_HAL_IMPL_NEON_UNPACKS(float32x4, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_UNPACKS(float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_EXTRACT(_Tpvec, suffix) \ +template \ +inline v_##_Tpvec v_extract(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vextq_##suffix(a.val, b.val, s)); \ +} + +OPENCV_HAL_IMPL_NEON_EXTRACT(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_EXTRACT(int8x16, s8) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_EXTRACT(int16x8, s16) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_EXTRACT(int32x4, s32) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint64x2, u64) +OPENCV_HAL_IMPL_NEON_EXTRACT(int64x2, s64) +OPENCV_HAL_IMPL_NEON_EXTRACT(float32x4, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_EXTRACT(float64x2, f64) +#endif + +inline v_int32x4 v_round(const v_float32x4& a) +{ + static const int32x4_t v_sign = vdupq_n_s32(1 << 31), + v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); + + int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(a.val))); + return v_int32x4(vcvtq_s32_f32(vaddq_f32(a.val, vreinterpretq_f32_s32(v_addition)))); +} + +inline v_int32x4 v_floor(const v_float32x4& a) +{ + int32x4_t a1 = vcvtq_s32_f32(a.val); + uint32x4_t mask = vcgtq_f32(vcvtq_f32_s32(a1), a.val); + return v_int32x4(vaddq_s32(a1, vreinterpretq_s32_u32(mask))); +} + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ + int32x4_t a1 = vcvtq_s32_f32(a.val); + uint32x4_t mask = vcgtq_f32(a.val, vcvtq_f32_s32(a1)); + return v_int32x4(vsubq_s32(a1, vreinterpretq_s32_u32(mask))); +} + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(vcvtq_s32_f32(a.val)); } + +#if CV_SIMD128_64F +inline v_int32x4 v_round(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); +} + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + int64x2_t a1 = vcvtq_s64_f64(a.val); + uint64x2_t mask = vcgtq_f64(vcvtq_f64_s64(a1), a.val); + a1 = vaddq_s64(a1, vreinterpretq_s64_u64(mask)); + return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + int64x2_t a1 = vcvtq_s64_f64(a.val); + uint64x2_t mask = vcgtq_f64(a.val, vcvtq_f64_s64(a1)); + a1 = vsubq_s64(a1, vreinterpretq_s64_u64(mask)); + return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \ +inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \ + const v_##_Tpvec& a2, const v_##_Tpvec& a3, \ + v_##_Tpvec& b0, v_##_Tpvec& b1, \ + v_##_Tpvec& b2, v_##_Tpvec& b3) \ +{ \ + /* m00 m01 m02 m03 */ \ + /* m10 m11 m12 m13 */ \ + /* m20 m21 m22 m23 */ \ + /* m30 m31 m32 m33 */ \ + _Tpvec##x2_t t0 = vtrnq_##suffix(a0.val, a1.val); \ + _Tpvec##x2_t t1 = vtrnq_##suffix(a2.val, a3.val); \ + /* m00 m10 m02 m12 */ \ + /* m01 m11 m03 m13 */ \ + /* m20 m30 m22 m32 */ \ + /* m21 m31 m23 m33 */ \ + b0.val = vcombine_##suffix(vget_low_##suffix(t0.val[0]), vget_low_##suffix(t1.val[0])); \ + b1.val = vcombine_##suffix(vget_low_##suffix(t0.val[1]), vget_low_##suffix(t1.val[1])); \ + b2.val = vcombine_##suffix(vget_high_##suffix(t0.val[0]), vget_high_##suffix(t1.val[0])); \ + b3.val = vcombine_##suffix(vget_high_##suffix(t0.val[1]), vget_high_##suffix(t1.val[1])); \ +} + +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s32) +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f32) + +#define OPENCV_HAL_IMPL_NEON_INTERLEAVED(_Tpvec, _Tp, suffix) \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \ +{ \ + _Tpvec##x2_t v = vld2q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ +} \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \ +{ \ + _Tpvec##x3_t v = vld3q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ + c.val = v.val[2]; \ +} \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \ + v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + _Tpvec##x4_t v = vld4q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ + c.val = v.val[2]; \ + d.val = v.val[3]; \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + _Tpvec##x2_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + vst2q_##suffix(ptr, v); \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, const v_##_Tpvec& c) \ +{ \ + _Tpvec##x3_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + v.val[2] = c.val; \ + vst3q_##suffix(ptr, v); \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + const v_##_Tpvec& c, const v_##_Tpvec& d) \ +{ \ + _Tpvec##x4_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + v.val[2] = c.val; \ + v.val[3] = d.val; \ + vst4q_##suffix(ptr, v); \ +} + +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(float32x4, float, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_INTERLEAVED(float64x2, double, f64) +#endif + +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ + return v_float32x4(vcvtq_f32_s32(a.val)); +} + +#if CV_SIMD128_64F +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + float32x2_t zero = vdup_n_f32(0.0f); + return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), zero)); +} + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_low_s32(a.val)))); +} + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_high_s32(a.val)))); +} + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vget_low_f32(a.val))); +} + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vget_high_f32(a.val))); +} +#endif + +#if defined (HAVE_FP16) +inline v_float32x4 v_cvt_f32(const v_float16x4& a) +{ + return v_float32x4(vcvt_f32_f16(a.val)); +} + +inline v_float16x4 v_cvt_f16(const v_float32x4& a) +{ + return v_float16x4(vcvt_f16_f32(a.val)); +} +#endif + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return checkHardwareSupport(CV_CPU_NEON); +} + +//! @} + +//! @endcond + +} + +#endif diff --git a/thirdparty/linux/include/opencv2/core/hal/intrin_sse.hpp b/thirdparty/linux/include/opencv2/core/hal/intrin_sse.hpp new file mode 100644 index 0000000..fc81dac --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/hal/intrin_sse.hpp @@ -0,0 +1,1744 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_SSE_HPP +#define OPENCV_HAL_SSE_HPP + +#include +#include "opencv2/core/utility.hpp" + +#define CV_SIMD128 1 +#define CV_SIMD128_64F 1 + +namespace cv +{ + +//! @cond IGNORED + +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + + v_uint8x16() {} + explicit v_uint8x16(__m128i v) : val(v) {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6, (char)v7, + (char)v8, (char)v9, (char)v10, (char)v11, + (char)v12, (char)v13, (char)v14, (char)v15); + } + uchar get0() const + { + return (uchar)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + + v_int8x16() {} + explicit v_int8x16(__m128i v) : val(v) {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6, (char)v7, + (char)v8, (char)v9, (char)v10, (char)v11, + (char)v12, (char)v13, (char)v14, (char)v15); + } + schar get0() const + { + return (schar)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + + v_uint16x8() {} + explicit v_uint16x8(__m128i v) : val(v) {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7); + } + ushort get0() const + { + return (ushort)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + + v_int16x8() {} + explicit v_int16x8(__m128i v) : val(v) {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7); + } + short get0() const + { + return (short)_mm_cvtsi128_si32(val); + } + __m128i val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + + v_uint32x4() {} + explicit v_uint32x4(__m128i v) : val(v) {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + val = _mm_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3); + } + unsigned get0() const + { + return (unsigned)_mm_cvtsi128_si32(val); + } + __m128i val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + + v_int32x4() {} + explicit v_int32x4(__m128i v) : val(v) {} + v_int32x4(int v0, int v1, int v2, int v3) + { + val = _mm_setr_epi32(v0, v1, v2, v3); + } + int get0() const + { + return _mm_cvtsi128_si32(val); + } + __m128i val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + + v_float32x4() {} + explicit v_float32x4(__m128 v) : val(v) {} + v_float32x4(float v0, float v1, float v2, float v3) + { + val = _mm_setr_ps(v0, v1, v2, v3); + } + float get0() const + { + return _mm_cvtss_f32(val); + } + __m128 val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + + v_uint64x2() {} + explicit v_uint64x2(__m128i v) : val(v) {} + v_uint64x2(uint64 v0, uint64 v1) + { + val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); + } + uint64 get0() const + { + int a = _mm_cvtsi128_si32(val); + int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); + return (unsigned)a | ((uint64)(unsigned)b << 32); + } + __m128i val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + + v_int64x2() {} + explicit v_int64x2(__m128i v) : val(v) {} + v_int64x2(int64 v0, int64 v1) + { + val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); + } + int64 get0() const + { + int a = _mm_cvtsi128_si32(val); + int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); + return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); + } + __m128i val; +}; + +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + + v_float64x2() {} + explicit v_float64x2(__m128d v) : val(v) {} + v_float64x2(double v0, double v1) + { + val = _mm_setr_pd(v0, v1); + } + double get0() const + { + return _mm_cvtsd_f64(val); + } + __m128d val; +}; + +#if defined(HAVE_FP16) +struct v_float16x4 +{ + typedef short lane_type; + enum { nlanes = 4 }; + + v_float16x4() {} + explicit v_float16x4(__m128i v) : val(v) {} + v_float16x4(short v0, short v1, short v2, short v3) + { + val = _mm_setr_epi16(v0, v1, v2, v3, 0, 0, 0, 0); + } + short get0() const + { + return (short)_mm_cvtsi128_si32(val); + } + __m128i val; +}; +#endif + +#define OPENCV_HAL_IMPL_SSE_INITVEC(_Tpvec, _Tp, suffix, zsuffix, ssuffix, _Tps, cast) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec(_mm_setzero_##zsuffix()); } \ +inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(_mm_set1_##ssuffix((_Tps)v)); } \ +template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \ +{ return _Tpvec(cast(a.val)); } + +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint8x16, uchar, u8, si128, epi8, char, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int8x16, schar, s8, si128, epi8, char, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint16x8, ushort, u16, si128, epi16, short, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int16x8, short, s16, si128, epi16, short, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint32x4, unsigned, u32, si128, epi32, int, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int32x4, int, s32, si128, epi32, int, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_float32x4, float, f32, ps, ps, float, _mm_castsi128_ps) +OPENCV_HAL_IMPL_SSE_INITVEC(v_float64x2, double, f64, pd, pd, double, _mm_castsi128_pd) + +inline v_uint64x2 v_setzero_u64() { return v_uint64x2(_mm_setzero_si128()); } +inline v_int64x2 v_setzero_s64() { return v_int64x2(_mm_setzero_si128()); } +inline v_uint64x2 v_setall_u64(uint64 val) { return v_uint64x2(val, val); } +inline v_int64x2 v_setall_s64(int64 val) { return v_int64x2(val, val); } + +template inline +v_uint64x2 v_reinterpret_as_u64(const _Tpvec& a) { return v_uint64x2(a.val); } +template inline +v_int64x2 v_reinterpret_as_s64(const _Tpvec& a) { return v_int64x2(a.val); } +inline v_float32x4 v_reinterpret_as_f32(const v_uint64x2& a) +{ return v_float32x4(_mm_castsi128_ps(a.val)); } +inline v_float32x4 v_reinterpret_as_f32(const v_int64x2& a) +{ return v_float32x4(_mm_castsi128_ps(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_uint64x2& a) +{ return v_float64x2(_mm_castsi128_pd(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_int64x2& a) +{ return v_float64x2(_mm_castsi128_pd(a.val)); } + +#define OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(_Tpvec, suffix) \ +inline _Tpvec v_reinterpret_as_##suffix(const v_float32x4& a) \ +{ return _Tpvec(_mm_castps_si128(a.val)); } \ +inline _Tpvec v_reinterpret_as_##suffix(const v_float64x2& a) \ +{ return _Tpvec(_mm_castpd_si128(a.val)); } + +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint8x16, u8) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int8x16, s8) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint16x8, u16) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int16x8, s16) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint32x4, u32) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int32x4, s32) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint64x2, u64) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int64x2, s64) + +inline v_float32x4 v_reinterpret_as_f32(const v_float32x4& a) {return a; } +inline v_float64x2 v_reinterpret_as_f64(const v_float64x2& a) {return a; } +inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& a) {return v_float32x4(_mm_castpd_ps(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& a) {return v_float64x2(_mm_castps_pd(a.val)); } + +//////////////// PACK /////////////// +inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b) +{ + __m128i delta = _mm_set1_epi16(255); + return v_uint8x16(_mm_packus_epi16(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)), + _mm_subs_epu16(b.val, _mm_subs_epu16(b.val, delta)))); +} + +inline void v_pack_store(uchar* ptr, const v_uint16x8& a) +{ + __m128i delta = _mm_set1_epi16(255); + __m128i a1 = _mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b) +{ return v_uint8x16(_mm_packus_epi16(a.val, b.val)); } + +inline void v_pack_u_store(uchar* ptr, const v_int16x8& a) +{ _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a.val, a.val)); } + +template inline +v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_uint8x16(_mm_packus_epi16(_mm_srli_epi16(_mm_adds_epu16(a.val, delta), n), + _mm_srli_epi16(_mm_adds_epu16(b.val, delta), n))); +} + +template inline +void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srli_epi16(_mm_adds_epu16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +template inline +v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_uint8x16(_mm_packus_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), + _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); +} + +template inline +void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b) +{ return v_int8x16(_mm_packs_epi16(a.val, b.val)); } + +inline void v_pack_store(schar* ptr, v_int16x8& a) +{ _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a.val, a.val)); } + +template inline +v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_int8x16(_mm_packs_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), + _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); +} +template inline +void v_rshr_pack_store(schar* ptr, const v_int16x8& a) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a1, a1)); +} + + +// bit-wise "mask ? a : b" +inline __m128i v_select_si128(__m128i mask, __m128i a, __m128i b) +{ + return _mm_xor_si128(b, _mm_and_si128(_mm_xor_si128(a, b), mask)); +} + +inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b) +{ + __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32); + __m128i b1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, b.val), maxval32, b.val), delta32); + __m128i r = _mm_packs_epi32(a1, b1); + return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768))); +} + +inline void v_pack_store(ushort* ptr, const v_uint32x4& a) +{ + __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32); + __m128i r = _mm_packs_epi32(a1, a1); + _mm_storel_epi64((__m128i*)ptr, _mm_sub_epi16(r, _mm_set1_epi16(-32768))); +} + +template inline +v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i b1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(b.val, delta), n), delta32); + return v_uint16x8(_mm_sub_epi16(_mm_packs_epi32(a1, b1), _mm_set1_epi16(-32768))); +} + +template inline +void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b) +{ + __m128i delta32 = _mm_set1_epi32(32768); + __m128i r = _mm_packs_epi32(_mm_sub_epi32(a.val, delta32), _mm_sub_epi32(b.val, delta32)); + return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768))); +} + +inline void v_pack_u_store(ushort* ptr, const v_int32x4& a) +{ + __m128i delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(a.val, delta32); + __m128i r = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, r); +} + +template inline +v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + __m128i b1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(b.val, delta), n), delta32); + __m128i b2 = _mm_sub_epi16(_mm_packs_epi32(b1, b1), _mm_set1_epi16(-32768)); + return v_uint16x8(_mm_unpacklo_epi64(a2, b2)); +} + +template inline +void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b) +{ return v_int16x8(_mm_packs_epi32(a.val, b.val)); } + +inline void v_pack_store(short* ptr, const v_int32x4& a) +{ + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a.val, a.val)); +} + +template inline +v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)); + return v_int16x8(_mm_packs_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), + _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); +} + +template inline +void v_rshr_pack_store(short* ptr, const v_int32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)); + __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a1, a1)); +} + + +// [a0 0 | b0 0] [a1 0 | b1 0] +inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 + return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); +} + +inline void v_pack_store(unsigned* ptr, const v_uint64x2& a) +{ + __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a1); +} + +// [a0 0 | b0 0] [a1 0 | b1 0] +inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 + return v_int32x4(_mm_unpacklo_epi32(v0, v1)); +} + +inline void v_pack_store(int* ptr, const v_int64x2& a) +{ + __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a1); +} + +template inline +v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b) +{ + uint64 delta = (uint64)1 << (n-1); + v_uint64x2 delta2(delta, delta); + __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i b1 = _mm_srli_epi64(_mm_add_epi64(b.val, delta2.val), n); + __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 + return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); +} + +template inline +void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a) +{ + uint64 delta = (uint64)1 << (n-1); + v_uint64x2 delta2(delta, delta); + __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline __m128i v_sign_epi64(__m128i a) +{ + return _mm_shuffle_epi32(_mm_srai_epi32(a, 31), _MM_SHUFFLE(3, 3, 1, 1)); // x m0 | x m1 +} + +inline __m128i v_srai_epi64(__m128i a, int imm) +{ + __m128i smask = v_sign_epi64(a); + return _mm_xor_si128(_mm_srli_epi64(_mm_xor_si128(a, smask), imm), smask); +} + +template inline +v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b) +{ + int64 delta = (int64)1 << (n-1); + v_int64x2 delta2(delta, delta); + __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i b1 = v_srai_epi64(_mm_add_epi64(b.val, delta2.val), n); + __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 + return v_int32x4(_mm_unpacklo_epi32(v0, v1)); +} + +template inline +void v_rshr_pack_store(int* ptr, const v_int64x2& a) +{ + int64 delta = (int64)1 << (n-1); + v_int64x2 delta2(delta, delta); + __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); + __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); + __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); + __m128 v3 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(3, 3, 3, 3)), m3.val); + + return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, v3))); +} + + +#define OPENCV_HAL_IMPL_SSE_BIN_OP(bin_op, _Tpvec, intrin) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { \ + return _Tpvec(intrin(a.val, b.val)); \ + } \ + inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ + { \ + a.val = intrin(a.val, b.val); \ + return a; \ + } + +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint8x16, _mm_adds_epu8) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint8x16, _mm_subs_epu8) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int8x16, _mm_adds_epi8) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int8x16, _mm_subs_epi8) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint16x8, _mm_adds_epu16) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint16x8, _mm_subs_epu16) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_uint16x8, _mm_mullo_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int16x8, _mm_adds_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int16x8, _mm_subs_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_int16x8, _mm_mullo_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint32x4, _mm_add_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint32x4, _mm_sub_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int32x4, _mm_add_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int32x4, _mm_sub_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float32x4, _mm_add_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float32x4, _mm_sub_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float32x4, _mm_mul_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float32x4, _mm_div_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float64x2, _mm_add_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float64x2, _mm_sub_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float64x2, _mm_mul_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float64x2, _mm_div_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint64x2, _mm_add_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint64x2, _mm_sub_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int64x2, _mm_add_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int64x2, _mm_sub_epi64) + +inline v_uint32x4 operator * (const v_uint32x4& a, const v_uint32x4& b) +{ + __m128i c0 = _mm_mul_epu32(a.val, b.val); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); + __m128i d0 = _mm_unpacklo_epi32(c0, c1); + __m128i d1 = _mm_unpackhi_epi32(c0, c1); + return v_uint32x4(_mm_unpacklo_epi64(d0, d1)); +} +inline v_int32x4 operator * (const v_int32x4& a, const v_int32x4& b) +{ + __m128i c0 = _mm_mul_epu32(a.val, b.val); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); + __m128i d0 = _mm_unpacklo_epi32(c0, c1); + __m128i d1 = _mm_unpackhi_epi32(c0, c1); + return v_int32x4(_mm_unpacklo_epi64(d0, d1)); +} +inline v_uint32x4& operator *= (v_uint32x4& a, const v_uint32x4& b) +{ + a = a * b; + return a; +} +inline v_int32x4& operator *= (v_int32x4& a, const v_int32x4& b) +{ + a = a * b; + return a; +} + +inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, + v_int32x4& c, v_int32x4& d) +{ + __m128i v0 = _mm_mullo_epi16(a.val, b.val); + __m128i v1 = _mm_mulhi_epi16(a.val, b.val); + c.val = _mm_unpacklo_epi16(v0, v1); + d.val = _mm_unpackhi_epi16(v0, v1); +} + +inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, + v_uint32x4& c, v_uint32x4& d) +{ + __m128i v0 = _mm_mullo_epi16(a.val, b.val); + __m128i v1 = _mm_mulhi_epu16(a.val, b.val); + c.val = _mm_unpacklo_epi16(v0, v1); + d.val = _mm_unpackhi_epi16(v0, v1); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, + v_uint64x2& c, v_uint64x2& d) +{ + __m128i c0 = _mm_mul_epu32(a.val, b.val); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); + c.val = _mm_unpacklo_epi64(c0, c1); + d.val = _mm_unpackhi_epi64(c0, c1); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ + return v_int32x4(_mm_madd_epi16(a.val, b.val)); +} + +#define OPENCV_HAL_IMPL_SSE_LOGIC_OP(_Tpvec, suffix, not_const) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(&, _Tpvec, _mm_and_##suffix) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(|, _Tpvec, _mm_or_##suffix) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(^, _Tpvec, _mm_xor_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { \ + return _Tpvec(_mm_xor_##suffix(a.val, not_const)); \ + } + +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint8x16, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int8x16, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint16x8, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int16x8, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint32x4, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int32x4, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint64x2, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int64x2, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float32x4, ps, _mm_castsi128_ps(_mm_set1_epi32(-1))) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float64x2, pd, _mm_castsi128_pd(_mm_set1_epi32(-1))) + +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ return v_float32x4(_mm_sqrt_ps(x.val)); } + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + static const __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f); + __m128 t = x.val; + __m128 h = _mm_mul_ps(t, _0_5); + t = _mm_rsqrt_ps(t); + t = _mm_mul_ps(t, _mm_sub_ps(_1_5, _mm_mul_ps(_mm_mul_ps(t, t), h))); + return v_float32x4(t); +} + +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ return v_float64x2(_mm_sqrt_pd(x.val)); } + +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ + static const __m128d v_1 = _mm_set1_pd(1.); + return v_float64x2(_mm_div_pd(v_1, _mm_sqrt_pd(x.val))); +} + +#define OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(_Tpuvec, _Tpsvec, func, suffix, subWidth) \ +inline _Tpuvec v_abs(const _Tpsvec& x) \ +{ return _Tpuvec(_mm_##func##_ep##suffix(x.val, _mm_sub_ep##subWidth(_mm_setzero_si128(), x.val))); } + +OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint8x16, v_int8x16, min, u8, i8) +OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint16x8, v_int16x8, max, i16, i16) +inline v_uint32x4 v_abs(const v_int32x4& x) +{ + __m128i s = _mm_srli_epi32(x.val, 31); + __m128i f = _mm_srai_epi32(x.val, 31); + return v_uint32x4(_mm_add_epi32(_mm_xor_si128(x.val, f), s)); +} +inline v_float32x4 v_abs(const v_float32x4& x) +{ return v_float32x4(_mm_and_ps(x.val, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff)))); } +inline v_float64x2 v_abs(const v_float64x2& x) +{ + return v_float64x2(_mm_and_pd(x.val, + _mm_castsi128_pd(_mm_srli_epi64(_mm_set1_epi32(-1), 1)))); +} + +// TODO: exp, log, sin, cos + +#define OPENCV_HAL_IMPL_SSE_BIN_FUNC(_Tpvec, func, intrin) \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_min, _mm_min_epu8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_max, _mm_max_epu8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_min, _mm_min_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_max, _mm_max_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_min, _mm_min_ps) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_max, _mm_max_ps) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_min, _mm_min_pd) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_max, _mm_max_pd) + +inline v_int8x16 v_min(const v_int8x16& a, const v_int8x16& b) +{ + __m128i delta = _mm_set1_epi8((char)-128); + return v_int8x16(_mm_xor_si128(delta, _mm_min_epu8(_mm_xor_si128(a.val, delta), + _mm_xor_si128(b.val, delta)))); +} +inline v_int8x16 v_max(const v_int8x16& a, const v_int8x16& b) +{ + __m128i delta = _mm_set1_epi8((char)-128); + return v_int8x16(_mm_xor_si128(delta, _mm_max_epu8(_mm_xor_si128(a.val, delta), + _mm_xor_si128(b.val, delta)))); +} +inline v_uint16x8 v_min(const v_uint16x8& a, const v_uint16x8& b) +{ + return v_uint16x8(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, b.val))); +} +inline v_uint16x8 v_max(const v_uint16x8& a, const v_uint16x8& b) +{ + return v_uint16x8(_mm_adds_epu16(_mm_subs_epu16(a.val, b.val), b.val)); +} +inline v_uint32x4 v_min(const v_uint32x4& a, const v_uint32x4& b) +{ + __m128i delta = _mm_set1_epi32((int)0x80000000); + __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); + return v_uint32x4(v_select_si128(mask, b.val, a.val)); +} +inline v_uint32x4 v_max(const v_uint32x4& a, const v_uint32x4& b) +{ + __m128i delta = _mm_set1_epi32((int)0x80000000); + __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); + return v_uint32x4(v_select_si128(mask, a.val, b.val)); +} +inline v_int32x4 v_min(const v_int32x4& a, const v_int32x4& b) +{ + return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), b.val, a.val)); +} +inline v_int32x4 v_max(const v_int32x4& a, const v_int32x4& b) +{ + return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), a.val, b.val)); +} + +#define OPENCV_HAL_IMPL_SSE_INT_CMP_OP(_Tpuvec, _Tpsvec, suffix, sbit) \ +inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ +{ return _Tpuvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpuvec operator != (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpuvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ +{ return _Tpsvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpsvec operator != (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpuvec operator < (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask))); \ +} \ +inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask))); \ +} \ +inline _Tpuvec operator <= (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + __m128i not_mask = _mm_set1_epi32(-1); \ + __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask)); \ + return _Tpuvec(_mm_xor_si128(res, not_mask)); \ +} \ +inline _Tpuvec operator >= (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + __m128i not_mask = _mm_set1_epi32(-1); \ + __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask)); \ + return _Tpuvec(_mm_xor_si128(res, not_mask)); \ +} \ +inline _Tpsvec operator < (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + return _Tpsvec(_mm_cmpgt_##suffix(b.val, a.val)); \ +} \ +inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + return _Tpsvec(_mm_cmpgt_##suffix(a.val, b.val)); \ +} \ +inline _Tpsvec operator <= (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpsvec operator >= (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(b.val, a.val), not_mask)); \ +} + +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint8x16, v_int8x16, epi8, (char)-128) +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint16x8, v_int16x8, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint32x4, v_int32x4, epi32, (int)0x80000000) + +#define OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(_Tpvec, suffix) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpneq_##suffix(a.val, b.val)); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmplt_##suffix(a.val, b.val)); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpgt_##suffix(a.val, b.val)); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmple_##suffix(a.val, b.val)); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpge_##suffix(a.val, b.val)); } + +OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float32x4, ps) +OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float64x2, pd) + +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_add_wrap, _mm_add_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_sub_wrap, _mm_sub_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_sub_wrap, _mm_sub_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_sub_wrap, _mm_sub_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_sub_wrap, _mm_sub_epi16) + +#define OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(_Tpuvec, _Tpsvec, bits, smask32) \ +inline _Tpuvec v_absdiff(const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + return _Tpuvec(_mm_add_epi##bits(_mm_subs_epu##bits(a.val, b.val), _mm_subs_epu##bits(b.val, a.val))); \ +} \ +inline _Tpuvec v_absdiff(const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i smask = _mm_set1_epi32(smask32); \ + __m128i a1 = _mm_xor_si128(a.val, smask); \ + __m128i b1 = _mm_xor_si128(b.val, smask); \ + return _Tpuvec(_mm_add_epi##bits(_mm_subs_epu##bits(a1, b1), _mm_subs_epu##bits(b1, a1))); \ +} + +OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(v_uint8x16, v_int8x16, 8, (int)0x80808080) +OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(v_uint16x8, v_int16x8, 16, (int)0x80008000) + +inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b) +{ + return v_max(a, b) - v_min(a, b); +} + +inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) +{ + __m128i d = _mm_sub_epi32(a.val, b.val); + __m128i m = _mm_cmpgt_epi32(b.val, a.val); + return v_uint32x4(_mm_sub_epi32(_mm_xor_si128(d, m), m)); +} + +#define OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(_Tpvec, _Tp, _Tpreg, suffix, absmask_vec) \ +inline _Tpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpreg absmask = _mm_castsi128_##suffix(absmask_vec); \ + return _Tpvec(_mm_and_##suffix(_mm_sub_##suffix(a.val, b.val), absmask)); \ +} \ +inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpreg res = _mm_add_##suffix(_mm_mul_##suffix(a.val, a.val), _mm_mul_##suffix(b.val, b.val)); \ + return _Tpvec(_mm_sqrt_##suffix(res)); \ +} \ +inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpreg res = _mm_add_##suffix(_mm_mul_##suffix(a.val, a.val), _mm_mul_##suffix(b.val, b.val)); \ + return _Tpvec(res); \ +} \ +inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ \ + return _Tpvec(_mm_add_##suffix(_mm_mul_##suffix(a.val, b.val), c.val)); \ +} + +OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float32x4, float, __m128, ps, _mm_set1_epi32((int)0x7fffffff)) +OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float64x2, double, __m128d, pd, _mm_srli_epi64(_mm_set1_epi32(-1), 1)) + +#define OPENCV_HAL_IMPL_SSE_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ +inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ +{ \ + return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ +{ \ + return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ +{ \ + return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ +} \ +inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ +{ \ + return _Tpsvec(srai(a.val, imm)); \ +} \ +template \ +inline _Tpuvec v_shl(const _Tpuvec& a) \ +{ \ + return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpsvec v_shl(const _Tpsvec& a) \ +{ \ + return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpuvec v_shr(const _Tpuvec& a) \ +{ \ + return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpsvec v_shr(const _Tpsvec& a) \ +{ \ + return _Tpsvec(srai(a.val, imm)); \ +} + +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint16x8, v_int16x8, epi16, _mm_srai_epi16) +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint32x4, v_int32x4, epi32, _mm_srai_epi32) +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint64x2, v_int64x2, epi64, v_srai_epi64) + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(_Tpvec, _Tp) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadu_si128((const __m128i*)ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(_mm_load_si128((const __m128i*)ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ \ + return _Tpvec(_mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ + _mm_loadl_epi64((const __m128i*)ptr1))); \ +} \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storeu_si128((__m128i*)ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ _mm_store_si128((__m128i*)ptr, a.val); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, a.val); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a.val, a.val)); } + +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint8x16, uchar) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int8x16, schar) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint16x8, ushort) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int16x8, short) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int32x4, int) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint64x2, uint64) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int64x2, int64) + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadu_##suffix(ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(_mm_load_##suffix(ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ \ + return _Tpvec(_mm_castsi128_##suffix( \ + _mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ + _mm_loadl_epi64((const __m128i*)ptr1)))); \ +} \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storeu_##suffix(ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ _mm_store_##suffix(ptr, a.val); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, _mm_cast##suffix##_si128(a.val)); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ \ + __m128i a1 = _mm_cast##suffix##_si128(a.val); \ + _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a1, a1)); \ +} + +OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps) +OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd) + +#if defined(HAVE_FP16) +inline v_float16x4 v_load_f16(const short* ptr) +{ return v_float16x4(_mm_loadl_epi64((const __m128i*)ptr)); } +inline void v_store_f16(short* ptr, v_float16x4& a) +{ _mm_storel_epi64((__m128i*)ptr, a.val); } +#endif + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(_Tpvec, scalartype, func, suffix, sbit) \ +inline scalartype v_reduce_##func(const v_##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ + return (scalartype)_mm_cvtsi128_si32(val); \ +} \ +inline unsigned scalartype v_reduce_##func(const v_u##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + __m128i smask = _mm_set1_epi16(sbit); \ + val = _mm_xor_si128(val, smask); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ + return (unsigned scalartype)(_mm_cvtsi128_si32(val) ^ sbit); \ +} +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(_Tpvec, scalartype, suffix) \ +inline scalartype v_reduce_sum(const v_##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 8)); \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 4)); \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 2)); \ + return (scalartype)_mm_cvtsi128_si32(val); \ +} \ +inline unsigned scalartype v_reduce_sum(const v_u##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 8)); \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 4)); \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 2)); \ + return (unsigned scalartype)_mm_cvtsi128_si32(val); \ +} +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, max, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, min, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(int16x8, short, 16) + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + scalartype CV_DECL_ALIGNED(16) buf[4]; \ + v_store_aligned(buf, a); \ + scalartype s0 = scalar_func(buf[0], buf[1]); \ + scalartype s1 = scalar_func(buf[2], buf[3]); \ + return scalar_func(s0, s1); \ +} + +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, sum, OPENCV_HAL_ADD) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, sum, OPENCV_HAL_ADD) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, min, std::min) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, sum, OPENCV_HAL_ADD) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, min, std::min) + +#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(_Tpvec, suffix, pack_op, and_op, signmask, allmask) \ +inline int v_signmask(const _Tpvec& a) \ +{ \ + return and_op(_mm_movemask_##suffix(pack_op(a.val)), signmask); \ +} \ +inline bool v_check_all(const _Tpvec& a) \ +{ return and_op(_mm_movemask_##suffix(a.val), allmask) == allmask; } \ +inline bool v_check_any(const _Tpvec& a) \ +{ return and_op(_mm_movemask_##suffix(a.val), allmask) != 0; } + +#define OPENCV_HAL_PACKS(a) _mm_packs_epi16(a, a) +inline __m128i v_packq_epi32(__m128i a) +{ + __m128i b = _mm_packs_epi32(a, a); + return _mm_packs_epi16(b, b); +} + +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float32x4, ps, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 15, 15) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float64x2, pd, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 3, 3) + +#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, suffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(_mm_xor_##suffix(b.val, _mm_and_##suffix(_mm_xor_##suffix(b.val, a.val), mask.val))); \ +} + +OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, si128) +// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, si128) +// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, pd) + +#define OPENCV_HAL_IMPL_SSE_EXPAND(_Tpuvec, _Tpwuvec, _Tpu, _Tpsvec, _Tpwsvec, _Tps, suffix, wsuffix, shift) \ +inline void v_expand(const _Tpuvec& a, _Tpwuvec& b0, _Tpwuvec& b1) \ +{ \ + __m128i z = _mm_setzero_si128(); \ + b0.val = _mm_unpacklo_##suffix(a.val, z); \ + b1.val = _mm_unpackhi_##suffix(a.val, z); \ +} \ +inline _Tpwuvec v_load_expand(const _Tpu* ptr) \ +{ \ + __m128i z = _mm_setzero_si128(); \ + return _Tpwuvec(_mm_unpacklo_##suffix(_mm_loadl_epi64((const __m128i*)ptr), z)); \ +} \ +inline void v_expand(const _Tpsvec& a, _Tpwsvec& b0, _Tpwsvec& b1) \ +{ \ + b0.val = _mm_srai_##wsuffix(_mm_unpacklo_##suffix(a.val, a.val), shift); \ + b1.val = _mm_srai_##wsuffix(_mm_unpackhi_##suffix(a.val, a.val), shift); \ +} \ +inline _Tpwsvec v_load_expand(const _Tps* ptr) \ +{ \ + __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ + return _Tpwsvec(_mm_srai_##wsuffix(_mm_unpacklo_##suffix(a, a), shift)); \ +} + +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint8x16, v_uint16x8, uchar, v_int8x16, v_int16x8, schar, epi8, epi16, 8) +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint16x8, v_uint32x4, ushort, v_int16x8, v_int32x4, short, epi16, epi32, 16) + +inline void v_expand(const v_uint32x4& a, v_uint64x2& b0, v_uint64x2& b1) +{ + __m128i z = _mm_setzero_si128(); + b0.val = _mm_unpacklo_epi32(a.val, z); + b1.val = _mm_unpackhi_epi32(a.val, z); +} +inline v_uint64x2 v_load_expand(const unsigned* ptr) +{ + __m128i z = _mm_setzero_si128(); + return v_uint64x2(_mm_unpacklo_epi32(_mm_loadl_epi64((const __m128i*)ptr), z)); +} +inline void v_expand(const v_int32x4& a, v_int64x2& b0, v_int64x2& b1) +{ + __m128i s = _mm_srai_epi32(a.val, 31); + b0.val = _mm_unpacklo_epi32(a.val, s); + b1.val = _mm_unpackhi_epi32(a.val, s); +} +inline v_int64x2 v_load_expand(const int* ptr) +{ + __m128i a = _mm_loadl_epi64((const __m128i*)ptr); + __m128i s = _mm_srai_epi32(a, 31); + return v_int64x2(_mm_unpacklo_epi32(a, s)); +} + +inline v_uint32x4 v_load_expand_q(const uchar* ptr) +{ + __m128i z = _mm_setzero_si128(); + __m128i a = _mm_cvtsi32_si128(*(const int*)ptr); + return v_uint32x4(_mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z)); +} + +inline v_int32x4 v_load_expand_q(const schar* ptr) +{ + __m128i a = _mm_cvtsi32_si128(*(const int*)ptr); + a = _mm_unpacklo_epi8(a, a); + a = _mm_unpacklo_epi8(a, a); + return v_int32x4(_mm_srai_epi32(a, 24)); +} + +#define OPENCV_HAL_IMPL_SSE_UNPACKS(_Tpvec, suffix, cast_from, cast_to) \ +inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \ +{ \ + b0.val = _mm_unpacklo_##suffix(a0.val, a1.val); \ + b1.val = _mm_unpackhi_##suffix(a0.val, a1.val); \ +} \ +inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + return _Tpvec(cast_to(_mm_unpacklo_epi64(a1, b1))); \ +} \ +inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + return _Tpvec(cast_to(_mm_unpackhi_epi64(a1, b1))); \ +} \ +inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + c.val = cast_to(_mm_unpacklo_epi64(a1, b1)); \ + d.val = cast_to(_mm_unpackhi_epi64(a1, b1)); \ +} + +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_float64x2, pd, _mm_castpd_si128, _mm_castsi128_pd) + +template +inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) +{ + const int w = sizeof(typename _Tpvec::lane_type); + const int n = _Tpvec::nlanes; + __m128i ra, rb; + ra = _mm_srli_si128(a.val, s*w); + rb = _mm_slli_si128(b.val, (n-s)*w); + return _Tpvec(_mm_or_si128(ra, rb)); +} + +inline v_int32x4 v_round(const v_float32x4& a) +{ return v_int32x4(_mm_cvtps_epi32(a.val)); } + +inline v_int32x4 v_floor(const v_float32x4& a) +{ + __m128i a1 = _mm_cvtps_epi32(a.val); + __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(_mm_cvtepi32_ps(a1), a.val)); + return v_int32x4(_mm_add_epi32(a1, mask)); +} + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ + __m128i a1 = _mm_cvtps_epi32(a.val); + __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(a.val, _mm_cvtepi32_ps(a1))); + return v_int32x4(_mm_sub_epi32(a1, mask)); +} + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(_mm_cvttps_epi32(a.val)); } + +inline v_int32x4 v_round(const v_float64x2& a) +{ return v_int32x4(_mm_cvtpd_epi32(a.val)); } + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + __m128i a1 = _mm_cvtpd_epi32(a.val); + __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(_mm_cvtepi32_pd(a1), a.val)); + mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 + return v_int32x4(_mm_add_epi32(a1, mask)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + __m128i a1 = _mm_cvtpd_epi32(a.val); + __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(a.val, _mm_cvtepi32_pd(a1))); + mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 + return v_int32x4(_mm_sub_epi32(a1, mask)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ return v_int32x4(_mm_cvttpd_epi32(a.val)); } + +#define OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ +inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, \ + _Tpvec& b2, _Tpvec& b3) \ +{ \ + __m128i t0 = cast_from(_mm_unpacklo_##suffix(a0.val, a1.val)); \ + __m128i t1 = cast_from(_mm_unpacklo_##suffix(a2.val, a3.val)); \ + __m128i t2 = cast_from(_mm_unpackhi_##suffix(a0.val, a1.val)); \ + __m128i t3 = cast_from(_mm_unpackhi_##suffix(a2.val, a3.val)); \ +\ + b0.val = cast_to(_mm_unpacklo_epi64(t0, t1)); \ + b1.val = cast_to(_mm_unpackhi_epi64(t0, t1)); \ + b2.val = cast_to(_mm_unpacklo_epi64(t2, t3)); \ + b3.val = cast_to(_mm_unpackhi_epi64(t2, t3)); \ +} + +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) + +// adopted from sse_utils.hpp +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + + __m128i t10 = _mm_unpacklo_epi8(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi8(t01, _mm_unpackhi_epi64(t02, t02)); + + __m128i t20 = _mm_unpacklo_epi8(t10, _mm_unpackhi_epi64(t11, t11)); + __m128i t21 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t10, t10), t12); + __m128i t22 = _mm_unpacklo_epi8(t11, _mm_unpackhi_epi64(t12, t12)); + + __m128i t30 = _mm_unpacklo_epi8(t20, _mm_unpackhi_epi64(t21, t21)); + __m128i t31 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t20, t20), t22); + __m128i t32 = _mm_unpacklo_epi8(t21, _mm_unpackhi_epi64(t22, t22)); + + a.val = _mm_unpacklo_epi8(t30, _mm_unpackhi_epi64(t31, t31)); + b.val = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t30, t30), t32); + c.val = _mm_unpacklo_epi8(t31, _mm_unpackhi_epi64(t32, t32)); +} + +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d) +{ + __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ... + __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... + __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); // a8 b8 c8 d8 ... + __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 48)); // a12 b12 c12 d12 ... + + __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 a8 b0 b8 ... + __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a2 a10 b2 b10 ... + __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a4 a12 b4 b12 ... + __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a6 a14 b6 b14 ... + + u0 = _mm_unpacklo_epi8(v0, v2); // a0 a4 a8 a12 ... + u1 = _mm_unpacklo_epi8(v1, v3); // a2 a6 a10 a14 ... + u2 = _mm_unpackhi_epi8(v0, v2); // a1 a5 a9 a13 ... + u3 = _mm_unpackhi_epi8(v1, v3); // a3 a7 a11 a15 ... + + v0 = _mm_unpacklo_epi8(u0, u1); // a0 a2 a4 a6 ... + v1 = _mm_unpacklo_epi8(u2, u3); // a1 a3 a5 a7 ... + v2 = _mm_unpackhi_epi8(u0, u1); // c0 c2 c4 c6 ... + v3 = _mm_unpackhi_epi8(u2, u3); // c1 c3 c5 c7 ... + + a.val = _mm_unpacklo_epi8(v0, v1); + b.val = _mm_unpackhi_epi8(v0, v1); + c.val = _mm_unpacklo_epi8(v2, v3); + d.val = _mm_unpackhi_epi8(v2, v3); +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 8)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + + __m128i t10 = _mm_unpacklo_epi16(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi16(t01, _mm_unpackhi_epi64(t02, t02)); + + __m128i t20 = _mm_unpacklo_epi16(t10, _mm_unpackhi_epi64(t11, t11)); + __m128i t21 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t10, t10), t12); + __m128i t22 = _mm_unpacklo_epi16(t11, _mm_unpackhi_epi64(t12, t12)); + + a.val = _mm_unpacklo_epi16(t20, _mm_unpackhi_epi64(t21, t21)); + b.val = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t20, t20), t22); + c.val = _mm_unpacklo_epi16(t21, _mm_unpackhi_epi64(t22, t22)); +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d) +{ + __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 + __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 8)); // a2 b2 c2 d2 ... + __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... + __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 24)); // a6 b6 c6 d6 ... + + __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 a4 b0 b4 ... + __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a1 a5 b1 b5 ... + __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a2 a6 b2 b6 ... + __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a3 a7 b3 b7 ... + + u0 = _mm_unpacklo_epi16(v0, v2); // a0 a2 a4 a6 ... + u1 = _mm_unpacklo_epi16(v1, v3); // a1 a3 a5 a7 ... + u2 = _mm_unpackhi_epi16(v0, v2); // c0 c2 c4 c6 ... + u3 = _mm_unpackhi_epi16(v1, v3); // c1 c3 c5 c7 ... + + a.val = _mm_unpacklo_epi16(u0, u1); + b.val = _mm_unpackhi_epi16(u0, u1); + c.val = _mm_unpacklo_epi16(u2, u3); + d.val = _mm_unpackhi_epi16(u2, u3); +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 4)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 8)); + + __m128i t10 = _mm_unpacklo_epi32(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi32(t01, _mm_unpackhi_epi64(t02, t02)); + + a.val = _mm_unpacklo_epi32(t10, _mm_unpackhi_epi64(t11, t11)); + b.val = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t10, t10), t12); + c.val = _mm_unpacklo_epi32(t11, _mm_unpackhi_epi64(t12, t12)); +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d) +{ + v_uint32x4 u0(_mm_loadu_si128((const __m128i*)ptr)); // a0 b0 c0 d0 + v_uint32x4 u1(_mm_loadu_si128((const __m128i*)(ptr + 4))); // a1 b1 c1 d1 + v_uint32x4 u2(_mm_loadu_si128((const __m128i*)(ptr + 8))); // a2 b2 c2 d2 + v_uint32x4 u3(_mm_loadu_si128((const __m128i*)(ptr + 12))); // a3 b3 c3 d3 + + v_transpose4x4(u0, u1, u2, u3, a, b, c, d); +} + +// 2-channel, float only +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b) +{ + const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1); + + __m128 u0 = _mm_loadu_ps(ptr); // a0 b0 a1 b1 + __m128 u1 = _mm_loadu_ps((ptr + 4)); // a2 b2 a3 b3 + + a.val = _mm_shuffle_ps(u0, u1, mask_lo); // a0 a1 a2 a3 + b.val = _mm_shuffle_ps(u0, u1, mask_hi); // b0 b1 ab b3 +} + +inline void v_store_interleave( short* ptr, const v_int16x8& a, const v_int16x8& b ) +{ + __m128i t0, t1; + t0 = _mm_unpacklo_epi16(a.val, b.val); + t1 = _mm_unpackhi_epi16(a.val, b.val); + _mm_storeu_si128((__m128i*)(ptr), t0); + _mm_storeu_si128((__m128i*)(ptr + 8), t1); +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + const v_uint8x16& c ) +{ + __m128i z = _mm_setzero_si128(); + __m128i ab0 = _mm_unpacklo_epi8(a.val, b.val); + __m128i ab1 = _mm_unpackhi_epi8(a.val, b.val); + __m128i c0 = _mm_unpacklo_epi8(c.val, z); + __m128i c1 = _mm_unpackhi_epi8(c.val, z); + + __m128i p00 = _mm_unpacklo_epi16(ab0, c0); + __m128i p01 = _mm_unpackhi_epi16(ab0, c0); + __m128i p02 = _mm_unpacklo_epi16(ab1, c1); + __m128i p03 = _mm_unpackhi_epi16(ab1, c1); + + __m128i p10 = _mm_unpacklo_epi32(p00, p01); + __m128i p11 = _mm_unpackhi_epi32(p00, p01); + __m128i p12 = _mm_unpacklo_epi32(p02, p03); + __m128i p13 = _mm_unpackhi_epi32(p02, p03); + + __m128i p20 = _mm_unpacklo_epi64(p10, p11); + __m128i p21 = _mm_unpackhi_epi64(p10, p11); + __m128i p22 = _mm_unpacklo_epi64(p12, p13); + __m128i p23 = _mm_unpackhi_epi64(p12, p13); + + p20 = _mm_slli_si128(p20, 1); + p22 = _mm_slli_si128(p22, 1); + + __m128i p30 = _mm_slli_epi64(_mm_unpacklo_epi32(p20, p21), 8); + __m128i p31 = _mm_srli_epi64(_mm_unpackhi_epi32(p20, p21), 8); + __m128i p32 = _mm_slli_epi64(_mm_unpacklo_epi32(p22, p23), 8); + __m128i p33 = _mm_srli_epi64(_mm_unpackhi_epi32(p22, p23), 8); + + __m128i p40 = _mm_unpacklo_epi64(p30, p31); + __m128i p41 = _mm_unpackhi_epi64(p30, p31); + __m128i p42 = _mm_unpacklo_epi64(p32, p33); + __m128i p43 = _mm_unpackhi_epi64(p32, p33); + + __m128i v0 = _mm_or_si128(_mm_srli_si128(p40, 2), _mm_slli_si128(p41, 10)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(p41, 6), _mm_slli_si128(p42, 6)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(p42, 10), _mm_slli_si128(p43, 2)); + + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + _mm_storeu_si128((__m128i*)(ptr + 32), v2); +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + const v_uint8x16& c, const v_uint8x16& d) +{ + // a0 a1 a2 a3 .... + // b0 b1 b2 b3 .... + // c0 c1 c2 c3 .... + // d0 d1 d2 d3 .... + __m128i u0 = _mm_unpacklo_epi8(a.val, c.val); // a0 c0 a1 c1 ... + __m128i u1 = _mm_unpackhi_epi8(a.val, c.val); // a8 c8 a9 c9 ... + __m128i u2 = _mm_unpacklo_epi8(b.val, d.val); // b0 d0 b1 d1 ... + __m128i u3 = _mm_unpackhi_epi8(b.val, d.val); // b8 d8 b9 d9 ... + + __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 b0 c0 d0 ... + __m128i v1 = _mm_unpacklo_epi8(u1, u3); // a8 b8 c8 d8 ... + __m128i v2 = _mm_unpackhi_epi8(u0, u2); // a4 b4 c4 d4 ... + __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a12 b12 c12 d12 ... + + _mm_storeu_si128((__m128i*)ptr, v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v2); + _mm_storeu_si128((__m128i*)(ptr + 32), v1); + _mm_storeu_si128((__m128i*)(ptr + 48), v3); +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, + const v_uint16x8& b, + const v_uint16x8& c ) +{ + __m128i z = _mm_setzero_si128(); + __m128i ab0 = _mm_unpacklo_epi16(a.val, b.val); + __m128i ab1 = _mm_unpackhi_epi16(a.val, b.val); + __m128i c0 = _mm_unpacklo_epi16(c.val, z); + __m128i c1 = _mm_unpackhi_epi16(c.val, z); + + __m128i p10 = _mm_unpacklo_epi32(ab0, c0); + __m128i p11 = _mm_unpackhi_epi32(ab0, c0); + __m128i p12 = _mm_unpacklo_epi32(ab1, c1); + __m128i p13 = _mm_unpackhi_epi32(ab1, c1); + + __m128i p20 = _mm_unpacklo_epi64(p10, p11); + __m128i p21 = _mm_unpackhi_epi64(p10, p11); + __m128i p22 = _mm_unpacklo_epi64(p12, p13); + __m128i p23 = _mm_unpackhi_epi64(p12, p13); + + p20 = _mm_slli_si128(p20, 2); + p22 = _mm_slli_si128(p22, 2); + + __m128i p30 = _mm_unpacklo_epi64(p20, p21); + __m128i p31 = _mm_unpackhi_epi64(p20, p21); + __m128i p32 = _mm_unpacklo_epi64(p22, p23); + __m128i p33 = _mm_unpackhi_epi64(p22, p23); + + __m128i v0 = _mm_or_si128(_mm_srli_si128(p30, 2), _mm_slli_si128(p31, 10)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(p31, 6), _mm_slli_si128(p32, 6)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(p32, 10), _mm_slli_si128(p33, 2)); + + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + _mm_storeu_si128((__m128i*)(ptr + 16), v2); +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, + const v_uint16x8& c, const v_uint16x8& d) +{ + // a0 a1 a2 a3 .... + // b0 b1 b2 b3 .... + // c0 c1 c2 c3 .... + // d0 d1 d2 d3 .... + __m128i u0 = _mm_unpacklo_epi16(a.val, c.val); // a0 c0 a1 c1 ... + __m128i u1 = _mm_unpackhi_epi16(a.val, c.val); // a4 c4 a5 c5 ... + __m128i u2 = _mm_unpacklo_epi16(b.val, d.val); // b0 d0 b1 d1 ... + __m128i u3 = _mm_unpackhi_epi16(b.val, d.val); // b4 d4 b5 d5 ... + + __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 b0 c0 d0 ... + __m128i v1 = _mm_unpacklo_epi16(u1, u3); // a4 b4 c4 d4 ... + __m128i v2 = _mm_unpackhi_epi16(u0, u2); // a2 b2 c2 d2 ... + __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a6 b6 c6 d6 ... + + _mm_storeu_si128((__m128i*)ptr, v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v2); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + _mm_storeu_si128((__m128i*)(ptr + 24), v3); +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c ) +{ + v_uint32x4 z = v_setzero_u32(), u0, u1, u2, u3; + v_transpose4x4(a, b, c, z, u0, u1, u2, u3); + + __m128i v0 = _mm_or_si128(u0.val, _mm_slli_si128(u1.val, 12)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(u1.val, 4), _mm_slli_si128(u2.val, 8)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(u2.val, 8), _mm_slli_si128(u3.val, 4)); + + _mm_storeu_si128((__m128i*)ptr, v0); + _mm_storeu_si128((__m128i*)(ptr + 4), v1); + _mm_storeu_si128((__m128i*)(ptr + 8), v2); +} + +inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + v_uint32x4 t0, t1, t2, t3; + v_transpose4x4(a, b, c, d, t0, t1, t2, t3); + v_store(ptr, t0); + v_store(ptr + 4, t1); + v_store(ptr + 8, t2); + v_store(ptr + 12, t3); +} + +// 2-channel, float only +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b) +{ + // a0 a1 a2 a3 ... + // b0 b1 b2 b3 ... + __m128 u0 = _mm_unpacklo_ps(a.val, b.val); // a0 b0 a1 b1 + __m128 u1 = _mm_unpackhi_ps(a.val, b.val); // a2 b2 a3 b3 + + _mm_storeu_ps(ptr, u0); + _mm_storeu_ps((ptr + 4), u1); +} + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(_Tpvec, _Tp, suffix, _Tpuvec, _Tpu, usuffix) \ +inline void v_load_deinterleave( const _Tp* ptr, _Tpvec& a0, \ + _Tpvec& b0, _Tpvec& c0 ) \ +{ \ + _Tpuvec a1, b1, c1; \ + v_load_deinterleave((const _Tpu*)ptr, a1, b1, c1); \ + a0 = v_reinterpret_as_##suffix(a1); \ + b0 = v_reinterpret_as_##suffix(b1); \ + c0 = v_reinterpret_as_##suffix(c1); \ +} \ +inline void v_load_deinterleave( const _Tp* ptr, _Tpvec& a0, \ + _Tpvec& b0, _Tpvec& c0, _Tpvec& d0 ) \ +{ \ + _Tpuvec a1, b1, c1, d1; \ + v_load_deinterleave((const _Tpu*)ptr, a1, b1, c1, d1); \ + a0 = v_reinterpret_as_##suffix(a1); \ + b0 = v_reinterpret_as_##suffix(b1); \ + c0 = v_reinterpret_as_##suffix(c1); \ + d0 = v_reinterpret_as_##suffix(d1); \ +} \ +inline void v_store_interleave( _Tp* ptr, const _Tpvec& a0, \ + const _Tpvec& b0, const _Tpvec& c0 ) \ +{ \ + _Tpuvec a1 = v_reinterpret_as_##usuffix(a0); \ + _Tpuvec b1 = v_reinterpret_as_##usuffix(b0); \ + _Tpuvec c1 = v_reinterpret_as_##usuffix(c0); \ + v_store_interleave((_Tpu*)ptr, a1, b1, c1); \ +} \ +inline void v_store_interleave( _Tp* ptr, const _Tpvec& a0, const _Tpvec& b0, \ + const _Tpvec& c0, const _Tpvec& d0 ) \ +{ \ + _Tpuvec a1 = v_reinterpret_as_##usuffix(a0); \ + _Tpuvec b1 = v_reinterpret_as_##usuffix(b0); \ + _Tpuvec c1 = v_reinterpret_as_##usuffix(c0); \ + _Tpuvec d1 = v_reinterpret_as_##usuffix(d0); \ + v_store_interleave((_Tpu*)ptr, a1, b1, c1, d1); \ +} + +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_float32x4, float, f32, v_uint32x4, unsigned, u32) + +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ + return v_float32x4(_mm_cvtepi32_ps(a.val)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + return v_float32x4(_mm_cvtpd_ps(a.val)); +} + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ + return v_float64x2(_mm_cvtepi32_pd(a.val)); +} + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ + return v_float64x2(_mm_cvtepi32_pd(_mm_srli_si128(a.val,8))); +} + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ + return v_float64x2(_mm_cvtps_pd(a.val)); +} + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ + return v_float64x2(_mm_cvtps_pd(_mm_castsi128_ps(_mm_srli_si128(_mm_castps_si128(a.val),8)))); +} + +#if defined(HAVE_FP16) +inline v_float32x4 v_cvt_f32(const v_float16x4& a) +{ + return v_float32x4(_mm_cvtph_ps(a.val)); +} + +inline v_float16x4 v_cvt_f16(const v_float32x4& a) +{ + return v_float16x4(_mm_cvtps_ph(a.val, 0)); +} +#endif + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return checkHardwareSupport(CV_CPU_SSE2); +} + +//! @} + +//! @endcond + +} + +#endif diff --git a/thirdparty/linux/include/opencv2/core/ippasync.hpp b/thirdparty/linux/include/opencv2/core/ippasync.hpp new file mode 100644 index 0000000..0ed8264 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/ippasync.hpp @@ -0,0 +1,195 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_IPPASYNC_HPP +#define OPENCV_CORE_IPPASYNC_HPP + +#ifdef HAVE_IPP_A + +#include "opencv2/core.hpp" +#include +#include + +namespace cv +{ + +namespace hpp +{ + +/** @addtogroup core_ipp +This section describes conversion between OpenCV and [Intel® IPP Asynchronous +C/C++](http://software.intel.com/en-us/intel-ipp-preview) library. [Getting Started +Guide](http://registrationcenter.intel.com/irc_nas/3727/ipp_async_get_started.htm) help you to +install the library, configure header and library build paths. + */ +//! @{ + + //! convert OpenCV data type to hppDataType + inline int toHppType(const int cvType) + { + int depth = CV_MAT_DEPTH(cvType); + int hppType = depth == CV_8U ? HPP_DATA_TYPE_8U : + depth == CV_16U ? HPP_DATA_TYPE_16U : + depth == CV_16S ? HPP_DATA_TYPE_16S : + depth == CV_32S ? HPP_DATA_TYPE_32S : + depth == CV_32F ? HPP_DATA_TYPE_32F : + depth == CV_64F ? HPP_DATA_TYPE_64F : -1; + CV_Assert( hppType >= 0 ); + return hppType; + } + + //! convert hppDataType to OpenCV data type + inline int toCvType(const int hppType) + { + int cvType = hppType == HPP_DATA_TYPE_8U ? CV_8U : + hppType == HPP_DATA_TYPE_16U ? CV_16U : + hppType == HPP_DATA_TYPE_16S ? CV_16S : + hppType == HPP_DATA_TYPE_32S ? CV_32S : + hppType == HPP_DATA_TYPE_32F ? CV_32F : + hppType == HPP_DATA_TYPE_64F ? CV_64F : -1; + CV_Assert( cvType >= 0 ); + return cvType; + } + + /** @brief Convert hppiMatrix to Mat. + + This function allocates and initializes new matrix (if needed) that has the same size and type as + input matrix. Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + @param src input hppiMatrix. + @param dst output matrix. + @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). + @param cn number of channels. + */ + inline void copyHppToMat(hppiMatrix* src, Mat& dst, hppAccel accel, int cn) + { + hppDataType type; + hpp32u width, height; + hppStatus sts; + + if (src == NULL) + return dst.release(); + + sts = hppiInquireMatrix(src, &type, &width, &height); + + CV_Assert( sts == HPP_STATUS_NO_ERROR); + + int matType = CV_MAKETYPE(toCvType(type), cn); + + CV_Assert(width%cn == 0); + + width /= cn; + + dst.create((int)height, (int)width, (int)matType); + + size_t newSize = (size_t)(height*(hpp32u)(dst.step)); + + sts = hppiGetMatrixData(accel,src,(hpp32u)(dst.step),dst.data,&newSize); + + CV_Assert( sts == HPP_STATUS_NO_ERROR); + } + + /** @brief Create Mat from hppiMatrix. + + This function allocates and initializes the Mat that has the same size and type as input matrix. + Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + @param src input hppiMatrix. + @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). + @param cn number of channels. + @sa howToUseIPPAconversion, hpp::copyHppToMat, hpp::getHpp. + */ + inline Mat getMat(hppiMatrix* src, hppAccel accel, int cn) + { + Mat dst; + copyHppToMat(src, dst, accel, cn); + return dst; + } + + /** @brief Create hppiMatrix from Mat. + + This function allocates and initializes the hppiMatrix that has the same size and type as input + matrix, returns the hppiMatrix*. + + If you want to use zero-copy for GPU you should to have 4KB aligned matrix data. See details + [hppiCreateSharedMatrix](http://software.intel.com/ru-ru/node/501697). + + Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + + @note The hppiMatrix pointer to the image buffer in system memory refers to the src.data. Control + the lifetime of the matrix and don't change its data, if there is no special need. + @param src input matrix. + @param accel accelerator instance. Supports type: + - **HPP_ACCEL_TYPE_CPU** - accelerated by optimized CPU instructions. + - **HPP_ACCEL_TYPE_GPU** - accelerated by GPU programmable units or fixed-function + accelerators. + - **HPP_ACCEL_TYPE_ANY** - any acceleration or no acceleration available. + @sa howToUseIPPAconversion, hpp::getMat + */ + inline hppiMatrix* getHpp(const Mat& src, hppAccel accel) + { + int htype = toHppType(src.type()); + int cn = src.channels(); + + CV_Assert(src.data); + hppAccelType accelType = hppQueryAccelType(accel); + + if (accelType!=HPP_ACCEL_TYPE_CPU) + { + hpp32u pitch, size; + hppQueryMatrixAllocParams(accel, src.cols*cn, src.rows, htype, &pitch, &size); + if (pitch!=0 && size!=0) + if ((int)(src.data)%4096==0 && pitch==(hpp32u)(src.step)) + { + return hppiCreateSharedMatrix(htype, src.cols*cn, src.rows, src.data, pitch, size); + } + } + + return hppiCreateMatrix(htype, src.cols*cn, src.rows, src.data, (hpp32s)(src.step));; + } + +//! @} +}} + +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/core/mat.hpp b/thirdparty/linux/include/opencv2/core/mat.hpp new file mode 100644 index 0000000..39c197e --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/mat.hpp @@ -0,0 +1,3520 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MAT_HPP +#define OPENCV_CORE_MAT_HPP + +#ifndef __cplusplus +# error mat.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/matx.hpp" +#include "opencv2/core/types.hpp" + +#include "opencv2/core/bufferpool.hpp" + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +enum { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25, + ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 }; + +class CV_EXPORTS _OutputArray; + +//////////////////////// Input/Output Array Arguments ///////////////////////////////// + +/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions. + +It is defined as: +@code + typedef const _InputArray& InputArray; +@endcode +where _InputArray is a class that can be constructed from `Mat`, `Mat_`, `Matx`, +`std::vector`, `std::vector >` or `std::vector`. It can also be constructed +from a matrix expression. + +Since this is mostly implementation-level class, and its interface may change in future versions, we +do not describe it in details. There are a few key things, though, that should be kept in mind: + +- When you see in the reference manual or in OpenCV source code a function that takes + InputArray, it means that you can actually pass `Mat`, `Matx`, `vector` etc. (see above the + complete list). +- Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or + simply cv::Mat() as you probably did before). +- The class is designed solely for passing parameters. That is, normally you *should not* + declare class members, local and global variables of this type. +- If you want to design your own function or a class method that can operate of arrays of + multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside + a function you should use _InputArray::getMat() method to construct a matrix header for the + array (without copying data). _InputArray::kind() can be used to distinguish Mat from + `vector<>` etc., but normally it is not needed. + +Here is how you can use a function that takes InputArray : +@code + std::vector vec; + // points or a circle + for( int i = 0; i < 30; i++ ) + vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)), + (float)(100 - 30*sin(i*CV_PI*2/5)))); + cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20)); +@endcode +That is, we form an STL vector containing points, and apply in-place affine transformation to the +vector using the 2x3 matrix created inline as `Matx` instance. + +Here is how such a function can be implemented (for simplicity, we implement a very specific case of +it, according to the assertion statement inside) : +@code + void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m) + { + // get Mat headers for input arrays. This is O(1) operation, + // unless _src and/or _m are matrix expressions. + Mat src = _src.getMat(), m = _m.getMat(); + CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) ); + + // [re]create the output array so that it has the proper size and type. + // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize. + _dst.create(src.size(), src.type()); + Mat dst = _dst.getMat(); + + for( int i = 0; i < src.rows; i++ ) + for( int j = 0; j < src.cols; j++ ) + { + Point2f pt = src.at(i, j); + dst.at(i, j) = Point2f(m.at(0, 0)*pt.x + + m.at(0, 1)*pt.y + + m.at(0, 2), + m.at(1, 0)*pt.x + + m.at(1, 1)*pt.y + + m.at(1, 2)); + } + } +@endcode +There is another related type, InputArrayOfArrays, which is currently defined as a synonym for +InputArray: +@code + typedef InputArray InputArrayOfArrays; +@endcode +It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate +synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation +level their use is similar, but _InputArray::getMat(idx) should be used to get header for the +idx-th component of the outer vector and _InputArray::size().area() should be used to find the +number of components (vectors/matrices) of the outer vector. + */ +class CV_EXPORTS _InputArray +{ +public: + enum { + KIND_SHIFT = 16, + FIXED_TYPE = 0x8000 << KIND_SHIFT, + FIXED_SIZE = 0x4000 << KIND_SHIFT, + KIND_MASK = 31 << KIND_SHIFT, + + NONE = 0 << KIND_SHIFT, + MAT = 1 << KIND_SHIFT, + MATX = 2 << KIND_SHIFT, + STD_VECTOR = 3 << KIND_SHIFT, + STD_VECTOR_VECTOR = 4 << KIND_SHIFT, + STD_VECTOR_MAT = 5 << KIND_SHIFT, + EXPR = 6 << KIND_SHIFT, + OPENGL_BUFFER = 7 << KIND_SHIFT, + CUDA_HOST_MEM = 8 << KIND_SHIFT, + CUDA_GPU_MAT = 9 << KIND_SHIFT, + UMAT =10 << KIND_SHIFT, + STD_VECTOR_UMAT =11 << KIND_SHIFT, + STD_BOOL_VECTOR =12 << KIND_SHIFT, + STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT + }; + + _InputArray(); + _InputArray(int _flags, void* _obj); + _InputArray(const Mat& m); + _InputArray(const MatExpr& expr); + _InputArray(const std::vector& vec); + template _InputArray(const Mat_<_Tp>& m); + template _InputArray(const std::vector<_Tp>& vec); + _InputArray(const std::vector& vec); + template _InputArray(const std::vector >& vec); + template _InputArray(const std::vector >& vec); + template _InputArray(const _Tp* vec, int n); + template _InputArray(const Matx<_Tp, m, n>& matx); + _InputArray(const double& val); + _InputArray(const cuda::GpuMat& d_mat); + _InputArray(const std::vector& d_mat_array); + _InputArray(const ogl::Buffer& buf); + _InputArray(const cuda::HostMem& cuda_mem); + template _InputArray(const cudev::GpuMat_<_Tp>& m); + _InputArray(const UMat& um); + _InputArray(const std::vector& umv); + + Mat getMat(int idx=-1) const; + Mat getMat_(int idx=-1) const; + UMat getUMat(int idx=-1) const; + void getMatVector(std::vector& mv) const; + void getUMatVector(std::vector& umv) const; + void getGpuMatVector(std::vector& gpumv) const; + cuda::GpuMat getGpuMat() const; + ogl::Buffer getOGlBuffer() const; + + int getFlags() const; + void* getObj() const; + Size getSz() const; + + int kind() const; + int dims(int i=-1) const; + int cols(int i=-1) const; + int rows(int i=-1) const; + Size size(int i=-1) const; + int sizend(int* sz, int i=-1) const; + bool sameSize(const _InputArray& arr) const; + size_t total(int i=-1) const; + int type(int i=-1) const; + int depth(int i=-1) const; + int channels(int i=-1) const; + bool isContinuous(int i=-1) const; + bool isSubmatrix(int i=-1) const; + bool empty() const; + void copyTo(const _OutputArray& arr) const; + void copyTo(const _OutputArray& arr, const _InputArray & mask) const; + size_t offset(int i=-1) const; + size_t step(int i=-1) const; + bool isMat() const; + bool isUMat() const; + bool isMatVector() const; + bool isUMatVector() const; + bool isMatx() const; + bool isVector() const; + bool isGpuMatVector() const; + ~_InputArray(); + +protected: + int flags; + void* obj; + Size sz; + + void init(int _flags, const void* _obj); + void init(int _flags, const void* _obj, Size _sz); +}; + + +/** @brief This type is very similar to InputArray except that it is used for input/output and output function +parameters. + +Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`, +`vector` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly +create OutputArray instances* applies here too. + +If you want to make your function polymorphic (i.e. accept different arrays as output parameters), +it is also not very difficult. Take the sample above as the reference. Note that +_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee +that the output array is properly allocated. + +Optional output parameters. If you do not need certain output array to be computed and returned to +you, pass cv::noArray(), just like you would in the case of optional input array. At the +implementation level, use _OutputArray::needed() to check if certain output array needs to be +computed or not. + +There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper +generators: +@code + typedef OutputArray OutputArrayOfArrays; + typedef OutputArray InputOutputArray; + typedef OutputArray InputOutputArrayOfArrays; +@endcode + */ +class CV_EXPORTS _OutputArray : public _InputArray +{ +public: + enum + { + DEPTH_MASK_8U = 1 << CV_8U, + DEPTH_MASK_8S = 1 << CV_8S, + DEPTH_MASK_16U = 1 << CV_16U, + DEPTH_MASK_16S = 1 << CV_16S, + DEPTH_MASK_32S = 1 << CV_32S, + DEPTH_MASK_32F = 1 << CV_32F, + DEPTH_MASK_64F = 1 << CV_64F, + DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1, + DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S, + DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F + }; + + _OutputArray(); + _OutputArray(int _flags, void* _obj); + _OutputArray(Mat& m); + _OutputArray(std::vector& vec); + _OutputArray(cuda::GpuMat& d_mat); + _OutputArray(std::vector& d_mat); + _OutputArray(ogl::Buffer& buf); + _OutputArray(cuda::HostMem& cuda_mem); + template _OutputArray(cudev::GpuMat_<_Tp>& m); + template _OutputArray(std::vector<_Tp>& vec); + _OutputArray(std::vector& vec); + template _OutputArray(std::vector >& vec); + template _OutputArray(std::vector >& vec); + template _OutputArray(Mat_<_Tp>& m); + template _OutputArray(_Tp* vec, int n); + template _OutputArray(Matx<_Tp, m, n>& matx); + _OutputArray(UMat& m); + _OutputArray(std::vector& vec); + + _OutputArray(const Mat& m); + _OutputArray(const std::vector& vec); + _OutputArray(const cuda::GpuMat& d_mat); + _OutputArray(const std::vector& d_mat); + _OutputArray(const ogl::Buffer& buf); + _OutputArray(const cuda::HostMem& cuda_mem); + template _OutputArray(const cudev::GpuMat_<_Tp>& m); + template _OutputArray(const std::vector<_Tp>& vec); + template _OutputArray(const std::vector >& vec); + template _OutputArray(const std::vector >& vec); + template _OutputArray(const Mat_<_Tp>& m); + template _OutputArray(const _Tp* vec, int n); + template _OutputArray(const Matx<_Tp, m, n>& matx); + _OutputArray(const UMat& m); + _OutputArray(const std::vector& vec); + + bool fixedSize() const; + bool fixedType() const; + bool needed() const; + Mat& getMatRef(int i=-1) const; + UMat& getUMatRef(int i=-1) const; + cuda::GpuMat& getGpuMatRef() const; + std::vector& getGpuMatVecRef() const; + ogl::Buffer& getOGlBufferRef() const; + cuda::HostMem& getHostMemRef() const; + void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void createSameSize(const _InputArray& arr, int mtype) const; + void release() const; + void clear() const; + void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const; + + void assign(const UMat& u) const; + void assign(const Mat& m) const; +}; + + +class CV_EXPORTS _InputOutputArray : public _OutputArray +{ +public: + _InputOutputArray(); + _InputOutputArray(int _flags, void* _obj); + _InputOutputArray(Mat& m); + _InputOutputArray(std::vector& vec); + _InputOutputArray(cuda::GpuMat& d_mat); + _InputOutputArray(ogl::Buffer& buf); + _InputOutputArray(cuda::HostMem& cuda_mem); + template _InputOutputArray(cudev::GpuMat_<_Tp>& m); + template _InputOutputArray(std::vector<_Tp>& vec); + _InputOutputArray(std::vector& vec); + template _InputOutputArray(std::vector >& vec); + template _InputOutputArray(std::vector >& vec); + template _InputOutputArray(Mat_<_Tp>& m); + template _InputOutputArray(_Tp* vec, int n); + template _InputOutputArray(Matx<_Tp, m, n>& matx); + _InputOutputArray(UMat& m); + _InputOutputArray(std::vector& vec); + + _InputOutputArray(const Mat& m); + _InputOutputArray(const std::vector& vec); + _InputOutputArray(const cuda::GpuMat& d_mat); + _InputOutputArray(const std::vector& d_mat); + _InputOutputArray(const ogl::Buffer& buf); + _InputOutputArray(const cuda::HostMem& cuda_mem); + template _InputOutputArray(const cudev::GpuMat_<_Tp>& m); + template _InputOutputArray(const std::vector<_Tp>& vec); + template _InputOutputArray(const std::vector >& vec); + template _InputOutputArray(const std::vector >& vec); + template _InputOutputArray(const Mat_<_Tp>& m); + template _InputOutputArray(const _Tp* vec, int n); + template _InputOutputArray(const Matx<_Tp, m, n>& matx); + _InputOutputArray(const UMat& m); + _InputOutputArray(const std::vector& vec); +}; + +typedef const _InputArray& InputArray; +typedef InputArray InputArrayOfArrays; +typedef const _OutputArray& OutputArray; +typedef OutputArray OutputArrayOfArrays; +typedef const _InputOutputArray& InputOutputArray; +typedef InputOutputArray InputOutputArrayOfArrays; + +CV_EXPORTS InputOutputArray noArray(); + +/////////////////////////////////// MatAllocator ////////////////////////////////////// + +//! Usage flags for allocator +enum UMatUsageFlags +{ + USAGE_DEFAULT = 0, + + // buffer allocation policy is platform and usage specific + USAGE_ALLOCATE_HOST_MEMORY = 1 << 0, + USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1, + USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY + + __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint +}; + +struct CV_EXPORTS UMatData; + +/** @brief Custom array allocator +*/ +class CV_EXPORTS MatAllocator +{ +public: + MatAllocator() {} + virtual ~MatAllocator() {} + + // let's comment it off for now to detect and fix all the uses of allocator + //virtual void allocate(int dims, const int* sizes, int type, int*& refcount, + // uchar*& datastart, uchar*& data, size_t* step) = 0; + //virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0; + virtual UMatData* allocate(int dims, const int* sizes, int type, + void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const = 0; + virtual bool allocate(UMatData* data, int accessflags, UMatUsageFlags usageFlags) const = 0; + virtual void deallocate(UMatData* data) const = 0; + virtual void map(UMatData* data, int accessflags) const; + virtual void unmap(UMatData* data) const; + virtual void download(UMatData* data, void* dst, int dims, const size_t sz[], + const size_t srcofs[], const size_t srcstep[], + const size_t dststep[]) const; + virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[], + const size_t dstofs[], const size_t dststep[], + const size_t srcstep[]) const; + virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[], + const size_t srcofs[], const size_t srcstep[], + const size_t dstofs[], const size_t dststep[], bool sync) const; + + // default implementation returns DummyBufferPoolController + virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const; +}; + + +//////////////////////////////// MatCommaInitializer ////////////////////////////////// + +/** @brief Comma-separated Matrix Initializer + + The class instances are usually not created explicitly. + Instead, they are created on "matrix << firstValue" operator. + + The sample below initializes 2x2 rotation matrix: + + \code + double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180); + Mat R = (Mat_(2,2) << a, -b, b, a); + \endcode +*/ +template class MatCommaInitializer_ +{ +public: + //! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat + MatCommaInitializer_(Mat_<_Tp>* _m); + //! the operator that takes the next value and put it to the matrix + template MatCommaInitializer_<_Tp>& operator , (T2 v); + //! another form of conversion operator + operator Mat_<_Tp>() const; +protected: + MatIterator_<_Tp> it; +}; + + +/////////////////////////////////////// Mat /////////////////////////////////////////// + +// note that umatdata might be allocated together +// with the matrix data, not as a separate object. +// therefore, it does not have constructor or destructor; +// it should be explicitly initialized using init(). +struct CV_EXPORTS UMatData +{ + enum { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2, + DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24, + USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64}; + UMatData(const MatAllocator* allocator); + ~UMatData(); + + // provide atomic access to the structure + void lock(); + void unlock(); + + bool hostCopyObsolete() const; + bool deviceCopyObsolete() const; + bool deviceMemMapped() const; + bool copyOnMap() const; + bool tempUMat() const; + bool tempCopiedUMat() const; + void markHostCopyObsolete(bool flag); + void markDeviceCopyObsolete(bool flag); + void markDeviceMemMapped(bool flag); + + const MatAllocator* prevAllocator; + const MatAllocator* currAllocator; + int urefcount; + int refcount; + uchar* data; + uchar* origdata; + size_t size; + + int flags; + void* handle; + void* userdata; + int allocatorFlags_; + int mapcount; + UMatData* originalUMatData; +}; + + +struct CV_EXPORTS UMatDataAutoLock +{ + explicit UMatDataAutoLock(UMatData* u); + ~UMatDataAutoLock(); + UMatData* u; +}; + + +struct CV_EXPORTS MatSize +{ + explicit MatSize(int* _p); + Size operator()() const; + const int& operator[](int i) const; + int& operator[](int i); + operator const int*() const; + bool operator == (const MatSize& sz) const; + bool operator != (const MatSize& sz) const; + + int* p; +}; + +struct CV_EXPORTS MatStep +{ + MatStep(); + explicit MatStep(size_t s); + const size_t& operator[](int i) const; + size_t& operator[](int i); + operator size_t() const; + MatStep& operator = (size_t s); + + size_t* p; + size_t buf[2]; +protected: + MatStep& operator = (const MatStep&); +}; + +/** @example cout_mat.cpp +An example demonstrating the serial out capabilities of cv::Mat +*/ + + /** @brief n-dimensional dense array class + +The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It +can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel +volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms +may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array +`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means +that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, +and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() . + +So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV +1.x. It is also compatible with the majority of dense array types from the standard toolkits and +SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any +array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this +compatibility, it is possible to make a Mat header for user-allocated data and process it in-place +using OpenCV functions. + +There are many different ways to create a Mat object. The most popular options are listed below: + +- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) +constructor. A new array of the specified size and type is allocated. type has the same meaning as +in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 +means a 2-channel (complex) floating-point array, and so on. +@code + // make a 7x7 complex matrix filled with 1+3j. + Mat M(7,7,CV_32FC2,Scalar(1,3)); + // and now turn M to a 100x60 15-channel 8-bit matrix. + // The old content will be deallocated + M.create(100,60,CV_8UC(15)); +@endcode +As noted in the introduction to this chapter, create() allocates only a new array when the shape +or type of the current array are different from the specified ones. + +- Create a multi-dimensional array: +@code + // create a 100x100x100 8-bit array + int sz[] = {100, 100, 100}; + Mat bigCube(3, sz, CV_8U, Scalar::all(0)); +@endcode +It passes the number of dimensions =1 to the Mat constructor but the created array will be +2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0 +when the array is empty). + +- Use a copy constructor or assignment operator where there can be an array or expression on the +right side (see below). As noted in the introduction, the array assignment is an O(1) operation +because it only copies the header and increases the reference counter. The Mat::clone() method can +be used to get a full (deep) copy of the array when you need it. + +- Construct a header for a part of another array. It can be a single row, single column, several +rows, several columns, rectangular region in the array (called a *minor* in algebra) or a +diagonal. Such operations are also O(1) because the new header references the same data. You can +actually modify a part of the array using this feature, for example: +@code + // add the 5-th row, multiplied by 3 to the 3rd row + M.row(3) = M.row(3) + M.row(5)*3; + // now copy the 7-th column to the 1-st column + // M.col(1) = M.col(7); // this will not work + Mat M1 = M.col(1); + M.col(7).copyTo(M1); + // create a new 320x240 image + Mat img(Size(320,240),CV_8UC3); + // select a ROI + Mat roi(img, Rect(10,10,100,100)); + // fill the ROI with (0,255,0) (which is green in RGB space); + // the original 320x240 image will be modified + roi = Scalar(0,255,0); +@endcode +Due to the additional datastart and dataend members, it is possible to compute a relative +sub-array position in the main *container* array using locateROI(): +@code + Mat A = Mat::eye(10, 10, CV_32S); + // extracts A columns, 1 (inclusive) to 3 (exclusive). + Mat B = A(Range::all(), Range(1, 3)); + // extracts B rows, 5 (inclusive) to 9 (exclusive). + // that is, C \~ A(Range(5, 9), Range(1, 3)) + Mat C = B(Range(5, 9), Range::all()); + Size size; Point ofs; + C.locateROI(size, ofs); + // size will be (width=10,height=10) and the ofs will be (x=1, y=5) +@endcode +As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted +sub-matrices. + +- Make a header for user-allocated data. It can be useful to do the following: + -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or + a processing module for gstreamer, and so on). For example: + @code + void process_video_frame(const unsigned char* pixels, + int width, int height, int step) + { + Mat img(height, width, CV_8UC3, pixels, step); + GaussianBlur(img, img, Size(7,7), 1.5, 1.5); + } + @endcode + -# Quickly initialize small matrices and/or get a super-fast element access. + @code + double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}}; + Mat M = Mat(3, 3, CV_64F, m).inv(); + @endcode + . + Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and + IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or + IplImage and the optional flag indicating whether to copy the data or not. + @snippet samples/cpp/image.cpp iplimage + +- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example: +@code + // create a double-precision identity martix and add it to M. + M += Mat::eye(M.rows, M.cols, CV_64F); +@endcode + +- Use a comma-separated initializer: +@code + // create a 3x3 double-precision identity matrix + Mat M = (Mat_(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1); +@endcode +With this approach, you first call a constructor of the Mat class with the proper parameters, and +then you just put `<< operator` followed by comma-separated values that can be constants, +variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation +errors. + +Once the array is created, it is automatically managed via a reference-counting mechanism. If the +array header is built on top of user-allocated data, you should handle the data by yourself. The +array data is deallocated when no one points to it. If you want to release the data pointed by a +array header before the array destructor is called, use Mat::release(). + +The next important thing to learn about the array class is element access. This manual already +described how to compute an address of each array element. Normally, you are not required to use the +formula directly in the code. If you know the array element type (which can be retrieved using the +method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as: +@code + M.at(i,j) += 1.f; +@endcode +assuming that `M` is a double-precision floating-point array. There are several variants of the method +at for a different number of dimensions. + +If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to +the row first, and then just use the plain C operator [] : +@code + // compute sum of positive matrix elements + // (assuming that M isa double-precision matrix) + double sum=0; + for(int i = 0; i < M.rows; i++) + { + const double* Mi = M.ptr(i); + for(int j = 0; j < M.cols; j++) + sum += std::max(Mi[j], 0.); + } +@endcode +Some operations, like the one above, do not actually depend on the array shape. They just process +elements of an array one by one (or elements from multiple arrays that have the same coordinates, +for example, array addition). Such operations are called *element-wise*. It makes sense to check +whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If +yes, process them as a long single row: +@code + // compute the sum of positive matrix elements, optimized variant + double sum=0; + int cols = M.cols, rows = M.rows; + if(M.isContinuous()) + { + cols *= rows; + rows = 1; + } + for(int i = 0; i < rows; i++) + { + const double* Mi = M.ptr(i); + for(int j = 0; j < cols; j++) + sum += std::max(Mi[j], 0.); + } +@endcode +In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is +smaller, which is especially noticeable in case of small matrices. + +Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows: +@code + // compute sum of positive matrix elements, iterator-based variant + double sum=0; + MatConstIterator_ it = M.begin(), it_end = M.end(); + for(; it != it_end; ++it) + sum += std::max(*it, 0.); +@endcode +The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, +including std::sort(). +*/ +class CV_EXPORTS Mat +{ +public: + /** + These are various constructors that form a matrix. As noted in the AutomaticAllocation, often + the default constructor is enough, and the proper matrix will be allocated by an OpenCV function. + The constructed matrix can further be assigned to another matrix or matrix expression or can be + allocated with Mat::create . In the former case, the old content is de-referenced. + */ + Mat(); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(int rows, int cols, int type); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(Size size, int type); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(int rows, int cols, int type, const Scalar& s); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(Size size, int type, const Scalar& s); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(int ndims, const int* sizes, int type); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(const std::vector& sizes, int type); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(int ndims, const int* sizes, int type, const Scalar& s); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(const std::vector& sizes, int type, const Scalar& s); + + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + */ + Mat(const Mat& m); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param step Number of bytes each matrix row occupies. The value should include the padding bytes at + the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed + and the actual step is calculated as cols*elemSize(). See Mat::elemSize. + */ + Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param step Number of bytes each matrix row occupies. The value should include the padding bytes at + the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed + and the actual step is calculated as cols*elemSize(). See Mat::elemSize. + */ + Mat(Size size, int type, void* data, size_t step=AUTO_STEP); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always + set to the element size). If not specified, the matrix is assumed to be continuous. + */ + Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always + set to the element size). If not specified, the matrix is assumed to be continuous. + */ + Mat(const std::vector& sizes, int type, void* data, const size_t* steps=0); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range + end is exclusive. Use Range::all() to take all the rows. + @param colRange Range of the m columns to take. Use Range::all() to take all the columns. + */ + Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all()); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param roi Region of interest. + */ + Mat(const Mat& m, const Rect& roi); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param ranges Array of selected ranges of m along each dimensionality. + */ + Mat(const Mat& m, const Range* ranges); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param ranges Array of selected ranges of m along each dimensionality. + */ + Mat(const Mat& m, const std::vector& ranges); + + /** @overload + @param vec STL vector whose elements form the matrix. The matrix has a single column and the number + of rows equal to the number of vector elements. Type of the matrix matches the type of vector + elements. The constructor can handle arbitrary types, for which there is a properly declared + DataType . This means that the vector elements must be primitive numbers or uni-type numerical + tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is + explicit. Since STL vectors are not automatically converted to Mat instances, you should write + Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements + will be added to the vector because it can potentially yield vector data reallocation, and, thus, + the matrix data pointer will be invalid. + @param copyData Flag to specify whether the underlying data of the STL vector should be copied + to (true) or shared with (false) the newly constructed matrix. When the data is copied, the + allocated buffer is managed using Mat reference counting mechanism. While the data is shared, + the reference counter is NULL, and you should not deallocate the data until the matrix is not + destructed. + */ + template explicit Mat(const std::vector<_Tp>& vec, bool copyData=false); + + /** @overload + */ + template explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Point_<_Tp>& pt, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Point3_<_Tp>& pt, bool copyData=true); + + /** @overload + */ + template explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer); + + //! download data from GpuMat + explicit Mat(const cuda::GpuMat& m); + + //! destructor - calls release() + ~Mat(); + + /** @brief assignment operators + + These are available assignment operators. Since they all are very different, make sure to read the + operator parameters description. + @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that + no data is copied but the data is shared and the reference counter, if any, is incremented. Before + assigning new data, the old data is de-referenced via Mat::release . + */ + Mat& operator = (const Mat& m); + + /** @overload + @param expr Assigned matrix expression object. As opposite to the first form of the assignment + operation, the second form can reuse already allocated matrix if it has the right size and type to + fit the matrix expression result. It is automatically handled by the real function that the matrix + expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of + automatic C reallocation. + */ + Mat& operator = (const MatExpr& expr); + + //! retrieve UMat from Mat + UMat getUMat(int accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const; + + /** @brief Creates a matrix header for the specified matrix row. + + The method makes a new header for the specified matrix row and returns it. This is an O(1) + operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + original matrix. Here is the example of one of the classical basic matrix processing operations, + axpy, used by LU and many other algorithms: + @code + inline void matrix_axpy(Mat& A, int i, int j, double alpha) + { + A.row(i) += A.row(j)*alpha; + } + @endcode + @note In the current implementation, the following code does not work as expected: + @code + Mat A; + ... + A.row(i) = A.row(j); // will not work + @endcode + This happens because A.row(i) forms a temporary header that is further assigned to another header. + Remember that each of these operations is O(1), that is, no data is copied. Thus, the above + assignment is not true if you may have expected the j-th row to be copied to the i-th row. To + achieve that, you should either turn this simple assignment into an expression or use the + Mat::copyTo method: + @code + Mat A; + ... + // works, but looks a bit obscure. + A.row(i) = A.row(j) + 0; + // this is a bit longer, but the recommended method. + A.row(j).copyTo(A.row(i)); + @endcode + @param y A 0-based row index. + */ + Mat row(int y) const; + + /** @brief Creates a matrix header for the specified matrix column. + + The method makes a new header for the specified matrix column and returns it. This is an O(1) + operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + original matrix. See also the Mat::row description. + @param x A 0-based column index. + */ + Mat col(int x) const; + + /** @brief Creates a matrix header for the specified row span. + + The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and + Mat::col , this is an O(1) operation. + @param startrow An inclusive 0-based start index of the row span. + @param endrow An exclusive 0-based ending index of the row span. + */ + Mat rowRange(int startrow, int endrow) const; + + /** @overload + @param r Range structure containing both the start and the end indices. + */ + Mat rowRange(const Range& r) const; + + /** @brief Creates a matrix header for the specified column span. + + The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and + Mat::col , this is an O(1) operation. + @param startcol An inclusive 0-based start index of the column span. + @param endcol An exclusive 0-based ending index of the column span. + */ + Mat colRange(int startcol, int endcol) const; + + /** @overload + @param r Range structure containing both the start and the end indices. + */ + Mat colRange(const Range& r) const; + + /** @brief Extracts a diagonal from a matrix + + The method makes a new header for the specified matrix diagonal. The new matrix is represented as a + single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. + @param d index of the diagonal, with the following values: + - `d=0` is the main diagonal. + - `d>0` is a diagonal from the lower half. For example, d=1 means the diagonal is set + immediately below the main one. + - `d<0` is a diagonal from the upper half. For example, d=-1 means the diagonal is set + immediately above the main one. + */ + Mat diag(int d=0) const; + + /** @brief creates a diagonal matrix + + The method creates a square diagonal matrix from specified main diagonal. + @param d One-dimensional matrix that represents the main diagonal. + */ + static Mat diag(const Mat& d); + + /** @brief Creates a full copy of the array and the underlying data. + + The method creates a full copy of the array. The original step[] is not taken into account. So, the + array copy is a continuous array occupying total()*elemSize() bytes. + */ + Mat clone() const; + + /** @brief Copies the matrix to another one. + + The method copies the matrix data to another matrix. Before copying the data, the method invokes : + @code + m.create(this->size(), this->type()); + @endcode + so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the + function does not handle the case of a partial overlap between the source and the destination + matrices. + + When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, + the newly allocated matrix is initialized with all zeros before copying the data. + @param m Destination matrix. If it does not have a proper size or type before the operation, it is + reallocated. + */ + void copyTo( OutputArray m ) const; + + /** @overload + @param m Destination matrix. If it does not have a proper size or type before the operation, it is + reallocated. + @param mask Operation mask. Its non-zero elements indicate which matrix elements need to be copied. + The mask has to be of type CV_8U and can have 1 or multiple channels. + */ + void copyTo( OutputArray m, InputArray mask ) const; + + /** @brief Converts an array to another data type with optional scaling. + + The method converts source pixel values to the target data type. saturate_cast\<\> is applied at + the end to avoid possible overflows: + + \f[m(x,y) = saturate \_ cast( \alpha (*this)(x,y) + \beta )\f] + @param m output matrix; if it does not have a proper size or type before the operation, it is + reallocated. + @param rtype desired output matrix type or, rather, the depth since the number of channels are the + same as the input has; if rtype is negative, the output matrix will have the same type as the input. + @param alpha optional scale factor. + @param beta optional delta added to the scaled values. + */ + void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; + + /** @brief Provides a functional form of convertTo. + + This is an internally used method called by the @ref MatrixExpressions engine. + @param m Destination array. + @param type Desired destination array depth (or -1 if it should be the same as the source type). + */ + void assignTo( Mat& m, int type=-1 ) const; + + /** @brief Sets all or some of the array elements to the specified value. + @param s Assigned scalar converted to the actual array type. + */ + Mat& operator = (const Scalar& s); + + /** @brief Sets all or some of the array elements to the specified value. + + This is an advanced variant of the Mat::operator=(const Scalar& s) operator. + @param value Assigned scalar converted to the actual array type. + @param mask Operation mask of the same size as \*this. + */ + Mat& setTo(InputArray value, InputArray mask=noArray()); + + /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data. + + The method makes a new matrix header for \*this elements. The new matrix may have a different size + and/or different number of channels. Any combination is possible if: + - No extra elements are included into the new matrix and no elements are excluded. Consequently, + the product rows\*cols\*channels() must stay the same after the transformation. + - No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + rows, or the operation changes the indices of elements row in some other way, the matrix must be + continuous. See Mat::isContinuous . + + For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + points as a 3xN matrix, do the following: + @code + std::vector vec; + ... + Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + // Also, an O(1) operation + t(); // finally, transpose the Nx3 matrix. + // This involves copying all the elements + @endcode + @param cn New number of channels. If the parameter is 0, the number of channels remains the same. + @param rows New number of rows. If the parameter is 0, the number of rows remains the same. + */ + Mat reshape(int cn, int rows=0) const; + + /** @overload */ + Mat reshape(int cn, int newndims, const int* newsz) const; + + /** @brief Transposes a matrix. + + The method performs matrix transposition by means of matrix expressions. It does not perform the + actual transposition but returns a temporary matrix transposition object that can be further used as + a part of more complex matrix expressions or can be assigned to a matrix: + @code + Mat A1 = A + Mat::eye(A.size(), A.type())*lambda; + Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I) + @endcode + */ + MatExpr t() const; + + /** @brief Inverses a matrix. + + The method performs a matrix inversion by means of matrix expressions. This means that a temporary + matrix inversion object is returned by the method and can be used further as a part of more complex + matrix expressions or can be assigned to a matrix. + @param method Matrix inversion method. One of cv::DecompTypes + */ + MatExpr inv(int method=DECOMP_LU) const; + + /** @brief Performs an element-wise multiplication or division of the two matrices. + + The method returns a temporary object encoding per-element array multiplication, with optional + scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator. + + Example: + @code + Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5) + @endcode + @param m Another array of the same type and the same size as \*this, or a matrix expression. + @param scale Optional scale factor. + */ + MatExpr mul(InputArray m, double scale=1) const; + + /** @brief Computes a cross-product of two 3-element vectors. + + The method computes a cross-product of two 3-element vectors. The vectors must be 3-element + floating-point vectors of the same shape and size. The result is another 3-element vector of the + same shape and type as operands. + @param m Another cross-product operand. + */ + Mat cross(InputArray m) const; + + /** @brief Computes a dot-product of two vectors. + + The method computes a dot-product of two matrices. If the matrices are not single-column or + single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D + vectors. The vectors must have the same size and type. If the matrices have more than one channel, + the dot products from all the channels are summed together. + @param m another dot-product operand. + */ + double dot(InputArray m) const; + + /** @brief Returns a zero array of the specified size and type. + + The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant + array as a function parameter, part of a matrix expression, or as a matrix initializer. : + @code + Mat A; + A = Mat::zeros(3, 3, CV_32F); + @endcode + In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix. + Otherwise, the existing matrix A is filled with zeros. + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr zeros(int rows, int cols, int type); + + /** @overload + @param size Alternative to the matrix size specification Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr zeros(Size size, int type); + + /** @overload + @param ndims Array dimensionality. + @param sz Array of integers specifying the array shape. + @param type Created matrix type. + */ + static MatExpr zeros(int ndims, const int* sz, int type); + + /** @brief Returns an array of all 1's of the specified size and type. + + The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using + this method you can initialize an array with an arbitrary value, using the following Matlab idiom: + @code + Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3. + @endcode + The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it + just remembers the scale factor (3 in this case) and use it when actually invoking the matrix + initializer. + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr ones(int rows, int cols, int type); + + /** @overload + @param size Alternative to the matrix size specification Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr ones(Size size, int type); + + /** @overload + @param ndims Array dimensionality. + @param sz Array of integers specifying the array shape. + @param type Created matrix type. + */ + static MatExpr ones(int ndims, const int* sz, int type); + + /** @brief Returns an identity matrix of the specified size and type. + + The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to + Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently: + @code + // make a 4x4 diagonal matrix with 0.1's on the diagonal. + Mat A = Mat::eye(4, 4, CV_32F)*0.1; + @endcode + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr eye(int rows, int cols, int type); + + /** @overload + @param size Alternative matrix size specification as Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr eye(Size size, int type); + + /** @brief Allocates new array data if needed. + + This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays + call this method for each output array. The method uses the following algorithm: + + -# If the current array shape and the type match the new ones, return immediately. Otherwise, + de-reference the previous data by calling Mat::release. + -# Initialize the new header. + -# Allocate the new data of total()\*elemSize() bytes. + -# Allocate the new, associated with the data, reference counter and set it to 1. + + Such a scheme makes the memory management robust and efficient at the same time and helps avoid + extra typing for you. This means that usually there is no need to explicitly allocate output arrays. + That is, instead of writing: + @code + Mat color; + ... + Mat gray(color.rows, color.cols, color.depth()); + cvtColor(color, gray, COLOR_BGR2GRAY); + @endcode + you can simply write: + @code + Mat color; + ... + Mat gray; + cvtColor(color, gray, COLOR_BGR2GRAY); + @endcode + because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array + internally. + @param rows New number of rows. + @param cols New number of columns. + @param type New matrix type. + */ + void create(int rows, int cols, int type); + + /** @overload + @param size Alternative new matrix size specification: Size(cols, rows) + @param type New matrix type. + */ + void create(Size size, int type); + + /** @overload + @param ndims New array dimensionality. + @param sizes Array of integers specifying a new array shape. + @param type New matrix type. + */ + void create(int ndims, const int* sizes, int type); + + /** @overload + @param sizes Array of integers specifying a new array shape. + @param type New matrix type. + */ + void create(const std::vector& sizes, int type); + + /** @brief Increments the reference counter. + + The method increments the reference counter associated with the matrix data. If the matrix header + points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no + effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It + is called implicitly by the matrix assignment operator. The reference counter increment is an atomic + operation on the platforms that support it. Thus, it is safe to operate on the same matrices + asynchronously in different threads. + */ + void addref(); + + /** @brief Decrements the reference counter and deallocates the matrix if needed. + + The method decrements the reference counter associated with the matrix data. When the reference + counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers + are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the + reference counter is NULL, and the method has no effect in this case. + + This method can be called manually to force the matrix data deallocation. But since this method is + automatically called in the destructor, or by any other method that changes the data pointer, it is + usually not needed. The reference counter decrement and check for 0 is an atomic operation on the + platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in + different threads. + */ + void release(); + + //! deallocates the matrix data + void deallocate(); + //! internal use function; properly re-allocates _size, _step arrays + void copySize(const Mat& m); + + /** @brief Reserves space for the certain number of rows. + + The method reserves space for sz rows. If the matrix already has enough space to store sz rows, + nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method + emulates the corresponding method of the STL vector class. + @param sz Number of rows. + */ + void reserve(size_t sz); + + /** @brief Changes the number of matrix rows. + + The methods change the number of matrix rows. If the matrix is reallocated, the first + min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL + vector class. + @param sz New number of rows. + */ + void resize(size_t sz); + + /** @overload + @param sz New number of rows. + @param s Value assigned to the newly added elements. + */ + void resize(size_t sz, const Scalar& s); + + //! internal function + void push_back_(const void* elem); + + /** @brief Adds elements to the bottom of the matrix. + + The methods add one or more elements to the bottom of the matrix. They emulate the corresponding + method of the STL vector class. When elem is Mat , its type and the number of columns must be the + same as in the container matrix. + @param elem Added element(s). + */ + template void push_back(const _Tp& elem); + + /** @overload + @param elem Added element(s). + */ + template void push_back(const Mat_<_Tp>& elem); + + /** @overload + @param m Added line(s). + */ + void push_back(const Mat& m); + + /** @brief Removes elements from the bottom of the matrix. + + The method removes one or more rows from the bottom of the matrix. + @param nelems Number of removed rows. If it is greater than the total number of rows, an exception + is thrown. + */ + void pop_back(size_t nelems=1); + + /** @brief Locates the matrix header within a parent matrix. + + After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange, + Mat::colRange, and others, the resultant submatrix points just to the part of the original big + matrix. However, each submatrix contains information (represented by datastart and dataend + fields) that helps reconstruct the original matrix size and the position of the extracted + submatrix within the original matrix. The method locateROI does exactly that. + @param wholeSize Output parameter that contains the size of the whole matrix containing *this* + as a part. + @param ofs Output parameter that contains an offset of *this* inside the whole matrix. + */ + void locateROI( Size& wholeSize, Point& ofs ) const; + + /** @brief Adjusts a submatrix size and position within the parent matrix. + + The method is complimentary to Mat::locateROI . The typical use of these functions is to determine + the submatrix position within the parent matrix and then shift the position somehow. Typically, it + can be required for filtering operations when pixels outside of the ROI should be taken into + account. When all the method parameters are positive, the ROI needs to grow in all directions by the + specified amount, for example: + @code + A.adjustROI(2, 2, 2, 2); + @endcode + In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted + by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the + filtering with the 5x5 kernel. + + adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the + adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is + located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not + be increased in the upward direction. + + The function is used internally by the OpenCV filtering functions, like filter2D , morphological + operations, and so on. + @param dtop Shift of the top submatrix boundary upwards. + @param dbottom Shift of the bottom submatrix boundary downwards. + @param dleft Shift of the left submatrix boundary to the left. + @param dright Shift of the right submatrix boundary to the right. + @sa copyMakeBorder + */ + Mat& adjustROI( int dtop, int dbottom, int dleft, int dright ); + + /** @brief Extracts a rectangular submatrix. + + The operators make a new header for the specified sub-array of \*this . They are the most + generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + the operators are O(1) operations, that is, no matrix data is copied. + @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To + select all the rows, use Range::all(). + @param colRange Start and end column of the extracted submatrix. The upper boundary is not included. + To select all the columns, use Range::all(). + */ + Mat operator()( Range rowRange, Range colRange ) const; + + /** @overload + @param roi Extracted submatrix specified as a rectangle. + */ + Mat operator()( const Rect& roi ) const; + + /** @overload + @param ranges Array of selected ranges along each array dimension. + */ + Mat operator()( const Range* ranges ) const; + + /** @overload + @param ranges Array of selected ranges along each array dimension. + */ + Mat operator()(const std::vector& ranges) const; + + // //! converts header to CvMat; no data is copied + // operator CvMat() const; + // //! converts header to CvMatND; no data is copied + // operator CvMatND() const; + // //! converts header to IplImage; no data is copied + // operator IplImage() const; + + template operator std::vector<_Tp>() const; + template operator Vec<_Tp, n>() const; + template operator Matx<_Tp, m, n>() const; + + /** @brief Reports whether the matrix is continuous or not. + + The method returns true if the matrix elements are stored continuously without gaps at the end of + each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous. + Matrices created with Mat::create are always continuous. But if you extract a part of the matrix + using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data, + such matrices may no longer have this property. + + The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when + you construct a matrix header. Thus, the continuity check is a very fast operation, though + theoretically it could be done as follows: + @code + // alternative implementation of Mat::isContinuous() + bool myCheckMatContinuity(const Mat& m) + { + //return (m.flags & Mat::CONTINUOUS_FLAG) != 0; + return m.rows == 1 || m.step == m.cols*m.elemSize(); + } + @endcode + The method is used in quite a few of OpenCV functions. The point is that element-wise operations + (such as arithmetic and logical operations, math functions, alpha blending, color space + transformations, and others) do not depend on the image geometry. Thus, if all the input and output + arrays are continuous, the functions can process them as very long single-row vectors. The example + below illustrates how an alpha-blending function can be implemented: + @code + template + void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + { + const float alpha_scale = (float)std::numeric_limits::max(), + inv_scale = 1.f/alpha_scale; + + CV_Assert( src1.type() == src2.type() && + src1.type() == CV_MAKETYPE(DataType::depth, 4) && + src1.size() == src2.size()); + Size size = src1.size(); + dst.create(size, src1.type()); + + // here is the idiom: check the arrays for continuity and, + // if this is the case, + // treat the arrays as 1D vectors + if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) + { + size.width *= size.height; + size.height = 1; + } + size.width *= 4; + + for( int i = 0; i < size.height; i++ ) + { + // when the arrays are continuous, + // the outer loop is executed only once + const T* ptr1 = src1.ptr(i); + const T* ptr2 = src2.ptr(i); + T* dptr = dst.ptr(i); + + for( int j = 0; j < size.width; j += 4 ) + { + float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale; + dptr[j] = saturate_cast(ptr1[j]*alpha + ptr2[j]*beta); + dptr[j+1] = saturate_cast(ptr1[j+1]*alpha + ptr2[j+1]*beta); + dptr[j+2] = saturate_cast(ptr1[j+2]*alpha + ptr2[j+2]*beta); + dptr[j+3] = saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale); + } + } + } + @endcode + This approach, while being very simple, can boost the performance of a simple element-operation by + 10-20 percents, especially if the image is rather small and the operation is quite simple. + + Another OpenCV idiom in this function, a call of Mat::create for the destination array, that + allocates the destination array unless it already has the proper size and type. And while the newly + allocated arrays are always continuous, you still need to check the destination array because + Mat::create does not always allocate a new matrix. + */ + bool isContinuous() const; + + //! returns true if the matrix is a submatrix of another matrix + bool isSubmatrix() const; + + /** @brief Returns the matrix element size in bytes. + + The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 , + the method returns 3\*sizeof(short) or 6. + */ + size_t elemSize() const; + + /** @brief Returns the size of each matrix element channel in bytes. + + The method returns the matrix element channel size in bytes, that is, it ignores the number of + channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2. + */ + size_t elemSize1() const; + + /** @brief Returns the type of a matrix element. + + The method returns a matrix element type. This is an identifier compatible with the CvMat type + system, like CV_16SC3 or 16-bit signed 3-channel array, and so on. + */ + int type() const; + + /** @brief Returns the depth of a matrix element. + + The method returns the identifier of the matrix element depth (the type of each individual channel). + For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of + matrix types contains the following values: + - CV_8U - 8-bit unsigned integers ( 0..255 ) + - CV_8S - 8-bit signed integers ( -128..127 ) + - CV_16U - 16-bit unsigned integers ( 0..65535 ) + - CV_16S - 16-bit signed integers ( -32768..32767 ) + - CV_32S - 32-bit signed integers ( -2147483648..2147483647 ) + - CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN ) + - CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN ) + */ + int depth() const; + + /** @brief Returns the number of matrix channels. + + The method returns the number of matrix channels. + */ + int channels() const; + + /** @brief Returns a normalized step. + + The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an + arbitrary matrix element. + */ + size_t step1(int i=0) const; + + /** @brief Returns true if the array has no elements. + + The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and + resize() methods `M.total() == 0` does not imply that `M.data == NULL`. + */ + bool empty() const; + + /** @brief Returns the total number of array elements. + + The method returns the number of array elements (a number of pixels if the array represents an + image). + */ + size_t total() const; + + //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise + int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; + + /** @brief Returns a pointer to the specified matrix row. + + The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + Mat::isContinuous to know how to use these methods. + @param i0 A 0-based row index. + */ + uchar* ptr(int i0=0); + /** @overload */ + const uchar* ptr(int i0=0) const; + + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + uchar* ptr(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + const uchar* ptr(int row, int col) const; + + /** @overload */ + uchar* ptr(int i0, int i1, int i2); + /** @overload */ + const uchar* ptr(int i0, int i1, int i2) const; + + /** @overload */ + uchar* ptr(const int* idx); + /** @overload */ + const uchar* ptr(const int* idx) const; + /** @overload */ + template uchar* ptr(const Vec& idx); + /** @overload */ + template const uchar* ptr(const Vec& idx) const; + + /** @overload */ + template _Tp* ptr(int i0=0); + /** @overload */ + template const _Tp* ptr(int i0=0) const; + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template _Tp* ptr(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template const _Tp* ptr(int row, int col) const; + /** @overload */ + template _Tp* ptr(int i0, int i1, int i2); + /** @overload */ + template const _Tp* ptr(int i0, int i1, int i2) const; + /** @overload */ + template _Tp* ptr(const int* idx); + /** @overload */ + template const _Tp* ptr(const int* idx) const; + /** @overload */ + template _Tp* ptr(const Vec& idx); + /** @overload */ + template const _Tp* ptr(const Vec& idx) const; + + /** @brief Returns a reference to the specified array element. + + The template methods return a reference to the specified array element. For the sake of higher + performance, the index range checks are only performed in the Debug configuration. + + Note that the variants with a single index (i) can be used to access elements of single-row or + single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + + The example below initializes a Hilbert matrix: + @code + Mat H(100, 100, CV_64F); + for(int i = 0; i < H.rows; i++) + for(int j = 0; j < H.cols; j++) + H.at(i,j)=1./(i+j+1); + @endcode + + Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + + @param i0 Index along the dimension 0 + */ + template _Tp& at(int i0=0); + /** @overload + @param i0 Index along the dimension 0 + */ + template const _Tp& at(int i0=0) const; + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template _Tp& at(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template const _Tp& at(int row, int col) const; + + /** @overload + @param i0 Index along the dimension 0 + @param i1 Index along the dimension 1 + @param i2 Index along the dimension 2 + */ + template _Tp& at(int i0, int i1, int i2); + /** @overload + @param i0 Index along the dimension 0 + @param i1 Index along the dimension 1 + @param i2 Index along the dimension 2 + */ + template const _Tp& at(int i0, int i1, int i2) const; + + /** @overload + @param idx Array of Mat::dims indices. + */ + template _Tp& at(const int* idx); + /** @overload + @param idx Array of Mat::dims indices. + */ + template const _Tp& at(const int* idx) const; + + /** @overload */ + template _Tp& at(const Vec& idx); + /** @overload */ + template const _Tp& at(const Vec& idx) const; + + /** @overload + special versions for 2D arrays (especially convenient for referencing image pixels) + @param pt Element position specified as Point(j,i) . + */ + template _Tp& at(Point pt); + /** @overload + special versions for 2D arrays (especially convenient for referencing image pixels) + @param pt Element position specified as Point(j,i) . + */ + template const _Tp& at(Point pt) const; + + /** @brief Returns the matrix iterator and sets it to the first matrix element. + + The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very + similar to the use of bi-directional STL iterators. In the example below, the alpha blending + function is rewritten using the matrix iterators: + @code + template + void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + { + typedef Vec VT; + + const float alpha_scale = (float)std::numeric_limits::max(), + inv_scale = 1.f/alpha_scale; + + CV_Assert( src1.type() == src2.type() && + src1.type() == DataType::type && + src1.size() == src2.size()); + Size size = src1.size(); + dst.create(size, src1.type()); + + MatConstIterator_ it1 = src1.begin(), it1_end = src1.end(); + MatConstIterator_ it2 = src2.begin(); + MatIterator_ dst_it = dst.begin(); + + for( ; it1 != it1_end; ++it1, ++it2, ++dst_it ) + { + VT pix1 = *it1, pix2 = *it2; + float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale; + *dst_it = VT(saturate_cast(pix1[0]*alpha + pix2[0]*beta), + saturate_cast(pix1[1]*alpha + pix2[1]*beta), + saturate_cast(pix1[2]*alpha + pix2[2]*beta), + saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale)); + } + } + @endcode + */ + template MatIterator_<_Tp> begin(); + template MatConstIterator_<_Tp> begin() const; + + /** @brief Returns the matrix iterator and sets it to the after-last matrix element. + + The methods return the matrix read-only or read-write iterators, set to the point following the last + matrix element. + */ + template MatIterator_<_Tp> end(); + template MatConstIterator_<_Tp> end() const; + + /** @brief Runs the given functor over all matrix elements in parallel. + + The operation passed as argument has to be a function pointer, a function object or a lambda(C++11). + + Example 1. All of the operations below put 0xFF the first channel of all matrix elements: + @code + Mat image(1920, 1080, CV_8UC3); + typedef cv::Point3_ Pixel; + + // first. raw pointer access. + for (int r = 0; r < image.rows; ++r) { + Pixel* ptr = image.ptr(0, r); + const Pixel* ptr_end = ptr + image.cols; + for (; ptr != ptr_end; ++ptr) { + ptr->x = 255; + } + } + + // Using MatIterator. (Simple but there are a Iterator's overhead) + for (Pixel &p : cv::Mat_(image)) { + p.x = 255; + } + + // Parallel execution with function object. + struct Operator { + void operator ()(Pixel &pixel, const int * position) { + pixel.x = 255; + } + }; + image.forEach(Operator()); + + // Parallel execution using C++11 lambda. + image.forEach([](Pixel &p, const int * position) -> void { + p.x = 255; + }); + @endcode + Example 2. Using the pixel's position: + @code + // Creating 3D matrix (255 x 255 x 255) typed uint8_t + // and initialize all elements by the value which equals elements position. + // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3). + + int sizes[] = { 255, 255, 255 }; + typedef cv::Point3_ Pixel; + + Mat_ image = Mat::zeros(3, sizes, CV_8UC3); + + image.forEach([&](Pixel& pixel, const int position[]) -> void { + pixel.x = position[0]; + pixel.y = position[1]; + pixel.z = position[2]; + }); + @endcode + */ + template void forEach(const Functor& operation); + /** @overload */ + template void forEach(const Functor& operation) const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Mat(Mat&& m); + Mat& operator = (Mat&& m); +#endif + + enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; + enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + //! the matrix dimensionality, >= 2 + int dims; + //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + int rows, cols; + //! pointer to the data + uchar* data; + + //! helper fields used in locateROI and adjustROI + const uchar* datastart; + const uchar* dataend; + const uchar* datalimit; + + //! custom allocator + MatAllocator* allocator; + //! and the standard allocator + static MatAllocator* getStdAllocator(); + static MatAllocator* getDefaultAllocator(); + static void setDefaultAllocator(MatAllocator* allocator); + + //! interaction with UMat + UMatData* u; + + MatSize size; + MatStep step; + +protected: + template void forEach_impl(const Functor& operation); +}; + + +///////////////////////////////// Mat_<_Tp> //////////////////////////////////// + +/** @brief Template matrix class derived from Mat + +@code + template class Mat_ : public Mat + { + public: + // ... some specific methods + // and + // no new extra fields + }; +@endcode +The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any +extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to +these two classes can be freely but carefully converted one to another. For example: +@code + // create a 100x100 8-bit matrix + Mat M(100,100,CV_8U); + // this will be compiled fine. no any data conversion will be done. + Mat_& M1 = (Mat_&)M; + // the program is likely to crash at the statement below + M1(99,99) = 1.f; +@endcode +While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element +access operations and if you know matrix type at the compilation time. Note that +`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same +and run at the same speed, but the latter is certainly shorter: +@code + Mat_ M(20,20); + for(int i = 0; i < M.rows; i++) + for(int j = 0; j < M.cols; j++) + M(i,j) = 1./(i+j+1); + Mat E, V; + eigen(M,E,V); + cout << E.at(0,0)/E.at(M.rows-1,0); +@endcode +To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter: +@code + // allocate a 320x240 color image and fill it with green (in RGB space) + Mat_ img(240, 320, Vec3b(0,255,0)); + // now draw a diagonal white line + for(int i = 0; i < 100; i++) + img(i,i)=Vec3b(255,255,255); + // and now scramble the 2nd (red) channel of each pixel + for(int i = 0; i < img.rows; i++) + for(int j = 0; j < img.cols; j++) + img(i,j)[2] ^= (uchar)(i ^ j); +@endcode + */ +template class Mat_ : public Mat +{ +public: + typedef _Tp value_type; + typedef typename DataType<_Tp>::channel_type channel_type; + typedef MatIterator_<_Tp> iterator; + typedef MatConstIterator_<_Tp> const_iterator; + + //! default constructor + Mat_(); + //! equivalent to Mat(_rows, _cols, DataType<_Tp>::type) + Mat_(int _rows, int _cols); + //! constructor that sets each matrix element to specified value + Mat_(int _rows, int _cols, const _Tp& value); + //! equivalent to Mat(_size, DataType<_Tp>::type) + explicit Mat_(Size _size); + //! constructor that sets each matrix element to specified value + Mat_(Size _size, const _Tp& value); + //! n-dim array constructor + Mat_(int _ndims, const int* _sizes); + //! n-dim array constructor that sets each matrix element to specified value + Mat_(int _ndims, const int* _sizes, const _Tp& value); + //! copy/conversion contructor. If m is of different type, it's converted + Mat_(const Mat& m); + //! copy constructor + Mat_(const Mat_& m); + //! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type + Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP); + //! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type + Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0); + //! selects a submatrix + Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all()); + //! selects a submatrix + Mat_(const Mat_& m, const Rect& roi); + //! selects a submatrix, n-dim version + Mat_(const Mat_& m, const Range* ranges); + //! selects a submatrix, n-dim version + Mat_(const Mat_& m, const std::vector& ranges); + //! from a matrix expression + explicit Mat_(const MatExpr& e); + //! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column + explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false); + template explicit Mat_(const Vec::channel_type, n>& vec, bool copyData=true); + template explicit Mat_(const Matx::channel_type, m, n>& mtx, bool copyData=true); + explicit Mat_(const Point_::channel_type>& pt, bool copyData=true); + explicit Mat_(const Point3_::channel_type>& pt, bool copyData=true); + explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer); + + Mat_& operator = (const Mat& m); + Mat_& operator = (const Mat_& m); + //! set all the elements to s. + Mat_& operator = (const _Tp& s); + //! assign a matrix expression + Mat_& operator = (const MatExpr& e); + + //! iterators; they are smart enough to skip gaps in the end of rows + iterator begin(); + iterator end(); + const_iterator begin() const; + const_iterator end() const; + + //! template methods for for operation over all matrix elements. + // the operations take care of skipping gaps in the end of rows (if any) + template void forEach(const Functor& operation); + template void forEach(const Functor& operation) const; + + //! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type) + void create(int _rows, int _cols); + //! equivalent to Mat::create(_size, DataType<_Tp>::type) + void create(Size _size); + //! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type) + void create(int _ndims, const int* _sizes); + //! cross-product + Mat_ cross(const Mat_& m) const; + //! data type conversion + template operator Mat_() const; + //! overridden forms of Mat::row() etc. + Mat_ row(int y) const; + Mat_ col(int x) const; + Mat_ diag(int d=0) const; + Mat_ clone() const; + + //! overridden forms of Mat::elemSize() etc. + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1(int i=0) const; + //! returns step()/sizeof(_Tp) + size_t stepT(int i=0) const; + + //! overridden forms of Mat::zeros() etc. Data type is omitted, of course + static MatExpr zeros(int rows, int cols); + static MatExpr zeros(Size size); + static MatExpr zeros(int _ndims, const int* _sizes); + static MatExpr ones(int rows, int cols); + static MatExpr ones(Size size); + static MatExpr ones(int _ndims, const int* _sizes); + static MatExpr eye(int rows, int cols); + static MatExpr eye(Size size); + + //! some more overriden methods + Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright ); + Mat_ operator()( const Range& rowRange, const Range& colRange ) const; + Mat_ operator()( const Rect& roi ) const; + Mat_ operator()( const Range* ranges ) const; + Mat_ operator()(const std::vector& ranges) const; + + //! more convenient forms of row and element access operators + _Tp* operator [](int y); + const _Tp* operator [](int y) const; + + //! returns reference to the specified element + _Tp& operator ()(const int* idx); + //! returns read-only reference to the specified element + const _Tp& operator ()(const int* idx) const; + + //! returns reference to the specified element + template _Tp& operator ()(const Vec& idx); + //! returns read-only reference to the specified element + template const _Tp& operator ()(const Vec& idx) const; + + //! returns reference to the specified element (1D case) + _Tp& operator ()(int idx0); + //! returns read-only reference to the specified element (1D case) + const _Tp& operator ()(int idx0) const; + //! returns reference to the specified element (2D case) + _Tp& operator ()(int row, int col); + //! returns read-only reference to the specified element (2D case) + const _Tp& operator ()(int row, int col) const; + //! returns reference to the specified element (3D case) + _Tp& operator ()(int idx0, int idx1, int idx2); + //! returns read-only reference to the specified element (3D case) + const _Tp& operator ()(int idx0, int idx1, int idx2) const; + + _Tp& operator ()(Point pt); + const _Tp& operator ()(Point pt) const; + + //! conversion to vector. + operator std::vector<_Tp>() const; + //! conversion to Vec + template operator Vec::channel_type, n>() const; + //! conversion to Matx + template operator Matx::channel_type, m, n>() const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Mat_(Mat_&& m); + Mat_& operator = (Mat_&& m); + + Mat_(Mat&& m); + Mat_& operator = (Mat&& m); + + Mat_(MatExpr&& e); +#endif +}; + +typedef Mat_ Mat1b; +typedef Mat_ Mat2b; +typedef Mat_ Mat3b; +typedef Mat_ Mat4b; + +typedef Mat_ Mat1s; +typedef Mat_ Mat2s; +typedef Mat_ Mat3s; +typedef Mat_ Mat4s; + +typedef Mat_ Mat1w; +typedef Mat_ Mat2w; +typedef Mat_ Mat3w; +typedef Mat_ Mat4w; + +typedef Mat_ Mat1i; +typedef Mat_ Mat2i; +typedef Mat_ Mat3i; +typedef Mat_ Mat4i; + +typedef Mat_ Mat1f; +typedef Mat_ Mat2f; +typedef Mat_ Mat3f; +typedef Mat_ Mat4f; + +typedef Mat_ Mat1d; +typedef Mat_ Mat2d; +typedef Mat_ Mat3d; +typedef Mat_ Mat4d; + +/** @todo document */ +class CV_EXPORTS UMat +{ +public: + //! default constructor + UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT); + //! constructs 2D matrix of the specified size and type + // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.) + UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + //! constucts 2D matrix and fills it with the specified value _s. + UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! constructs n-dimensional matrix + UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! copy constructor + UMat(const UMat& m); + + //! creates a matrix header for a part of the bigger matrix + UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all()); + UMat(const UMat& m, const Rect& roi); + UMat(const UMat& m, const Range* ranges); + UMat(const UMat& m, const std::vector& ranges); + //! builds matrix from std::vector with or without copying the data + template explicit UMat(const std::vector<_Tp>& vec, bool copyData=false); + //! builds matrix from cv::Vec; the data is copied by default + template explicit UMat(const Vec<_Tp, n>& vec, bool copyData=true); + //! builds matrix from cv::Matx; the data is copied by default + template explicit UMat(const Matx<_Tp, m, n>& mtx, bool copyData=true); + //! builds matrix from a 2D point + template explicit UMat(const Point_<_Tp>& pt, bool copyData=true); + //! builds matrix from a 3D point + template explicit UMat(const Point3_<_Tp>& pt, bool copyData=true); + //! builds matrix from comma initializer + template explicit UMat(const MatCommaInitializer_<_Tp>& commaInitializer); + + //! destructor - calls release() + ~UMat(); + //! assignment operators + UMat& operator = (const UMat& m); + + Mat getMat(int flags) const; + + //! returns a new matrix header for the specified row + UMat row(int y) const; + //! returns a new matrix header for the specified column + UMat col(int x) const; + //! ... for the specified row span + UMat rowRange(int startrow, int endrow) const; + UMat rowRange(const Range& r) const; + //! ... for the specified column span + UMat colRange(int startcol, int endcol) const; + UMat colRange(const Range& r) const; + //! ... for the specified diagonal + // (d=0 - the main diagonal, + // >0 - a diagonal from the lower half, + // <0 - a diagonal from the upper half) + UMat diag(int d=0) const; + //! constructs a square diagonal matrix which main diagonal is vector "d" + static UMat diag(const UMat& d); + + //! returns deep copy of the matrix, i.e. the data is copied + UMat clone() const; + //! copies the matrix content to "m". + // It calls m.create(this->size(), this->type()). + void copyTo( OutputArray m ) const; + //! copies those matrix elements to "m" that are marked with non-zero mask elements. + void copyTo( OutputArray m, InputArray mask ) const; + //! converts matrix to another datatype with optional scalng. See cvConvertScale. + void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; + + void assignTo( UMat& m, int type=-1 ) const; + + //! sets every matrix element to s + UMat& operator = (const Scalar& s); + //! sets some of the matrix elements to s, according to the mask + UMat& setTo(InputArray value, InputArray mask=noArray()); + //! creates alternative matrix header for the same data, with different + // number of channels and/or different number of rows. see cvReshape. + UMat reshape(int cn, int rows=0) const; + UMat reshape(int cn, int newndims, const int* newsz) const; + + //! matrix transposition by means of matrix expressions + UMat t() const; + //! matrix inversion by means of matrix expressions + UMat inv(int method=DECOMP_LU) const; + //! per-element matrix multiplication by means of matrix expressions + UMat mul(InputArray m, double scale=1) const; + + //! computes dot-product + double dot(InputArray m) const; + + //! Matlab-style matrix initialization + static UMat zeros(int rows, int cols, int type); + static UMat zeros(Size size, int type); + static UMat zeros(int ndims, const int* sz, int type); + static UMat ones(int rows, int cols, int type); + static UMat ones(Size size, int type); + static UMat ones(int ndims, const int* sz, int type); + static UMat eye(int rows, int cols, int type); + static UMat eye(Size size, int type); + + //! allocates new matrix data unless the matrix already has specified size and type. + // previous data is unreferenced if needed. + void create(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(const std::vector& sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! increases the reference counter; use with care to avoid memleaks + void addref(); + //! decreases reference counter; + // deallocates the data when reference counter reaches 0. + void release(); + + //! deallocates the matrix data + void deallocate(); + //! internal use function; properly re-allocates _size, _step arrays + void copySize(const UMat& m); + + //! locates matrix header within a parent matrix. See below + void locateROI( Size& wholeSize, Point& ofs ) const; + //! moves/resizes the current matrix ROI inside the parent matrix. + UMat& adjustROI( int dtop, int dbottom, int dleft, int dright ); + //! extracts a rectangular sub-matrix + // (this is a generalized form of row, rowRange etc.) + UMat operator()( Range rowRange, Range colRange ) const; + UMat operator()( const Rect& roi ) const; + UMat operator()( const Range* ranges ) const; + UMat operator()(const std::vector& ranges) const; + + //! returns true iff the matrix data is continuous + // (i.e. when there are no gaps between successive rows). + // similar to CV_IS_MAT_CONT(cvmat->type) + bool isContinuous() const; + + //! returns true if the matrix is a submatrix of another matrix + bool isSubmatrix() const; + + //! returns element size in bytes, + // similar to CV_ELEM_SIZE(cvmat->type) + size_t elemSize() const; + //! returns the size of element channel in bytes. + size_t elemSize1() const; + //! returns element type, similar to CV_MAT_TYPE(cvmat->type) + int type() const; + //! returns element type, similar to CV_MAT_DEPTH(cvmat->type) + int depth() const; + //! returns element type, similar to CV_MAT_CN(cvmat->type) + int channels() const; + //! returns step/elemSize1() + size_t step1(int i=0) const; + //! returns true if matrix data is NULL + bool empty() const; + //! returns the total number of matrix elements + size_t total() const; + + //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise + int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; + +#ifdef CV_CXX_MOVE_SEMANTICS + UMat(UMat&& m); + UMat& operator = (UMat&& m); +#endif + + void* handle(int accessFlags) const; + void ndoffset(size_t* ofs) const; + + enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; + enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + //! the matrix dimensionality, >= 2 + int dims; + //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + int rows, cols; + + //! custom allocator + MatAllocator* allocator; + UMatUsageFlags usageFlags; // usage flags for allocator + //! and the standard allocator + static MatAllocator* getStdAllocator(); + + // black-box container of UMat data + UMatData* u; + + // offset of the submatrix (or 0) + size_t offset; + + MatSize size; + MatStep step; + +protected: +}; + + +/////////////////////////// multi-dimensional sparse matrix ////////////////////////// + +/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays. + +Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only +non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its +stored elements can actually become 0. It is up to you to detect such elements and delete them +using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is +filled so that the search time is O(1) in average (regardless of whether element is there or not). +Elements can be accessed using the following methods: +- Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and + SparseMat::find), for example: + @code + const int dims = 5; + int size[5] = {10, 10, 10, 10, 10}; + SparseMat sparse_mat(dims, size, CV_32F); + for(int i = 0; i < 1000; i++) + { + int idx[dims]; + for(int k = 0; k < dims; k++) + idx[k] = rand() % size[k]; + sparse_mat.ref(idx) += 1.f; + } + cout << "nnz = " << sparse_mat.nzcount() << endl; + @endcode +- Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator. + That is, the iteration loop is familiar to STL users: + @code + // prints elements of a sparse floating-point matrix + // and the sum of elements. + SparseMatConstIterator_ + it = sparse_mat.begin(), + it_end = sparse_mat.end(); + double s = 0; + int dims = sparse_mat.dims(); + for(; it != it_end; ++it) + { + // print element indices and the element value + const SparseMat::Node* n = it.node(); + printf("("); + for(int i = 0; i < dims; i++) + printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")"); + printf(": %g\n", it.value()); + s += *it; + } + printf("Element sum is %g\n", s); + @endcode + If you run this loop, you will notice that elements are not enumerated in a logical order + (lexicographical, and so on). They come in the same order as they are stored in the hash table + (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. + Note, however, that pointers to the nodes may become invalid when you add more elements to the + matrix. This may happen due to possible buffer reallocation. +- Combination of the above 2 methods when you need to process 2 or more sparse matrices + simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 + floating-point sparse matrices: + @code + double cross_corr(const SparseMat& a, const SparseMat& b) + { + const SparseMat *_a = &a, *_b = &b; + // if b contains less elements than a, + // it is faster to iterate through b + if(_a->nzcount() > _b->nzcount()) + std::swap(_a, _b); + SparseMatConstIterator_ it = _a->begin(), + it_end = _a->end(); + double ccorr = 0; + for(; it != it_end; ++it) + { + // take the next element from the first matrix + float avalue = *it; + const Node* anode = it.node(); + // and try to find an element with the same index in the second matrix. + // since the hash value depends only on the element index, + // reuse the hash value stored in the node + float bvalue = _b->value(anode->idx,&anode->hashval); + ccorr += avalue*bvalue; + } + return ccorr; + } + @endcode + */ +class CV_EXPORTS SparseMat +{ +public: + typedef SparseMatIterator iterator; + typedef SparseMatConstIterator const_iterator; + + enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 }; + + //! the sparse matrix header + struct CV_EXPORTS Hdr + { + Hdr(int _dims, const int* _sizes, int _type); + void clear(); + int refcount; + int dims; + int valueOffset; + size_t nodeSize; + size_t nodeCount; + size_t freeList; + std::vector pool; + std::vector hashtab; + int size[MAX_DIM]; + }; + + //! sparse matrix node - element of a hash table + struct CV_EXPORTS Node + { + //! hash value + size_t hashval; + //! index of the next node in the same hash table entry + size_t next; + //! index of the matrix element + int idx[MAX_DIM]; + }; + + /** @brief Various SparseMat constructors. + */ + SparseMat(); + + /** @overload + @param dims Array dimensionality. + @param _sizes Sparce matrix size on all dementions. + @param _type Sparse matrix data type. + */ + SparseMat(int dims, const int* _sizes, int _type); + + /** @overload + @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted + to sparse representation. + */ + SparseMat(const SparseMat& m); + + /** @overload + @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted + to sparse representation. + */ + explicit SparseMat(const Mat& m); + + //! the destructor + ~SparseMat(); + + //! assignment operator. This is O(1) operation, i.e. no data is copied + SparseMat& operator = (const SparseMat& m); + //! equivalent to the corresponding constructor + SparseMat& operator = (const Mat& m); + + //! creates full copy of the matrix + SparseMat clone() const; + + //! copies all the data to the destination matrix. All the previous content of m is erased + void copyTo( SparseMat& m ) const; + //! converts sparse matrix to dense matrix. + void copyTo( Mat& m ) const; + //! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type + void convertTo( SparseMat& m, int rtype, double alpha=1 ) const; + //! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. + /*! + @param [out] m - output matrix; if it does not have a proper size or type before the operation, + it is reallocated + @param [in] rtype – desired output matrix type or, rather, the depth since the number of channels + are the same as the input has; if rtype is negative, the output matrix will have the + same type as the input. + @param [in] alpha – optional scale factor + @param [in] beta – optional delta added to the scaled values + */ + void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const; + + // not used now + void assignTo( SparseMat& m, int type=-1 ) const; + + //! reallocates sparse matrix. + /*! + If the matrix already had the proper size and type, + it is simply cleared with clear(), otherwise, + the old matrix is released (using release()) and the new one is allocated. + */ + void create(int dims, const int* _sizes, int _type); + //! sets all the sparse matrix elements to 0, which means clearing the hash table. + void clear(); + //! manually increments the reference counter to the header. + void addref(); + // decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated. + void release(); + + //! converts sparse matrix to the old-style representation; all the elements are copied. + //operator CvSparseMat*() const; + //! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements) + size_t elemSize() const; + //! returns elemSize()/channels() + size_t elemSize1() const; + + //! returns type of sparse matrix elements + int type() const; + //! returns the depth of sparse matrix elements + int depth() const; + //! returns the number of channels + int channels() const; + + //! returns the array of sizes, or NULL if the matrix is not allocated + const int* size() const; + //! returns the size of i-th matrix dimension (or 0) + int size(int i) const; + //! returns the matrix dimensionality + int dims() const; + //! returns the number of non-zero elements (=the number of hash table nodes) + size_t nzcount() const; + + //! computes the element hash value (1D case) + size_t hash(int i0) const; + //! computes the element hash value (2D case) + size_t hash(int i0, int i1) const; + //! computes the element hash value (3D case) + size_t hash(int i0, int i1, int i2) const; + //! computes the element hash value (nD case) + size_t hash(const int* idx) const; + + //!@{ + /*! + specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case. + return pointer to the matrix element. + - if the element is there (it's non-zero), the pointer to it is returned + - if it's not there and createMissing=false, NULL pointer is returned + - if it's not there and createMissing=true, then the new element + is created and initialized with 0. Pointer to it is returned + - if the optional hashval pointer is not NULL, the element hash value is + not computed, but *hashval is taken instead. + */ + //! returns pointer to the specified element (1D case) + uchar* ptr(int i0, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (2D case) + uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (3D case) + uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (nD case) + uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0); + //!@} + + //!@{ + /*! + return read-write reference to the specified sparse matrix element. + + `ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`. + The methods always return a valid reference. + If the element did not exist, it is created and initialiazed with 0. + */ + //! returns reference to the specified element (1D case) + template _Tp& ref(int i0, size_t* hashval=0); + //! returns reference to the specified element (2D case) + template _Tp& ref(int i0, int i1, size_t* hashval=0); + //! returns reference to the specified element (3D case) + template _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); + //! returns reference to the specified element (nD case) + template _Tp& ref(const int* idx, size_t* hashval=0); + //!@} + + //!@{ + /*! + return value of the specified sparse matrix element. + + `value<_Tp>(i0,...[,hashval])` is equivalent to + @code + { const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); } + @endcode + + That is, if the element did not exist, the methods return 0. + */ + //! returns value of the specified element (1D case) + template _Tp value(int i0, size_t* hashval=0) const; + //! returns value of the specified element (2D case) + template _Tp value(int i0, int i1, size_t* hashval=0) const; + //! returns value of the specified element (3D case) + template _Tp value(int i0, int i1, int i2, size_t* hashval=0) const; + //! returns value of the specified element (nD case) + template _Tp value(const int* idx, size_t* hashval=0) const; + //!@} + + //!@{ + /*! + Return pointer to the specified sparse matrix element if it exists + + `find<_Tp>(i0,...[,hashval])` is equivalent to `(_const Tp*)ptr(i0,...false[,hashval])`. + + If the specified element does not exist, the methods return NULL. + */ + //! returns pointer to the specified element (1D case) + template const _Tp* find(int i0, size_t* hashval=0) const; + //! returns pointer to the specified element (2D case) + template const _Tp* find(int i0, int i1, size_t* hashval=0) const; + //! returns pointer to the specified element (3D case) + template const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const; + //! returns pointer to the specified element (nD case) + template const _Tp* find(const int* idx, size_t* hashval=0) const; + //!@} + + //! erases the specified element (2D case) + void erase(int i0, int i1, size_t* hashval=0); + //! erases the specified element (3D case) + void erase(int i0, int i1, int i2, size_t* hashval=0); + //! erases the specified element (nD case) + void erase(const int* idx, size_t* hashval=0); + + //!@{ + /*! + return the sparse matrix iterator pointing to the first sparse matrix element + */ + //! returns the sparse matrix iterator at the matrix beginning + SparseMatIterator begin(); + //! returns the sparse matrix iterator at the matrix beginning + template SparseMatIterator_<_Tp> begin(); + //! returns the read-only sparse matrix iterator at the matrix beginning + SparseMatConstIterator begin() const; + //! returns the read-only sparse matrix iterator at the matrix beginning + template SparseMatConstIterator_<_Tp> begin() const; + //!@} + /*! + return the sparse matrix iterator pointing to the element following the last sparse matrix element + */ + //! returns the sparse matrix iterator at the matrix end + SparseMatIterator end(); + //! returns the read-only sparse matrix iterator at the matrix end + SparseMatConstIterator end() const; + //! returns the typed sparse matrix iterator at the matrix end + template SparseMatIterator_<_Tp> end(); + //! returns the typed read-only sparse matrix iterator at the matrix end + template SparseMatConstIterator_<_Tp> end() const; + + //! returns the value stored in the sparse martix node + template _Tp& value(Node* n); + //! returns the value stored in the sparse martix node + template const _Tp& value(const Node* n) const; + + ////////////// some internal-use methods /////////////// + Node* node(size_t nidx); + const Node* node(size_t nidx) const; + + uchar* newNode(const int* idx, size_t hashval); + void removeNode(size_t hidx, size_t nidx, size_t previdx); + void resizeHashTab(size_t newsize); + + int flags; + Hdr* hdr; +}; + + + +///////////////////////////////// SparseMat_<_Tp> //////////////////////////////////// + +/** @brief Template sparse n-dimensional array class derived from SparseMat + +SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies +notation of some operations: +@code + int sz[] = {10, 20, 30}; + SparseMat_ M(3, sz); + ... + M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9); +@endcode + */ +template class SparseMat_ : public SparseMat +{ +public: + typedef SparseMatIterator_<_Tp> iterator; + typedef SparseMatConstIterator_<_Tp> const_iterator; + + //! the default constructor + SparseMat_(); + //! the full constructor equivelent to SparseMat(dims, _sizes, DataType<_Tp>::type) + SparseMat_(int dims, const int* _sizes); + //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted + SparseMat_(const SparseMat& m); + //! the copy constructor. This is O(1) operation - no data is copied + SparseMat_(const SparseMat_& m); + //! converts dense matrix to the sparse form + SparseMat_(const Mat& m); + //! converts the old-style sparse matrix to the C++ class. All the elements are copied + //SparseMat_(const CvSparseMat* m); + //! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted + SparseMat_& operator = (const SparseMat& m); + //! the assignment operator. This is O(1) operation - no data is copied + SparseMat_& operator = (const SparseMat_& m); + //! converts dense matrix to the sparse form + SparseMat_& operator = (const Mat& m); + + //! makes full copy of the matrix. All the elements are duplicated + SparseMat_ clone() const; + //! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type) + void create(int dims, const int* _sizes); + //! converts sparse matrix to the old-style CvSparseMat. All the elements are copied + //operator CvSparseMat*() const; + + //! returns type of the matrix elements + int type() const; + //! returns depth of the matrix elements + int depth() const; + //! returns the number of channels in each matrix element + int channels() const; + + //! equivalent to SparseMat::ref<_Tp>(i0, hashval) + _Tp& ref(int i0, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval) + _Tp& ref(int i0, int i1, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval) + _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(idx, hashval) + _Tp& ref(const int* idx, size_t* hashval=0); + + //! equivalent to SparseMat::value<_Tp>(i0, hashval) + _Tp operator()(int i0, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(i0, i1, hashval) + _Tp operator()(int i0, int i1, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval) + _Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(idx, hashval) + _Tp operator()(const int* idx, size_t* hashval=0) const; + + //! returns sparse matrix iterator pointing to the first sparse matrix element + SparseMatIterator_<_Tp> begin(); + //! returns read-only sparse matrix iterator pointing to the first sparse matrix element + SparseMatConstIterator_<_Tp> begin() const; + //! returns sparse matrix iterator pointing to the element following the last sparse matrix element + SparseMatIterator_<_Tp> end(); + //! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element + SparseMatConstIterator_<_Tp> end() const; +}; + + + +////////////////////////////////// MatConstIterator ////////////////////////////////// + +class CV_EXPORTS MatConstIterator +{ +public: + typedef uchar* value_type; + typedef ptrdiff_t difference_type; + typedef const uchar** pointer; + typedef uchar* reference; + +#ifndef OPENCV_NOSTL + typedef std::random_access_iterator_tag iterator_category; +#endif + + //! default constructor + MatConstIterator(); + //! constructor that sets the iterator to the beginning of the matrix + MatConstIterator(const Mat* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, const int* _idx); + //! copy constructor + MatConstIterator(const MatConstIterator& it); + + //! copy operator + MatConstIterator& operator = (const MatConstIterator& it); + //! returns the current matrix element + const uchar* operator *() const; + //! returns the i-th matrix element, relative to the current + const uchar* operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatConstIterator& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatConstIterator& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatConstIterator& operator --(); + //! decrements the iterator + MatConstIterator operator --(int); + //! increments the iterator + MatConstIterator& operator ++(); + //! increments the iterator + MatConstIterator operator ++(int); + //! returns the current iterator position + Point pos() const; + //! returns the current iterator position + void pos(int* _idx) const; + + ptrdiff_t lpos() const; + void seek(ptrdiff_t ofs, bool relative = false); + void seek(const int* _idx, bool relative = false); + + const Mat* m; + size_t elemSize; + const uchar* ptr; + const uchar* sliceStart; + const uchar* sliceEnd; +}; + + + +////////////////////////////////// MatConstIterator_ ///////////////////////////////// + +/** @brief Matrix read-only iterator + */ +template +class MatConstIterator_ : public MatConstIterator +{ +public: + typedef _Tp value_type; + typedef ptrdiff_t difference_type; + typedef const _Tp* pointer; + typedef const _Tp& reference; + +#ifndef OPENCV_NOSTL + typedef std::random_access_iterator_tag iterator_category; +#endif + + //! default constructor + MatConstIterator_(); + //! constructor that sets the iterator to the beginning of the matrix + MatConstIterator_(const Mat_<_Tp>* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx); + //! copy constructor + MatConstIterator_(const MatConstIterator_& it); + + //! copy operator + MatConstIterator_& operator = (const MatConstIterator_& it); + //! returns the current matrix element + const _Tp& operator *() const; + //! returns the i-th matrix element, relative to the current + const _Tp& operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatConstIterator_& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatConstIterator_& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatConstIterator_& operator --(); + //! decrements the iterator + MatConstIterator_ operator --(int); + //! increments the iterator + MatConstIterator_& operator ++(); + //! increments the iterator + MatConstIterator_ operator ++(int); + //! returns the current iterator position + Point pos() const; +}; + + + +//////////////////////////////////// MatIterator_ //////////////////////////////////// + +/** @brief Matrix read-write iterator +*/ +template +class MatIterator_ : public MatConstIterator_<_Tp> +{ +public: + typedef _Tp* pointer; + typedef _Tp& reference; + +#ifndef OPENCV_NOSTL + typedef std::random_access_iterator_tag iterator_category; +#endif + + //! the default constructor + MatIterator_(); + //! constructor that sets the iterator to the beginning of the matrix + MatIterator_(Mat_<_Tp>* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, const int* _idx); + //! copy constructor + MatIterator_(const MatIterator_& it); + //! copy operator + MatIterator_& operator = (const MatIterator_<_Tp>& it ); + + //! returns the current matrix element + _Tp& operator *() const; + //! returns the i-th matrix element, relative to the current + _Tp& operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatIterator_& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatIterator_& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatIterator_& operator --(); + //! decrements the iterator + MatIterator_ operator --(int); + //! increments the iterator + MatIterator_& operator ++(); + //! increments the iterator + MatIterator_ operator ++(int); +}; + + + +/////////////////////////////// SparseMatConstIterator /////////////////////////////// + +/** @brief Read-Only Sparse Matrix Iterator. + + Here is how to use the iterator to compute the sum of floating-point sparse matrix elements: + + \code + SparseMatConstIterator it = m.begin(), it_end = m.end(); + double s = 0; + CV_Assert( m.type() == CV_32F ); + for( ; it != it_end; ++it ) + s += it.value(); + \endcode +*/ +class CV_EXPORTS SparseMatConstIterator +{ +public: + //! the default constructor + SparseMatConstIterator(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatConstIterator(const SparseMat* _m); + //! the copy constructor + SparseMatConstIterator(const SparseMatConstIterator& it); + + //! the assignment operator + SparseMatConstIterator& operator = (const SparseMatConstIterator& it); + + //! template method returning the current matrix element + template const _Tp& value() const; + //! returns the current node of the sparse matrix. it.node->idx is the current element index + const SparseMat::Node* node() const; + + //! moves iterator to the previous element + SparseMatConstIterator& operator --(); + //! moves iterator to the previous element + SparseMatConstIterator operator --(int); + //! moves iterator to the next element + SparseMatConstIterator& operator ++(); + //! moves iterator to the next element + SparseMatConstIterator operator ++(int); + + //! moves iterator to the element after the last element + void seekEnd(); + + const SparseMat* m; + size_t hashidx; + uchar* ptr; +}; + + + +////////////////////////////////// SparseMatIterator ///////////////////////////////// + +/** @brief Read-write Sparse Matrix Iterator + + The class is similar to cv::SparseMatConstIterator, + but can be used for in-place modification of the matrix elements. +*/ +class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator +{ +public: + //! the default constructor + SparseMatIterator(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatIterator(SparseMat* _m); + //! the full constructor setting the iterator to the specified sparse matrix element + SparseMatIterator(SparseMat* _m, const int* idx); + //! the copy constructor + SparseMatIterator(const SparseMatIterator& it); + + //! the assignment operator + SparseMatIterator& operator = (const SparseMatIterator& it); + //! returns read-write reference to the current sparse matrix element + template _Tp& value() const; + //! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!) + SparseMat::Node* node() const; + + //! moves iterator to the next element + SparseMatIterator& operator ++(); + //! moves iterator to the next element + SparseMatIterator operator ++(int); +}; + + + +/////////////////////////////// SparseMatConstIterator_ ////////////////////////////// + +/** @brief Template Read-Only Sparse Matrix Iterator Class. + + This is the derived from SparseMatConstIterator class that + introduces more convenient operator *() for accessing the current element. +*/ +template class SparseMatConstIterator_ : public SparseMatConstIterator +{ +public: + +#ifndef OPENCV_NOSTL + typedef std::forward_iterator_tag iterator_category; +#endif + + //! the default constructor + SparseMatConstIterator_(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatConstIterator_(const SparseMat_<_Tp>* _m); + SparseMatConstIterator_(const SparseMat* _m); + //! the copy constructor + SparseMatConstIterator_(const SparseMatConstIterator_& it); + + //! the assignment operator + SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it); + //! the element access operator + const _Tp& operator *() const; + + //! moves iterator to the next element + SparseMatConstIterator_& operator ++(); + //! moves iterator to the next element + SparseMatConstIterator_ operator ++(int); +}; + + + +///////////////////////////////// SparseMatIterator_ ///////////////////////////////// + +/** @brief Template Read-Write Sparse Matrix Iterator Class. + + This is the derived from cv::SparseMatConstIterator_ class that + introduces more convenient operator *() for accessing the current element. +*/ +template class SparseMatIterator_ : public SparseMatConstIterator_<_Tp> +{ +public: + +#ifndef OPENCV_NOSTL + typedef std::forward_iterator_tag iterator_category; +#endif + + //! the default constructor + SparseMatIterator_(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatIterator_(SparseMat_<_Tp>* _m); + SparseMatIterator_(SparseMat* _m); + //! the copy constructor + SparseMatIterator_(const SparseMatIterator_& it); + + //! the assignment operator + SparseMatIterator_& operator = (const SparseMatIterator_& it); + //! returns the reference to the current element + _Tp& operator *() const; + + //! moves the iterator to the next element + SparseMatIterator_& operator ++(); + //! moves the iterator to the next element + SparseMatIterator_ operator ++(int); +}; + + + +/////////////////////////////////// NAryMatIterator ////////////////////////////////// + +/** @brief n-ary multi-dimensional array iterator. + +Use the class to implement unary, binary, and, generally, n-ary element-wise operations on +multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some +may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of +the iterators after each small operations may be a big overhead. In this case consider using +NAryMatIterator to iterate through several matrices simultaneously as long as they have the same +geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`, +`it.planes[1]`,... will be the slices of the corresponding matrices. + +The example below illustrates how you can compute a normalized and threshold 3D color histogram: +@code + void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb) + { + const int histSize[] = {N, N, N}; + + // make sure that the histogram has a proper size and type + hist.create(3, histSize, CV_32F); + + // and clear it + hist = Scalar(0); + + // the loop below assumes that the image + // is a 8-bit 3-channel. check it. + CV_Assert(image.type() == CV_8UC3); + MatConstIterator_ it = image.begin(), + it_end = image.end(); + for( ; it != it_end; ++it ) + { + const Vec3b& pix = *it; + hist.at(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f; + } + + minProb *= image.rows*image.cols; + + // initialize iterator (the style is different from STL). + // after initialization the iterator will contain + // the number of slices or planes the iterator will go through. + // it simultaneously increments iterators for several matrices + // supplied as a null terminated list of pointers + const Mat* arrays[] = {&hist, 0}; + Mat planes[1]; + NAryMatIterator itNAry(arrays, planes, 1); + double s = 0; + // iterate through the matrix. on each iteration + // itNAry.planes[i] (of type Mat) will be set to the current plane + // of the i-th n-dim matrix passed to the iterator constructor. + for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) + { + threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO); + s += sum(itNAry.planes[0])[0]; + } + + s = 1./s; + itNAry = NAryMatIterator(arrays, planes, 1); + for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) + itNAry.planes[0] *= s; + } +@endcode + */ +class CV_EXPORTS NAryMatIterator +{ +public: + //! the default constructor + NAryMatIterator(); + //! the full constructor taking arbitrary number of n-dim matrices + NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1); + //! the full constructor taking arbitrary number of n-dim matrices + NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1); + //! the separate iterator initialization method + void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1); + + //! proceeds to the next plane of every iterated matrix + NAryMatIterator& operator ++(); + //! proceeds to the next plane of every iterated matrix (postfix increment operator) + NAryMatIterator operator ++(int); + + //! the iterated arrays + const Mat** arrays; + //! the current planes + Mat* planes; + //! data pointers + uchar** ptrs; + //! the number of arrays + int narrays; + //! the number of hyper-planes that the iterator steps through + size_t nplanes; + //! the size of each segment (in elements) + size_t size; +protected: + int iterdepth; + size_t idx; +}; + + + +///////////////////////////////// Matrix Expressions ///////////////////////////////// + +class CV_EXPORTS MatOp +{ +public: + MatOp(); + virtual ~MatOp(); + + virtual bool elementWise(const MatExpr& expr) const; + virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0; + virtual void roi(const MatExpr& expr, const Range& rowRange, + const Range& colRange, MatExpr& res) const; + virtual void diag(const MatExpr& expr, int d, MatExpr& res) const; + virtual void augAssignAdd(const MatExpr& expr, Mat& m) const; + virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const; + virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const; + virtual void augAssignDivide(const MatExpr& expr, Mat& m) const; + virtual void augAssignAnd(const MatExpr& expr, Mat& m) const; + virtual void augAssignOr(const MatExpr& expr, Mat& m) const; + virtual void augAssignXor(const MatExpr& expr, Mat& m) const; + + virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const; + + virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; + + virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; + virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const; + + virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; + virtual void divide(double s, const MatExpr& expr, MatExpr& res) const; + + virtual void abs(const MatExpr& expr, MatExpr& res) const; + + virtual void transpose(const MatExpr& expr, MatExpr& res) const; + virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void invert(const MatExpr& expr, int method, MatExpr& res) const; + + virtual Size size(const MatExpr& expr) const; + virtual int type(const MatExpr& expr) const; +}; + +/** @brief Matrix expression representation +@anchor MatrixExpressions +This is a list of implemented matrix operations that can be combined in arbitrary complex +expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a +real-valued scalar ( double )): +- Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A` +- Scaling: `A*alpha` +- Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A` +- Matrix multiplication: `A*B` +- Transposition: `A.t()` (means AT) +- Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: + `A.inv([method]) (~ A-1)`, `A.inv([method])*B (~ X: AX=B)` +- Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of + `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose + elements are set to 255 (if the particular element or pair of elements satisfy the condition) or + 0. +- Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of + `&`, `|`, `^`. +- Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)` +- Element-wise absolute value: `abs(A)` +- Cross-product, dot-product: `A.cross(B)`, `A.dot(B)` +- Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm, + mean, sum, countNonZero, trace, determinant, repeat, and others. +- Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated + initializers, matrix constructors and operators that extract sub-matrices (see Mat description). +- Mat_() constructors to cast the result to the proper type. +@note Comma-separated initializers and probably some other operations may require additional +explicit Mat() or Mat_() constructor calls to resolve a possible ambiguity. + +Here are examples of matrix expressions: +@code + // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD) + SVD svd(A); + Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t(); + + // compute the new vector of parameters in the Levenberg-Marquardt algorithm + x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err); + + // sharpen image using "unsharp mask" algorithm + Mat blurred; double sigma = 1, threshold = 5, amount = 1; + GaussianBlur(img, blurred, Size(), sigma, sigma); + Mat lowContrastMask = abs(img - blurred) < threshold; + Mat sharpened = img*(1+amount) + blurred*(-amount); + img.copyTo(sharpened, lowContrastMask); +@endcode +*/ +class CV_EXPORTS MatExpr +{ +public: + MatExpr(); + explicit MatExpr(const Mat& m); + + MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(), + const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar()); + + operator Mat() const; + template operator Mat_<_Tp>() const; + + Size size() const; + int type() const; + + MatExpr row(int y) const; + MatExpr col(int x) const; + MatExpr diag(int d = 0) const; + MatExpr operator()( const Range& rowRange, const Range& colRange ) const; + MatExpr operator()( const Rect& roi ) const; + + MatExpr t() const; + MatExpr inv(int method = DECOMP_LU) const; + MatExpr mul(const MatExpr& e, double scale=1) const; + MatExpr mul(const Mat& m, double scale=1) const; + + Mat cross(const Mat& m) const; + double dot(const Mat& m) const; + + const MatOp* op; + int flags; + + Mat a, b, c; + double alpha, beta; + Scalar s; +}; + +//! @} core_basic + +//! @relates cv::MatExpr +//! @{ +CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a); +CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s); +CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e); +CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a); +CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s); +CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e); +CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator - (const Mat& m); +CV_EXPORTS MatExpr operator - (const MatExpr& e); + +CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator * (const Mat& a, double s); +CV_EXPORTS MatExpr operator * (double s, const Mat& a); +CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator * (const MatExpr& e, double s); +CV_EXPORTS MatExpr operator * (double s, const MatExpr& e); +CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator / (const Mat& a, double s); +CV_EXPORTS MatExpr operator / (double s, const Mat& a); +CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator / (const MatExpr& e, double s); +CV_EXPORTS MatExpr operator / (double s, const MatExpr& e); +CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator < (const Mat& a, double s); +CV_EXPORTS MatExpr operator < (double s, const Mat& a); + +CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator <= (const Mat& a, double s); +CV_EXPORTS MatExpr operator <= (double s, const Mat& a); + +CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator == (const Mat& a, double s); +CV_EXPORTS MatExpr operator == (double s, const Mat& a); + +CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator != (const Mat& a, double s); +CV_EXPORTS MatExpr operator != (double s, const Mat& a); + +CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator >= (const Mat& a, double s); +CV_EXPORTS MatExpr operator >= (double s, const Mat& a); + +CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator > (const Mat& a, double s); +CV_EXPORTS MatExpr operator > (double s, const Mat& a); + +CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator ~(const Mat& m); + +CV_EXPORTS MatExpr min(const Mat& a, const Mat& b); +CV_EXPORTS MatExpr min(const Mat& a, double s); +CV_EXPORTS MatExpr min(double s, const Mat& a); + +CV_EXPORTS MatExpr max(const Mat& a, const Mat& b); +CV_EXPORTS MatExpr max(const Mat& a, double s); +CV_EXPORTS MatExpr max(double s, const Mat& a); + +/** @brief Calculates an absolute value of each matrix element. + +abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms: +- C = abs(A-B) is equivalent to `absdiff(A, B, C)` +- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)` +- C = `Mat_ >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha, +beta)` + +The output matrix has the same size and the same type as the input one except for the last case, +where C is depth=CV_8U . +@param m matrix. +@sa @ref MatrixExpressions, absdiff, convertScaleAbs + */ +CV_EXPORTS MatExpr abs(const Mat& m); +/** @overload +@param e matrix expression. +*/ +CV_EXPORTS MatExpr abs(const MatExpr& e); +//! @} relates cv::MatExpr + +} // cv + +#include "opencv2/core/mat.inl.hpp" + +#endif // OPENCV_CORE_MAT_HPP diff --git a/thirdparty/linux/include/opencv2/core/mat.inl.hpp b/thirdparty/linux/include/opencv2/core/mat.inl.hpp new file mode 100644 index 0000000..4a32de1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/mat.inl.hpp @@ -0,0 +1,3733 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MATRIX_OPERATIONS_HPP +#define OPENCV_CORE_MATRIX_OPERATIONS_HPP + +#ifndef __cplusplus +# error mat.inl.hpp header must be compiled as C++ +#endif + +namespace cv +{ + +//! @cond IGNORED + +//////////////////////// Input/Output Arrays //////////////////////// + +inline void _InputArray::init(int _flags, const void* _obj) +{ flags = _flags; obj = (void*)_obj; } + +inline void _InputArray::init(int _flags, const void* _obj, Size _sz) +{ flags = _flags; obj = (void*)_obj; sz = _sz; } + +inline void* _InputArray::getObj() const { return obj; } +inline int _InputArray::getFlags() const { return flags; } +inline Size _InputArray::getSz() const { return sz; } + +inline _InputArray::_InputArray() { init(NONE, 0); } +inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); } +inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); } +inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); } +inline _InputArray::_InputArray(const UMat& m) { init(UMAT+ACCESS_READ, &m); } +inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + DataType<_Tp>::type + ACCESS_READ, &vec); } + +inline +_InputArray::_InputArray(const std::vector& vec) +{ init(FIXED_TYPE + STD_BOOL_VECTOR + DataType::type + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_READ, &mtx, Size(n, m)); } + +template inline +_InputArray::_InputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_READ, vec, Size(n, 1)); } + +template inline +_InputArray::_InputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + DataType<_Tp>::type + ACCESS_READ, &m); } + +inline _InputArray::_InputArray(const double& val) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); } + +inline _InputArray::_InputArray(const MatExpr& expr) +{ init(FIXED_TYPE + FIXED_SIZE + EXPR + ACCESS_READ, &expr); } + +inline _InputArray::_InputArray(const cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_READ, &d_mat); } + +inline _InputArray::_InputArray(const std::vector& d_mat) +{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_READ, &d_mat);} + +inline _InputArray::_InputArray(const ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_READ, &buf); } + +inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_READ, &cuda_mem); } + +inline _InputArray::~_InputArray() {} + +inline Mat _InputArray::getMat(int i) const +{ + if( kind() == MAT && i < 0 ) + return *(const Mat*)obj; + return getMat_(i); +} + +inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; } +inline bool _InputArray::isUMat() const { return kind() == _InputArray::UMAT; } +inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; } +inline bool _InputArray::isUMatVector() const { return kind() == _InputArray::STD_VECTOR_UMAT; } +inline bool _InputArray::isMatx() const { return kind() == _InputArray::MATX; } +inline bool _InputArray::isVector() const { return kind() == _InputArray::STD_VECTOR || kind() == _InputArray::STD_BOOL_VECTOR; } +inline bool _InputArray::isGpuMatVector() const { return kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT; } + +//////////////////////////////////////////////////////////////////////////////////////// + +inline _OutputArray::_OutputArray() { init(ACCESS_WRITE, 0); } +inline _OutputArray::_OutputArray(int _flags, void* _obj) { init(_flags|ACCESS_WRITE, _obj); } +inline _OutputArray::_OutputArray(Mat& m) { init(MAT+ACCESS_WRITE, &m); } +inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_WRITE, &vec); } +inline _OutputArray::_OutputArray(UMat& m) { init(UMAT+ACCESS_WRITE, &m); } +inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + DataType<_Tp>::type + ACCESS_WRITE, &vec); } + +inline +_OutputArray::_OutputArray(std::vector&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an output array\n"); } + +template inline +_OutputArray::_OutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + DataType<_Tp>::type + ACCESS_WRITE, &m); } + +template inline +_OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_WRITE, &mtx, Size(n, m)); } + +template inline +_OutputArray::_OutputArray(_Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_WRITE, vec, Size(n, 1)); } + +template inline +_OutputArray::_OutputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + DataType<_Tp>::type + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + DataType<_Tp>::type + ACCESS_WRITE, &m); } + +template inline +_OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_WRITE, &mtx, Size(n, m)); } + +template inline +_OutputArray::_OutputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_WRITE, vec, Size(n, 1)); } + +inline _OutputArray::_OutputArray(cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } + +inline _OutputArray::_OutputArray(std::vector& d_mat) +{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_WRITE, &d_mat);} + +inline _OutputArray::_OutputArray(ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_WRITE, &buf); } + +inline _OutputArray::_OutputArray(cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } + +inline _OutputArray::_OutputArray(const Mat& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_WRITE, &m); } + +inline _OutputArray::_OutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_WRITE, &vec); } + +inline _OutputArray::_OutputArray(const UMat& m) +{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_WRITE, &m); } + +inline _OutputArray::_OutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_WRITE, &vec); } + +inline _OutputArray::_OutputArray(const cuda::GpuMat& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } + + +inline _OutputArray::_OutputArray(const ogl::Buffer& buf) +{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_WRITE, &buf); } + +inline _OutputArray::_OutputArray(const cuda::HostMem& cuda_mem) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } + +/////////////////////////////////////////////////////////////////////////////////////////// + +inline _InputOutputArray::_InputOutputArray() { init(ACCESS_RW, 0); } +inline _InputOutputArray::_InputOutputArray(int _flags, void* _obj) { init(_flags|ACCESS_RW, _obj); } +inline _InputOutputArray::_InputOutputArray(Mat& m) { init(MAT+ACCESS_RW, &m); } +inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_RW, &vec); } +inline _InputOutputArray::_InputOutputArray(UMat& m) { init(UMAT+ACCESS_RW, &m); } +inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + DataType<_Tp>::type + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(std::vector&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an input/output array\n"); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + DataType<_Tp>::type + ACCESS_RW, &m); } + +template inline +_InputOutputArray::_InputOutputArray(Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_RW, &mtx, Size(n, m)); } + +template inline +_InputOutputArray::_InputOutputArray(_Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_RW, vec, Size(n, 1)); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + DataType<_Tp>::type + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + DataType<_Tp>::type + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + DataType<_Tp>::type + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + DataType<_Tp>::type + ACCESS_RW, &m); } + +template inline +_InputOutputArray::_InputOutputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_RW, &mtx, Size(n, m)); } + +template inline +_InputOutputArray::_InputOutputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type + ACCESS_RW, vec, Size(n, 1)); } + +inline _InputOutputArray::_InputOutputArray(cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_RW, &d_mat); } + +inline _InputOutputArray::_InputOutputArray(ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_RW, &buf); } + +inline _InputOutputArray::_InputOutputArray(cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } + +inline _InputOutputArray::_InputOutputArray(const Mat& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_RW, &m); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(const UMat& m) +{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_RW, &m); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(const cuda::GpuMat& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_RW, &d_mat); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} + +template<> inline _InputOutputArray::_InputOutputArray(std::vector& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} + +inline _InputOutputArray::_InputOutputArray(const ogl::Buffer& buf) +{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_RW, &buf); } + +inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } + +//////////////////////////////////////////// Mat ////////////////////////////////////////// + +inline +Mat::Mat() + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{} + +inline +Mat::Mat(int _rows, int _cols, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{ + create(_rows, _cols, _type); +} + +inline +Mat::Mat(int _rows, int _cols, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{ + create(_rows, _cols, _type); + *this = _s; +} + +inline +Mat::Mat(Size _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{ + create( _sz.height, _sz.width, _type ); +} + +inline +Mat::Mat(Size _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{ + create(_sz.height, _sz.width, _type); + *this = _s; +} + +inline +Mat::Mat(int _dims, const int* _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{ + create(_dims, _sz, _type); +} + +inline +Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{ + create(_dims, _sz, _type); + *this = _s; +} + +inline +Mat::Mat(const std::vector& _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{ + create(_sz, _type); +} + +inline +Mat::Mat(const std::vector& _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows) +{ + create(_sz, _type); + *this = _s; +} + +inline +Mat::Mat(const Mat& m) + : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), + datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), + u(m.u), size(&rows) +{ + if( u ) + CV_XADD(&u->refcount, 1); + if( m.dims <= 2 ) + { + step[0] = m.step[0]; step[1] = m.step[1]; + } + else + { + dims = 0; + copySize(m); + } +} + +inline +Mat::Mat(int _rows, int _cols, int _type, void* _data, size_t _step) + : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_rows), cols(_cols), + data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), + allocator(0), u(0), size(&rows) +{ + CV_Assert(total() == 0 || data != NULL); + + size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); + size_t minstep = cols * esz; + if( _step == AUTO_STEP ) + { + _step = minstep; + flags |= CONTINUOUS_FLAG; + } + else + { + if( rows == 1 ) _step = minstep; + CV_DbgAssert( _step >= minstep ); + + if (_step % esz1 != 0) + { + CV_Error(Error::BadStep, "Step must be a multiple of esz1"); + } + + flags |= _step == minstep ? CONTINUOUS_FLAG : 0; + } + step[0] = _step; + step[1] = esz; + datalimit = datastart + _step * rows; + dataend = datalimit - _step + minstep; +} + +inline +Mat::Mat(Size _sz, int _type, void* _data, size_t _step) + : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_sz.height), cols(_sz.width), + data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), + allocator(0), u(0), size(&rows) +{ + CV_Assert(total() == 0 || data != NULL); + + size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); + size_t minstep = cols*esz; + if( _step == AUTO_STEP ) + { + _step = minstep; + flags |= CONTINUOUS_FLAG; + } + else + { + if( rows == 1 ) _step = minstep; + CV_DbgAssert( _step >= minstep ); + + if (_step % esz1 != 0) + { + CV_Error(Error::BadStep, "Step must be a multiple of esz1"); + } + + flags |= _step == minstep ? CONTINUOUS_FLAG : 0; + } + step[0] = _step; + step[1] = esz; + datalimit = datastart + _step*rows; + dataend = datalimit - _step + minstep; +} + +template inline +Mat::Mat(const std::vector<_Tp>& vec, bool copyData) + : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), + cols(1), data(0), datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + if(vec.empty()) + return; + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&vec[0]; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat((int)vec.size(), 1, DataType<_Tp>::type, (uchar*)&vec[0]).copyTo(*this); +} + +template inline +Mat::Mat(const Vec<_Tp, n>& vec, bool copyData) + : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0), + datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)vec.val; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat(n, 1, DataType<_Tp>::type, (void*)vec.val).copyTo(*this); +} + + +template inline +Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData) + : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0), + datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + if( !copyData ) + { + step[0] = cols * sizeof(_Tp); + step[1] = sizeof(_Tp); + datastart = data = (uchar*)M.val; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat(m, n, DataType<_Tp>::type, (uchar*)M.val).copyTo(*this); +} + +template inline +Mat::Mat(const Point_<_Tp>& pt, bool copyData) + : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0), + datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&pt.x; + datalimit = dataend = datastart + rows * step[0]; + } + else + { + create(2, 1, DataType<_Tp>::type); + ((_Tp*)data)[0] = pt.x; + ((_Tp*)data)[1] = pt.y; + } +} + +template inline +Mat::Mat(const Point3_<_Tp>& pt, bool copyData) + : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0), + datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&pt.x; + datalimit = dataend = datastart + rows * step[0]; + } + else + { + create(3, 1, DataType<_Tp>::type); + ((_Tp*)data)[0] = pt.x; + ((_Tp*)data)[1] = pt.y; + ((_Tp*)data)[2] = pt.z; + } +} + +template inline +Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer) + : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0), + datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + *this = commaInitializer.operator Mat_<_Tp>(); +} + +inline +Mat::~Mat() +{ + release(); + if( step.p != step.buf ) + fastFree(step.p); +} + +inline +Mat& Mat::operator = (const Mat& m) +{ + if( this != &m ) + { + if( m.u ) + CV_XADD(&m.u->refcount, 1); + release(); + flags = m.flags; + if( dims <= 2 && m.dims <= 2 ) + { + dims = m.dims; + rows = m.rows; + cols = m.cols; + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + copySize(m); + data = m.data; + datastart = m.datastart; + dataend = m.dataend; + datalimit = m.datalimit; + allocator = m.allocator; + u = m.u; + } + return *this; +} + +inline +Mat Mat::row(int y) const +{ + return Mat(*this, Range(y, y + 1), Range::all()); +} + +inline +Mat Mat::col(int x) const +{ + return Mat(*this, Range::all(), Range(x, x + 1)); +} + +inline +Mat Mat::rowRange(int startrow, int endrow) const +{ + return Mat(*this, Range(startrow, endrow), Range::all()); +} + +inline +Mat Mat::rowRange(const Range& r) const +{ + return Mat(*this, r, Range::all()); +} + +inline +Mat Mat::colRange(int startcol, int endcol) const +{ + return Mat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +Mat Mat::colRange(const Range& r) const +{ + return Mat(*this, Range::all(), r); +} + +inline +Mat Mat::clone() const +{ + Mat m; + copyTo(m); + return m; +} + +inline +void Mat::assignTo( Mat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void Mat::create(int _rows, int _cols, int _type) +{ + _type &= TYPE_MASK; + if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && data ) + return; + int sz[] = {_rows, _cols}; + create(2, sz, _type); +} + +inline +void Mat::create(Size _sz, int _type) +{ + create(_sz.height, _sz.width, _type); +} + +inline +void Mat::addref() +{ + if( u ) + CV_XADD(&u->refcount, 1); +} + +inline +void Mat::release() +{ + if( u && CV_XADD(&u->refcount, -1) == 1 ) + deallocate(); + u = NULL; + datastart = dataend = datalimit = data = 0; + for(int i = 0; i < dims; i++) + size.p[i] = 0; +#ifdef _DEBUG + flags = MAGIC_VAL; + dims = rows = cols = 0; + if(step.p != step.buf) + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } +#endif +} + +inline +Mat Mat::operator()( Range _rowRange, Range _colRange ) const +{ + return Mat(*this, _rowRange, _colRange); +} + +inline +Mat Mat::operator()( const Rect& roi ) const +{ + return Mat(*this, roi); +} + +inline +Mat Mat::operator()(const Range* ranges) const +{ + return Mat(*this, ranges); +} + +inline +Mat Mat::operator()(const std::vector& ranges) const +{ + return Mat(*this, ranges); +} + +inline +bool Mat::isContinuous() const +{ + return (flags & CONTINUOUS_FLAG) != 0; +} + +inline +bool Mat::isSubmatrix() const +{ + return (flags & SUBMATRIX_FLAG) != 0; +} + +inline +size_t Mat::elemSize() const +{ + return dims > 0 ? step.p[dims - 1] : 0; +} + +inline +size_t Mat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int Mat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int Mat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int Mat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t Mat::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +inline +bool Mat::empty() const +{ + return data == 0 || total() == 0; +} + +inline +size_t Mat::total() const +{ + if( dims <= 2 ) + return (size_t)rows * cols; + size_t p = 1; + for( int i = 0; i < dims; i++ ) + p *= size[i]; + return p; +} + +inline +uchar* Mat::ptr(int y) +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return data + step.p[0] * y; +} + +inline +const uchar* Mat::ptr(int y) const +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return data + step.p[0] * y; +} + +template inline +_Tp* Mat::ptr(int y) +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return (_Tp*)(data + step.p[0] * y); +} + +template inline +const _Tp* Mat::ptr(int y) const +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && data && (unsigned)y < (unsigned)size.p[0]) ); + return (const _Tp*)(data + step.p[0] * y); +} + +inline +uchar* Mat::ptr(int i0, int i1) +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return data + i0 * step.p[0] + i1 * step.p[1]; +} + +inline +const uchar* Mat::ptr(int i0, int i1) const +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return data + i0 * step.p[0] + i1 * step.p[1]; +} + +template inline +_Tp* Mat::ptr(int i0, int i1) +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); +} + +template inline +const _Tp* Mat::ptr(int i0, int i1) const +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); +} + +inline +uchar* Mat::ptr(int i0, int i1, int i2) +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; +} + +inline +const uchar* Mat::ptr(int i0, int i1, int i2) const +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; +} + +template inline +_Tp* Mat::ptr(int i0, int i1, int i2) +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); +} + +template inline +const _Tp* Mat::ptr(int i0, int i1, int i2) const +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); +} + +inline +uchar* Mat::ptr(const int* idx) +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return p; +} + +inline +const uchar* Mat::ptr(const int* idx) const +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return p; +} + +template inline +_Tp& Mat::at(int i0, int i1) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); + return ((_Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +const _Tp& Mat::at(int i0, int i1) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); + return ((const _Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +_Tp& Mat::at(Point pt) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); + return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +const _Tp& Mat::at(Point pt) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); + return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +_Tp& Mat::at(int i0) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); + CV_DbgAssert(elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type)); + if( isContinuous() || size.p[0] == 1 ) + return ((_Tp*)data)[i0]; + if( size.p[1] == 1 ) + return *(_Tp*)(data + step.p[0] * i0); + int i = i0 / cols, j = i0 - i * cols; + return ((_Tp*)(data + step.p[0] * i))[j]; +} + +template inline +const _Tp& Mat::at(int i0) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); + CV_DbgAssert(elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type)); + if( isContinuous() || size.p[0] == 1 ) + return ((const _Tp*)data)[i0]; + if( size.p[1] == 1 ) + return *(const _Tp*)(data + step.p[0] * i0); + int i = i0 / cols, j = i0 - i * cols; + return ((const _Tp*)(data + step.p[0] * i))[j]; +} + +template inline +_Tp& Mat::at(int i0, int i1, int i2) +{ + CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); + return *(_Tp*)ptr(i0, i1, i2); +} + +template inline +const _Tp& Mat::at(int i0, int i1, int i2) const +{ + CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); + return *(const _Tp*)ptr(i0, i1, i2); +} + +template inline +_Tp& Mat::at(const int* idx) +{ + CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); + return *(_Tp*)ptr(idx); +} + +template inline +const _Tp& Mat::at(const int* idx) const +{ + CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); + return *(const _Tp*)ptr(idx); +} + +template inline +_Tp& Mat::at(const Vec& idx) +{ + CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); + return *(_Tp*)ptr(idx.val); +} + +template inline +const _Tp& Mat::at(const Vec& idx) const +{ + CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); + return *(const _Tp*)ptr(idx.val); +} + +template inline +MatConstIterator_<_Tp> Mat::begin() const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return MatConstIterator_<_Tp>((const Mat_<_Tp>*)this); +} + +template inline +MatConstIterator_<_Tp> Mat::end() const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this); + it += total(); + return it; +} + +template inline +MatIterator_<_Tp> Mat::begin() +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return MatIterator_<_Tp>((Mat_<_Tp>*)this); +} + +template inline +MatIterator_<_Tp> Mat::end() +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + MatIterator_<_Tp> it((Mat_<_Tp>*)this); + it += total(); + return it; +} + +template inline +void Mat::forEach(const Functor& operation) { + this->forEach_impl<_Tp>(operation); +} + +template inline +void Mat::forEach(const Functor& operation) const { + // call as not const + (const_cast(this))->forEach(operation); +} + +template inline +Mat::operator std::vector<_Tp>() const +{ + std::vector<_Tp> v; + copyTo(v); + return v; +} + +template inline +Mat::operator Vec<_Tp, n>() const +{ + CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) && + rows + cols - 1 == n && channels() == 1 ); + + if( isContinuous() && type() == DataType<_Tp>::type ) + return Vec<_Tp, n>((_Tp*)data); + Vec<_Tp, n> v; + Mat tmp(rows, cols, DataType<_Tp>::type, v.val); + convertTo(tmp, tmp.type()); + return v; +} + +template inline +Mat::operator Matx<_Tp, m, n>() const +{ + CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 ); + + if( isContinuous() && type() == DataType<_Tp>::type ) + return Matx<_Tp, m, n>((_Tp*)data); + Matx<_Tp, m, n> mtx; + Mat tmp(rows, cols, DataType<_Tp>::type, mtx.val); + convertTo(tmp, tmp.type()); + return mtx; +} + +template inline +void Mat::push_back(const _Tp& elem) +{ + if( !data ) + { + *this = Mat(1, 1, DataType<_Tp>::type, (void*)&elem).clone(); + return; + } + CV_Assert(DataType<_Tp>::type == type() && cols == 1 + /* && dims == 2 (cols == 1 implies dims == 2) */); + const uchar* tmp = dataend + step[0]; + if( !isSubmatrix() && isContinuous() && tmp <= datalimit ) + { + *(_Tp*)(data + (size.p[0]++) * step.p[0]) = elem; + dataend = tmp; + } + else + push_back_(&elem); +} + +template inline +void Mat::push_back(const Mat_<_Tp>& m) +{ + push_back((const Mat&)m); +} + +template<> inline +void Mat::push_back(const MatExpr& expr) +{ + push_back(static_cast(expr)); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +inline +Mat::Mat(Mat&& m) + : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), + datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), + u(m.u), size(&rows) +{ + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; + m.allocator = NULL; + m.u = NULL; +} + +inline +Mat& Mat::operator = (Mat&& m) +{ + if (this == &m) + return *this; + + release(); + flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; data = m.data; + datastart = m.datastart; dataend = m.dataend; datalimit = m.datalimit; allocator = m.allocator; + u = m.u; + if (step.p != step.buf) // release self step/size + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; + m.allocator = NULL; + m.u = NULL; + return *this; +} + +#endif + + +///////////////////////////// MatSize //////////////////////////// + +inline +MatSize::MatSize(int* _p) + : p(_p) {} + +inline +Size MatSize::operator()() const +{ + CV_DbgAssert(p[-1] <= 2); + return Size(p[1], p[0]); +} + +inline +const int& MatSize::operator[](int i) const +{ + return p[i]; +} + +inline +int& MatSize::operator[](int i) +{ + return p[i]; +} + +inline +MatSize::operator const int*() const +{ + return p; +} + +inline +bool MatSize::operator == (const MatSize& sz) const +{ + int d = p[-1]; + int dsz = sz.p[-1]; + if( d != dsz ) + return false; + if( d == 2 ) + return p[0] == sz.p[0] && p[1] == sz.p[1]; + + for( int i = 0; i < d; i++ ) + if( p[i] != sz.p[i] ) + return false; + return true; +} + +inline +bool MatSize::operator != (const MatSize& sz) const +{ + return !(*this == sz); +} + + + +///////////////////////////// MatStep //////////////////////////// + +inline +MatStep::MatStep() +{ + p = buf; p[0] = p[1] = 0; +} + +inline +MatStep::MatStep(size_t s) +{ + p = buf; p[0] = s; p[1] = 0; +} + +inline +const size_t& MatStep::operator[](int i) const +{ + return p[i]; +} + +inline +size_t& MatStep::operator[](int i) +{ + return p[i]; +} + +inline MatStep::operator size_t() const +{ + CV_DbgAssert( p == buf ); + return buf[0]; +} + +inline MatStep& MatStep::operator = (size_t s) +{ + CV_DbgAssert( p == buf ); + buf[0] = s; + return *this; +} + + + +////////////////////////////// Mat_<_Tp> //////////////////////////// + +template inline +Mat_<_Tp>::Mat_() + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; +} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols) + : Mat(_rows, _cols, DataType<_Tp>::type) +{ +} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& value) + : Mat(_rows, _cols, DataType<_Tp>::type) +{ + *this = value; +} + +template inline +Mat_<_Tp>::Mat_(Size _sz) + : Mat(_sz.height, _sz.width, DataType<_Tp>::type) +{} + +template inline +Mat_<_Tp>::Mat_(Size _sz, const _Tp& value) + : Mat(_sz.height, _sz.width, DataType<_Tp>::type) +{ + *this = value; +} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz) + : Mat(_dims, _sz, DataType<_Tp>::type) +{} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s) + : Mat(_dims, _sz, DataType<_Tp>::type, Scalar(_s)) +{} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz, _Tp* _data, const size_t* _steps) + : Mat(_dims, _sz, DataType<_Tp>::type, _data, _steps) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges) + : Mat(m, ranges) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const std::vector& ranges) + : Mat(m, ranges) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat& m) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; + *this = m; +} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m) + : Mat(m) +{} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps) + : Mat(_rows, _cols, DataType<_Tp>::type, _data, steps) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange) + : Mat(m, _rowRange, _colRange) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi) + : Mat(m, roi) +{} + +template template inline +Mat_<_Tp>::Mat_(const Vec::channel_type, n>& vec, bool copyData) + : Mat(n / DataType<_Tp>::channels, 1, DataType<_Tp>::type, (void*)&vec) +{ + CV_Assert(n%DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template template inline +Mat_<_Tp>::Mat_(const Matx::channel_type, m, n>& M, bool copyData) + : Mat(m, n / DataType<_Tp>::channels, DataType<_Tp>::type, (void*)&M) +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const Point_::channel_type>& pt, bool copyData) + : Mat(2 / DataType<_Tp>::channels, 1, DataType<_Tp>::type, (void*)&pt) +{ + CV_Assert(2 % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const Point3_::channel_type>& pt, bool copyData) + : Mat(3 / DataType<_Tp>::channels, 1, DataType<_Tp>::type, (void*)&pt) +{ + CV_Assert(3 % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer) + : Mat(commaInitializer) +{} + +template inline +Mat_<_Tp>::Mat_(const std::vector<_Tp>& vec, bool copyData) + : Mat(vec, copyData) +{} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat& m) +{ + if( DataType<_Tp>::type == m.type() ) + { + Mat::operator = (m); + return *this; + } + if( DataType<_Tp>::depth == m.depth() ) + { + return (*this = m.reshape(DataType<_Tp>::channels, m.dims, 0)); + } + CV_DbgAssert(DataType<_Tp>::channels == m.channels()); + m.convertTo(*this, type()); + return *this; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat_& m) +{ + Mat::operator=(m); + return *this; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const _Tp& s) +{ + typedef typename DataType<_Tp>::vec_type VT; + Mat::operator=(Scalar((const VT&)s)); + return *this; +} + +template inline +void Mat_<_Tp>::create(int _rows, int _cols) +{ + Mat::create(_rows, _cols, DataType<_Tp>::type); +} + +template inline +void Mat_<_Tp>::create(Size _sz) +{ + Mat::create(_sz, DataType<_Tp>::type); +} + +template inline +void Mat_<_Tp>::create(int _dims, const int* _sz) +{ + Mat::create(_dims, _sz, DataType<_Tp>::type); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const +{ + return Mat_<_Tp>(Mat::cross(m)); +} + +template template inline +Mat_<_Tp>::operator Mat_() const +{ + return Mat_(*this); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::row(int y) const +{ + return Mat_(*this, Range(y, y+1), Range::all()); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::col(int x) const +{ + return Mat_(*this, Range::all(), Range(x, x+1)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::diag(int d) const +{ + return Mat_(Mat::diag(d)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::clone() const +{ + return Mat_(Mat::clone()); +} + +template inline +size_t Mat_<_Tp>::elemSize() const +{ + CV_DbgAssert( Mat::elemSize() == sizeof(_Tp) ); + return sizeof(_Tp); +} + +template inline +size_t Mat_<_Tp>::elemSize1() const +{ + CV_DbgAssert( Mat::elemSize1() == sizeof(_Tp) / DataType<_Tp>::channels ); + return sizeof(_Tp) / DataType<_Tp>::channels; +} + +template inline +int Mat_<_Tp>::type() const +{ + CV_DbgAssert( Mat::type() == DataType<_Tp>::type ); + return DataType<_Tp>::type; +} + +template inline +int Mat_<_Tp>::depth() const +{ + CV_DbgAssert( Mat::depth() == DataType<_Tp>::depth ); + return DataType<_Tp>::depth; +} + +template inline +int Mat_<_Tp>::channels() const +{ + CV_DbgAssert( Mat::channels() == DataType<_Tp>::channels ); + return DataType<_Tp>::channels; +} + +template inline +size_t Mat_<_Tp>::stepT(int i) const +{ + return step.p[i] / elemSize(); +} + +template inline +size_t Mat_<_Tp>::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright ) +{ + return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const +{ + return Mat_<_Tp>(*this, _rowRange, _colRange); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const +{ + return Mat_<_Tp>(*this, roi); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const +{ + return Mat_<_Tp>(*this, ranges); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()(const std::vector& ranges) const +{ + return Mat_<_Tp>(*this, ranges); +} + +template inline +_Tp* Mat_<_Tp>::operator [](int y) +{ + CV_DbgAssert( 0 <= y && y < rows ); + return (_Tp*)(data + y*step.p[0]); +} + +template inline +const _Tp* Mat_<_Tp>::operator [](int y) const +{ + CV_DbgAssert( 0 <= y && y < rows ); + return (const _Tp*)(data + y*step.p[0]); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0, int i1) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert(type() == DataType<_Tp>::type); + return ((_Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0, int i1) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert(type() == DataType<_Tp>::type); + return ((const _Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +_Tp& Mat_<_Tp>::operator ()(Point pt) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); + CV_DbgAssert(type() == DataType<_Tp>::type); + return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(Point pt) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); + CV_DbgAssert(type() == DataType<_Tp>::type); + return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +_Tp& Mat_<_Tp>::operator ()(const int* idx) +{ + return Mat::at<_Tp>(idx); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(const int* idx) const +{ + return Mat::at<_Tp>(idx); +} + +template template inline +_Tp& Mat_<_Tp>::operator ()(const Vec& idx) +{ + return Mat::at<_Tp>(idx); +} + +template template inline +const _Tp& Mat_<_Tp>::operator ()(const Vec& idx) const +{ + return Mat::at<_Tp>(idx); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0) +{ + return this->at<_Tp>(i0); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0) const +{ + return this->at<_Tp>(i0); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) +{ + return this->at<_Tp>(i0, i1, i2); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const +{ + return this->at<_Tp>(i0, i1, i2); +} + +template inline +Mat_<_Tp>::operator std::vector<_Tp>() const +{ + std::vector<_Tp> v; + copyTo(v); + return v; +} + +template template inline +Mat_<_Tp>::operator Vec::channel_type, n>() const +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + +#if defined _MSC_VER + const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) + return pMat->operator Vec::channel_type, n>(); +#else + return this->Mat::operator Vec::channel_type, n>(); +#endif +} + +template template inline +Mat_<_Tp>::operator Matx::channel_type, m, n>() const +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + +#if defined _MSC_VER + const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) + Matx::channel_type, m, n> res = pMat->operator Matx::channel_type, m, n>(); + return res; +#else + Matx::channel_type, m, n> res = this->Mat::operator Matx::channel_type, m, n>(); + return res; +#endif +} + +template inline +MatConstIterator_<_Tp> Mat_<_Tp>::begin() const +{ + return Mat::begin<_Tp>(); +} + +template inline +MatConstIterator_<_Tp> Mat_<_Tp>::end() const +{ + return Mat::end<_Tp>(); +} + +template inline +MatIterator_<_Tp> Mat_<_Tp>::begin() +{ + return Mat::begin<_Tp>(); +} + +template inline +MatIterator_<_Tp> Mat_<_Tp>::end() +{ + return Mat::end<_Tp>(); +} + +template template inline +void Mat_<_Tp>::forEach(const Functor& operation) { + Mat::forEach<_Tp, Functor>(operation); +} + +template template inline +void Mat_<_Tp>::forEach(const Functor& operation) const { + Mat::forEach<_Tp, Functor>(operation); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +template inline +Mat_<_Tp>::Mat_(Mat_&& m) + : Mat(m) +{ +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (Mat_&& m) +{ + Mat::operator = (m); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(Mat&& m) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; + *this = m; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (Mat&& m) +{ + if( DataType<_Tp>::type == m.type() ) + { + Mat::operator = ((Mat&&)m); + return *this; + } + if( DataType<_Tp>::depth == m.depth() ) + { + Mat::operator = ((Mat&&)m.reshape(DataType<_Tp>::channels, m.dims, 0)); + return *this; + } + CV_DbgAssert(DataType<_Tp>::channels == m.channels()); + m.convertTo(*this, type()); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(MatExpr&& e) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; + *this = Mat(e); +} + +#endif + +///////////////////////////// SparseMat ///////////////////////////// + +inline +SparseMat::SparseMat() + : flags(MAGIC_VAL), hdr(0) +{} + +inline +SparseMat::SparseMat(int _dims, const int* _sizes, int _type) + : flags(MAGIC_VAL), hdr(0) +{ + create(_dims, _sizes, _type); +} + +inline +SparseMat::SparseMat(const SparseMat& m) + : flags(m.flags), hdr(m.hdr) +{ + addref(); +} + +inline +SparseMat::~SparseMat() +{ + release(); +} + +inline +SparseMat& SparseMat::operator = (const SparseMat& m) +{ + if( this != &m ) + { + if( m.hdr ) + CV_XADD(&m.hdr->refcount, 1); + release(); + flags = m.flags; + hdr = m.hdr; + } + return *this; +} + +inline +SparseMat& SparseMat::operator = (const Mat& m) +{ + return (*this = SparseMat(m)); +} + +inline +SparseMat SparseMat::clone() const +{ + SparseMat temp; + this->copyTo(temp); + return temp; +} + +inline +void SparseMat::assignTo( SparseMat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void SparseMat::addref() +{ + if( hdr ) + CV_XADD(&hdr->refcount, 1); +} + +inline +void SparseMat::release() +{ + if( hdr && CV_XADD(&hdr->refcount, -1) == 1 ) + delete hdr; + hdr = 0; +} + +inline +size_t SparseMat::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t SparseMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int SparseMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int SparseMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int SparseMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +const int* SparseMat::size() const +{ + return hdr ? hdr->size : 0; +} + +inline +int SparseMat::size(int i) const +{ + if( hdr ) + { + CV_DbgAssert((unsigned)i < (unsigned)hdr->dims); + return hdr->size[i]; + } + return 0; +} + +inline +int SparseMat::dims() const +{ + return hdr ? hdr->dims : 0; +} + +inline +size_t SparseMat::nzcount() const +{ + return hdr ? hdr->nodeCount : 0; +} + +inline +size_t SparseMat::hash(int i0) const +{ + return (size_t)i0; +} + +inline +size_t SparseMat::hash(int i0, int i1) const +{ + return (size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1; +} + +inline +size_t SparseMat::hash(int i0, int i1, int i2) const +{ + return ((size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1) * HASH_SCALE + (unsigned)i2; +} + +inline +size_t SparseMat::hash(const int* idx) const +{ + size_t h = (unsigned)idx[0]; + if( !hdr ) + return 0; + int d = hdr->dims; + for(int i = 1; i < d; i++ ) + h = h * HASH_SCALE + (unsigned)idx[i]; + return h; +} + +template inline +_Tp& SparseMat::ref(int i0, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval); +} + +template inline +_Tp& SparseMat::ref(int i0, int i1, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval); +} + +template inline +_Tp& SparseMat::ref(int i0, int i1, int i2, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, i2, true, hashval); +} + +template inline +_Tp& SparseMat::ref(const int* idx, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(idx, true, hashval); +} + +template inline +_Tp SparseMat::value(int i0, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(int i0, int i1, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(int i0, int i1, int i2, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(const int* idx, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); + return p ? *p : _Tp(); +} + +template inline +const _Tp* SparseMat::find(int i0, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); +} + +template inline +const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); +} + +template inline +const _Tp* SparseMat::find(int i0, int i1, int i2, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); +} + +template inline +const _Tp* SparseMat::find(const int* idx, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); +} + +template inline +_Tp& SparseMat::value(Node* n) +{ + return *(_Tp*)((uchar*)n + hdr->valueOffset); +} + +template inline +const _Tp& SparseMat::value(const Node* n) const +{ + return *(const _Tp*)((const uchar*)n + hdr->valueOffset); +} + +inline +SparseMat::Node* SparseMat::node(size_t nidx) +{ + return (Node*)(void*)&hdr->pool[nidx]; +} + +inline +const SparseMat::Node* SparseMat::node(size_t nidx) const +{ + return (const Node*)(const void*)&hdr->pool[nidx]; +} + +inline +SparseMatIterator SparseMat::begin() +{ + return SparseMatIterator(this); +} + +inline +SparseMatConstIterator SparseMat::begin() const +{ + return SparseMatConstIterator(this); +} + +inline +SparseMatIterator SparseMat::end() +{ + SparseMatIterator it(this); + it.seekEnd(); + return it; +} + +inline +SparseMatConstIterator SparseMat::end() const +{ + SparseMatConstIterator it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatIterator_<_Tp> SparseMat::begin() +{ + return SparseMatIterator_<_Tp>(this); +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat::begin() const +{ + return SparseMatConstIterator_<_Tp>(this); +} + +template inline +SparseMatIterator_<_Tp> SparseMat::end() +{ + SparseMatIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat::end() const +{ + SparseMatConstIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + + + +///////////////////////////// SparseMat_ //////////////////////////// + +template inline +SparseMat_<_Tp>::SparseMat_() +{ + flags = MAGIC_VAL | DataType<_Tp>::type; +} + +template inline +SparseMat_<_Tp>::SparseMat_(int _dims, const int* _sizes) + : SparseMat(_dims, _sizes, DataType<_Tp>::type) +{} + +template inline +SparseMat_<_Tp>::SparseMat_(const SparseMat& m) +{ + if( m.type() == DataType<_Tp>::type ) + *this = (const SparseMat_<_Tp>&)m; + else + m.convertTo(*this, DataType<_Tp>::type); +} + +template inline +SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m) +{ + this->flags = m.flags; + this->hdr = m.hdr; + if( this->hdr ) + CV_XADD(&this->hdr->refcount, 1); +} + +template inline +SparseMat_<_Tp>::SparseMat_(const Mat& m) +{ + SparseMat sm(m); + *this = sm; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat_<_Tp>& m) +{ + if( this != &m ) + { + if( m.hdr ) CV_XADD(&m.hdr->refcount, 1); + release(); + flags = m.flags; + hdr = m.hdr; + } + return *this; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat& m) +{ + if( m.type() == DataType<_Tp>::type ) + return (*this = (const SparseMat_<_Tp>&)m); + m.convertTo(*this, DataType<_Tp>::type); + return *this; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const Mat& m) +{ + return (*this = SparseMat(m)); +} + +template inline +SparseMat_<_Tp> SparseMat_<_Tp>::clone() const +{ + SparseMat_<_Tp> m; + this->copyTo(m); + return m; +} + +template inline +void SparseMat_<_Tp>::create(int _dims, const int* _sizes) +{ + SparseMat::create(_dims, _sizes, DataType<_Tp>::type); +} + +template inline +int SparseMat_<_Tp>::type() const +{ + return DataType<_Tp>::type; +} + +template inline +int SparseMat_<_Tp>::depth() const +{ + return DataType<_Tp>::depth; +} + +template inline +int SparseMat_<_Tp>::channels() const +{ + return DataType<_Tp>::channels; +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, i1, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, int i1, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, i1, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, int i1, int i2, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, i1, i2, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, int i1, int i2, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, i1, i2, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(const int* idx, size_t* hashval) +{ + return SparseMat::ref<_Tp>(idx, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(const int* idx, size_t* hashval) const +{ + return SparseMat::value<_Tp>(idx, hashval); +} + +template inline +SparseMatIterator_<_Tp> SparseMat_<_Tp>::begin() +{ + return SparseMatIterator_<_Tp>(this); +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begin() const +{ + return SparseMatConstIterator_<_Tp>(this); +} + +template inline +SparseMatIterator_<_Tp> SparseMat_<_Tp>::end() +{ + SparseMatIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const +{ + SparseMatConstIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + + + +////////////////////////// MatConstIterator ///////////////////////// + +inline +MatConstIterator::MatConstIterator() + : m(0), elemSize(0), ptr(0), sliceStart(0), sliceEnd(0) +{} + +inline +MatConstIterator::MatConstIterator(const Mat* _m) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + if( m && m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + seek((const int*)0); +} + +inline +MatConstIterator::MatConstIterator(const Mat* _m, int _row, int _col) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + CV_Assert(m && m->dims <= 2); + if( m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + int idx[] = {_row, _col}; + seek(idx); +} + +inline +MatConstIterator::MatConstIterator(const Mat* _m, Point _pt) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + CV_Assert(m && m->dims <= 2); + if( m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + int idx[] = {_pt.y, _pt.x}; + seek(idx); +} + +inline +MatConstIterator::MatConstIterator(const MatConstIterator& it) + : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd) +{} + +inline +MatConstIterator& MatConstIterator::operator = (const MatConstIterator& it ) +{ + m = it.m; elemSize = it.elemSize; ptr = it.ptr; + sliceStart = it.sliceStart; sliceEnd = it.sliceEnd; + return *this; +} + +inline +const uchar* MatConstIterator::operator *() const +{ + return ptr; +} + +inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs) +{ + if( !m || ofs == 0 ) + return *this; + ptrdiff_t ofsb = ofs*elemSize; + ptr += ofsb; + if( ptr < sliceStart || sliceEnd <= ptr ) + { + ptr -= ofsb; + seek(ofs, true); + } + return *this; +} + +inline +MatConstIterator& MatConstIterator::operator -= (ptrdiff_t ofs) +{ + return (*this += -ofs); +} + +inline +MatConstIterator& MatConstIterator::operator --() +{ + if( m && (ptr -= elemSize) < sliceStart ) + { + ptr += elemSize; + seek(-1, true); + } + return *this; +} + +inline +MatConstIterator MatConstIterator::operator --(int) +{ + MatConstIterator b = *this; + *this += -1; + return b; +} + +inline +MatConstIterator& MatConstIterator::operator ++() +{ + if( m && (ptr += elemSize) >= sliceEnd ) + { + ptr -= elemSize; + seek(1, true); + } + return *this; +} + +inline MatConstIterator MatConstIterator::operator ++(int) +{ + MatConstIterator b = *this; + *this += 1; + return b; +} + + +static inline +bool operator == (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +static inline +bool operator != (const MatConstIterator& a, const MatConstIterator& b) +{ + return !(a == b); +} + +static inline +bool operator < (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr < b.ptr; +} + +static inline +bool operator > (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr > b.ptr; +} + +static inline +bool operator <= (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr <= b.ptr; +} + +static inline +bool operator >= (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr >= b.ptr; +} + +static inline +ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a) +{ + if( a.m != b.m ) + return ((size_t)(-1) >> 1); + if( a.sliceEnd == b.sliceEnd ) + return (b.ptr - a.ptr)/static_cast(b.elemSize); + + return b.lpos() - a.lpos(); +} + +static inline +MatConstIterator operator + (const MatConstIterator& a, ptrdiff_t ofs) +{ + MatConstIterator b = a; + return b += ofs; +} + +static inline +MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator& a) +{ + MatConstIterator b = a; + return b += ofs; +} + +static inline +MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs) +{ + MatConstIterator b = a; + return b += -ofs; +} + + +inline +const uchar* MatConstIterator::operator [](ptrdiff_t i) const +{ + return *(*this + i); +} + + + +///////////////////////// MatConstIterator_ ///////////////////////// + +template inline +MatConstIterator_<_Tp>::MatConstIterator_() +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m) + : MatConstIterator(_m) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col) + : MatConstIterator(_m, _row, _col) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, Point _pt) + : MatConstIterator(_m, _pt) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const MatConstIterator_& it) + : MatConstIterator(it) +{} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator = (const MatConstIterator_& it ) +{ + MatConstIterator::operator = (it); + return *this; +} + +template inline +const _Tp& MatConstIterator_<_Tp>::operator *() const +{ + return *(_Tp*)(this->ptr); +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator += (ptrdiff_t ofs) +{ + MatConstIterator::operator += (ofs); + return *this; +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator -= (ptrdiff_t ofs) +{ + return (*this += -ofs); +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator --() +{ + MatConstIterator::operator --(); + return *this; +} + +template inline +MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator --(int) +{ + MatConstIterator_ b = *this; + MatConstIterator::operator --(); + return b; +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator ++() +{ + MatConstIterator::operator ++(); + return *this; +} + +template inline +MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator ++(int) +{ + MatConstIterator_ b = *this; + MatConstIterator::operator ++(); + return b; +} + + +template inline +Point MatConstIterator_<_Tp>::pos() const +{ + if( !m ) + return Point(); + CV_DbgAssert( m->dims <= 2 ); + if( m->isContinuous() ) + { + ptrdiff_t ofs = (const _Tp*)ptr - (const _Tp*)m->data; + int y = (int)(ofs / m->cols); + int x = (int)(ofs - (ptrdiff_t)y * m->cols); + return Point(x, y); + } + else + { + ptrdiff_t ofs = (uchar*)ptr - m->data; + int y = (int)(ofs / m->step); + int x = (int)((ofs - y * m->step)/sizeof(_Tp)); + return Point(x, y); + } +} + + +template static inline +bool operator == (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +template static inline +bool operator != (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) +{ + return a.m != b.m || a.ptr != b.ptr; +} + +template static inline +MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template static inline +MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template static inline +MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a - ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template inline +const _Tp& MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const +{ + return *(_Tp*)MatConstIterator::operator [](i); +} + + + +//////////////////////////// MatIterator_ /////////////////////////// + +template inline +MatIterator_<_Tp>::MatIterator_() + : MatConstIterator_<_Tp>() +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m) + : MatConstIterator_<_Tp>(_m) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int _row, int _col) + : MatConstIterator_<_Tp>(_m, _row, _col) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, Point _pt) + : MatConstIterator_<_Tp>(_m, _pt) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, const int* _idx) + : MatConstIterator_<_Tp>(_m, _idx) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(const MatIterator_& it) + : MatConstIterator_<_Tp>(it) +{} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator = (const MatIterator_<_Tp>& it ) +{ + MatConstIterator::operator = (it); + return *this; +} + +template inline +_Tp& MatIterator_<_Tp>::operator *() const +{ + return *(_Tp*)(this->ptr); +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator += (ptrdiff_t ofs) +{ + MatConstIterator::operator += (ofs); + return *this; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator -= (ptrdiff_t ofs) +{ + MatConstIterator::operator += (-ofs); + return *this; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator --() +{ + MatConstIterator::operator --(); + return *this; +} + +template inline +MatIterator_<_Tp> MatIterator_<_Tp>::operator --(int) +{ + MatIterator_ b = *this; + MatConstIterator::operator --(); + return b; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator ++() +{ + MatConstIterator::operator ++(); + return *this; +} + +template inline +MatIterator_<_Tp> MatIterator_<_Tp>::operator ++(int) +{ + MatIterator_ b = *this; + MatConstIterator::operator ++(); + return b; +} + +template inline +_Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const +{ + return *(*this + i); +} + + +template static inline +bool operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +template static inline +bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) +{ + return a.m != b.m || a.ptr != b.ptr; +} + +template static inline +MatIterator_<_Tp> operator + (const MatIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatIterator_<_Tp>&)t; +} + +template static inline +MatIterator_<_Tp> operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatIterator_<_Tp>&)t; +} + +template static inline +MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a - ofs; + return (MatIterator_<_Tp>&)t; +} + + + +/////////////////////// SparseMatConstIterator ////////////////////// + +inline +SparseMatConstIterator::SparseMatConstIterator() + : m(0), hashidx(0), ptr(0) +{} + +inline +SparseMatConstIterator::SparseMatConstIterator(const SparseMatConstIterator& it) + : m(it.m), hashidx(it.hashidx), ptr(it.ptr) +{} + +inline SparseMatConstIterator& SparseMatConstIterator::operator = (const SparseMatConstIterator& it) +{ + if( this != &it ) + { + m = it.m; + hashidx = it.hashidx; + ptr = it.ptr; + } + return *this; +} + +template inline +const _Tp& SparseMatConstIterator::value() const +{ + return *(const _Tp*)ptr; +} + +inline +const SparseMat::Node* SparseMatConstIterator::node() const +{ + return (ptr && m && m->hdr) ? (const SparseMat::Node*)(const void*)(ptr - m->hdr->valueOffset) : 0; +} + +inline +SparseMatConstIterator SparseMatConstIterator::operator ++(int) +{ + SparseMatConstIterator it = *this; + ++*this; + return it; +} + +inline +void SparseMatConstIterator::seekEnd() +{ + if( m && m->hdr ) + { + hashidx = m->hdr->hashtab.size(); + ptr = 0; + } +} + + +static inline +bool operator == (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) +{ + return it1.m == it2.m && it1.ptr == it2.ptr; +} + +static inline +bool operator != (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) +{ + return !(it1 == it2); +} + + + +///////////////////////// SparseMatIterator ///////////////////////// + +inline +SparseMatIterator::SparseMatIterator() +{} + +inline +SparseMatIterator::SparseMatIterator(SparseMat* _m) + : SparseMatConstIterator(_m) +{} + +inline +SparseMatIterator::SparseMatIterator(const SparseMatIterator& it) + : SparseMatConstIterator(it) +{} + +inline +SparseMatIterator& SparseMatIterator::operator = (const SparseMatIterator& it) +{ + (SparseMatConstIterator&)*this = it; + return *this; +} + +template inline +_Tp& SparseMatIterator::value() const +{ + return *(_Tp*)ptr; +} + +inline +SparseMat::Node* SparseMatIterator::node() const +{ + return (SparseMat::Node*)SparseMatConstIterator::node(); +} + +inline +SparseMatIterator& SparseMatIterator::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +inline +SparseMatIterator SparseMatIterator::operator ++(int) +{ + SparseMatIterator it = *this; + ++*this; + return it; +} + + + +////////////////////// SparseMatConstIterator_ ////////////////////// + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_() +{} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat_<_Tp>* _m) + : SparseMatConstIterator(_m) +{} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat* _m) + : SparseMatConstIterator(_m) +{ + CV_Assert( _m->type() == DataType<_Tp>::type ); +} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMatConstIterator_<_Tp>& it) + : SparseMatConstIterator(it) +{} + +template inline +SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator = (const SparseMatConstIterator_<_Tp>& it) +{ + return reinterpret_cast&> + (*reinterpret_cast(this) = + reinterpret_cast(it)); +} + +template inline +const _Tp& SparseMatConstIterator_<_Tp>::operator *() const +{ + return *(const _Tp*)this->ptr; +} + +template inline +SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMatConstIterator_<_Tp>::operator ++(int) +{ + SparseMatConstIterator_<_Tp> it = *this; + SparseMatConstIterator::operator ++(); + return it; +} + + + +///////////////////////// SparseMatIterator_ //////////////////////// + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_() +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat_<_Tp>* _m) + : SparseMatConstIterator_<_Tp>(_m) +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat* _m) + : SparseMatConstIterator_<_Tp>(_m) +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(const SparseMatIterator_<_Tp>& it) + : SparseMatConstIterator_<_Tp>(it) +{} + +template inline +SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator = (const SparseMatIterator_<_Tp>& it) +{ + return reinterpret_cast&> + (*reinterpret_cast(this) = + reinterpret_cast(it)); +} + +template inline +_Tp& SparseMatIterator_<_Tp>::operator *() const +{ + return *(_Tp*)this->ptr; +} + +template inline +SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +template inline +SparseMatIterator_<_Tp> SparseMatIterator_<_Tp>::operator ++(int) +{ + SparseMatIterator_<_Tp> it = *this; + SparseMatConstIterator::operator ++(); + return it; +} + + + +//////////////////////// MatCommaInitializer_ /////////////////////// + +template inline +MatCommaInitializer_<_Tp>::MatCommaInitializer_(Mat_<_Tp>* _m) + : it(_m) +{} + +template template inline +MatCommaInitializer_<_Tp>& MatCommaInitializer_<_Tp>::operator , (T2 v) +{ + CV_DbgAssert( this->it < ((const Mat_<_Tp>*)this->it.m)->end() ); + *this->it = _Tp(v); + ++this->it; + return *this; +} + +template inline +MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() const +{ + CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() ); + return Mat_<_Tp>(*this->it.m); +} + + +template static inline +MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val) +{ + MatCommaInitializer_<_Tp> commaInitializer((Mat_<_Tp>*)&m); + return (commaInitializer, val); +} + + + +///////////////////////// Matrix Expressions //////////////////////// + +inline +Mat& Mat::operator = (const MatExpr& e) +{ + e.op->assign(e, *this); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(const MatExpr& e) +{ + e.op->assign(e, *this, DataType<_Tp>::type); +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const MatExpr& e) +{ + e.op->assign(e, *this, DataType<_Tp>::type); + return *this; +} + +template inline +MatExpr Mat_<_Tp>::zeros(int rows, int cols) +{ + return Mat::zeros(rows, cols, DataType<_Tp>::type); +} + +template inline +MatExpr Mat_<_Tp>::zeros(Size sz) +{ + return Mat::zeros(sz, DataType<_Tp>::type); +} + +template inline +MatExpr Mat_<_Tp>::ones(int rows, int cols) +{ + return Mat::ones(rows, cols, DataType<_Tp>::type); +} + +template inline +MatExpr Mat_<_Tp>::ones(Size sz) +{ + return Mat::ones(sz, DataType<_Tp>::type); +} + +template inline +MatExpr Mat_<_Tp>::eye(int rows, int cols) +{ + return Mat::eye(rows, cols, DataType<_Tp>::type); +} + +template inline +MatExpr Mat_<_Tp>::eye(Size sz) +{ + return Mat::eye(sz, DataType<_Tp>::type); +} + +inline +MatExpr::MatExpr() + : op(0), flags(0), a(Mat()), b(Mat()), c(Mat()), alpha(0), beta(0), s() +{} + +inline +MatExpr::MatExpr(const MatOp* _op, int _flags, const Mat& _a, const Mat& _b, + const Mat& _c, double _alpha, double _beta, const Scalar& _s) + : op(_op), flags(_flags), a(_a), b(_b), c(_c), alpha(_alpha), beta(_beta), s(_s) +{} + +inline +MatExpr::operator Mat() const +{ + Mat m; + op->assign(*this, m); + return m; +} + +template inline +MatExpr::operator Mat_<_Tp>() const +{ + Mat_<_Tp> m; + op->assign(*this, m, DataType<_Tp>::type); + return m; +} + + +template static inline +MatExpr min(const Mat_<_Tp>& a, const Mat_<_Tp>& b) +{ + return cv::min((const Mat&)a, (const Mat&)b); +} + +template static inline +MatExpr min(const Mat_<_Tp>& a, double s) +{ + return cv::min((const Mat&)a, s); +} + +template static inline +MatExpr min(double s, const Mat_<_Tp>& a) +{ + return cv::min((const Mat&)a, s); +} + +template static inline +MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b) +{ + return cv::max((const Mat&)a, (const Mat&)b); +} + +template static inline +MatExpr max(const Mat_<_Tp>& a, double s) +{ + return cv::max((const Mat&)a, s); +} + +template static inline +MatExpr max(double s, const Mat_<_Tp>& a) +{ + return cv::max((const Mat&)a, s); +} + +template static inline +MatExpr abs(const Mat_<_Tp>& m) +{ + return cv::abs((const Mat&)m); +} + + +static inline +Mat& operator += (Mat& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, a); + return a; +} + +static inline +const Mat& operator += (const Mat& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator += (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, (Mat&)a); + return a; +} + +static inline +Mat& operator -= (Mat& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, a); + return a; +} + +static inline +const Mat& operator -= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator -= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, (Mat&)a); + return a; +} + +static inline +Mat& operator *= (Mat& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, a); + return a; +} + +static inline +const Mat& operator *= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator *= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, (Mat&)a); + return a; +} + +static inline +Mat& operator /= (Mat& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, a); + return a; +} + +static inline +const Mat& operator /= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator /= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, (Mat&)a); + return a; +} + + +//////////////////////////////// UMat //////////////////////////////// + +inline +UMat::UMat(UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{} + +inline +UMat::UMat(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_rows, _cols, _type); +} + +inline +UMat::UMat(int _rows, int _cols, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_rows, _cols, _type); + *this = _s; +} + +inline +UMat::UMat(Size _sz, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create( _sz.height, _sz.width, _type ); +} + +inline +UMat::UMat(Size _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_sz.height, _sz.width, _type); + *this = _s; +} + +inline +UMat::UMat(int _dims, const int* _sz, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_dims, _sz, _type); +} + +inline +UMat::UMat(int _dims, const int* _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_dims, _sz, _type); + *this = _s; +} + +inline +UMat::UMat(const UMat& m) +: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), + usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) +{ + addref(); + if( m.dims <= 2 ) + { + step[0] = m.step[0]; step[1] = m.step[1]; + } + else + { + dims = 0; + copySize(m); + } +} + + +template inline +UMat::UMat(const std::vector<_Tp>& vec, bool copyData) +: flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), +cols(1), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows) +{ + if(vec.empty()) + return; + if( !copyData ) + { + // !!!TODO!!! + CV_Error(Error::StsNotImplemented, ""); + } + else + Mat((int)vec.size(), 1, DataType<_Tp>::type, (uchar*)&vec[0]).copyTo(*this); +} + + +inline +UMat& UMat::operator = (const UMat& m) +{ + if( this != &m ) + { + const_cast(m).addref(); + release(); + flags = m.flags; + if( dims <= 2 && m.dims <= 2 ) + { + dims = m.dims; + rows = m.rows; + cols = m.cols; + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + copySize(m); + allocator = m.allocator; + if (usageFlags == USAGE_DEFAULT) + usageFlags = m.usageFlags; + u = m.u; + offset = m.offset; + } + return *this; +} + +inline +UMat UMat::row(int y) const +{ + return UMat(*this, Range(y, y + 1), Range::all()); +} + +inline +UMat UMat::col(int x) const +{ + return UMat(*this, Range::all(), Range(x, x + 1)); +} + +inline +UMat UMat::rowRange(int startrow, int endrow) const +{ + return UMat(*this, Range(startrow, endrow), Range::all()); +} + +inline +UMat UMat::rowRange(const Range& r) const +{ + return UMat(*this, r, Range::all()); +} + +inline +UMat UMat::colRange(int startcol, int endcol) const +{ + return UMat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +UMat UMat::colRange(const Range& r) const +{ + return UMat(*this, Range::all(), r); +} + +inline +UMat UMat::clone() const +{ + UMat m; + copyTo(m); + return m; +} + +inline +void UMat::assignTo( UMat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void UMat::create(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) +{ + _type &= TYPE_MASK; + if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && u ) + return; + int sz[] = {_rows, _cols}; + create(2, sz, _type, _usageFlags); +} + +inline +void UMat::create(Size _sz, int _type, UMatUsageFlags _usageFlags) +{ + create(_sz.height, _sz.width, _type, _usageFlags); +} + +inline +void UMat::addref() +{ + if( u ) + CV_XADD(&(u->urefcount), 1); +} + +inline void UMat::release() +{ + if( u && CV_XADD(&(u->urefcount), -1) == 1 ) + deallocate(); + for(int i = 0; i < dims; i++) + size.p[i] = 0; + u = 0; +} + +inline +UMat UMat::operator()( Range _rowRange, Range _colRange ) const +{ + return UMat(*this, _rowRange, _colRange); +} + +inline +UMat UMat::operator()( const Rect& roi ) const +{ + return UMat(*this, roi); +} + +inline +UMat UMat::operator()(const Range* ranges) const +{ + return UMat(*this, ranges); +} + +inline +UMat UMat::operator()(const std::vector& ranges) const +{ + return UMat(*this, ranges); +} + +inline +bool UMat::isContinuous() const +{ + return (flags & CONTINUOUS_FLAG) != 0; +} + +inline +bool UMat::isSubmatrix() const +{ + return (flags & SUBMATRIX_FLAG) != 0; +} + +inline +size_t UMat::elemSize() const +{ + return dims > 0 ? step.p[dims - 1] : 0; +} + +inline +size_t UMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int UMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int UMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int UMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t UMat::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +inline +bool UMat::empty() const +{ + return u == 0 || total() == 0; +} + +inline +size_t UMat::total() const +{ + if( dims <= 2 ) + return (size_t)rows * cols; + size_t p = 1; + for( int i = 0; i < dims; i++ ) + p *= size[i]; + return p; +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +inline +UMat::UMat(UMat&& m) +: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), + usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) +{ + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.allocator = NULL; + m.u = NULL; + m.offset = 0; +} + +inline +UMat& UMat::operator = (UMat&& m) +{ + if (this == &m) + return *this; + release(); + flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; + allocator = m.allocator; usageFlags = m.usageFlags; + u = m.u; + offset = m.offset; + if (step.p != step.buf) // release self step/size + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.allocator = NULL; + m.u = NULL; + m.offset = 0; + return *this; +} + +#endif + + +inline bool UMatData::hostCopyObsolete() const { return (flags & HOST_COPY_OBSOLETE) != 0; } +inline bool UMatData::deviceCopyObsolete() const { return (flags & DEVICE_COPY_OBSOLETE) != 0; } +inline bool UMatData::deviceMemMapped() const { return (flags & DEVICE_MEM_MAPPED) != 0; } +inline bool UMatData::copyOnMap() const { return (flags & COPY_ON_MAP) != 0; } +inline bool UMatData::tempUMat() const { return (flags & TEMP_UMAT) != 0; } +inline bool UMatData::tempCopiedUMat() const { return (flags & TEMP_COPIED_UMAT) == TEMP_COPIED_UMAT; } + +inline void UMatData::markDeviceMemMapped(bool flag) +{ + if(flag) + flags |= DEVICE_MEM_MAPPED; + else + flags &= ~DEVICE_MEM_MAPPED; +} + +inline void UMatData::markHostCopyObsolete(bool flag) +{ + if(flag) + flags |= HOST_COPY_OBSOLETE; + else + flags &= ~HOST_COPY_OBSOLETE; +} +inline void UMatData::markDeviceCopyObsolete(bool flag) +{ + if(flag) + flags |= DEVICE_COPY_OBSOLETE; + else + flags &= ~DEVICE_COPY_OBSOLETE; +} + +inline UMatDataAutoLock::UMatDataAutoLock(UMatData* _u) : u(_u) { u->lock(); } +inline UMatDataAutoLock::~UMatDataAutoLock() { u->unlock(); } + +//! @endcond + +} //cv + +#endif diff --git a/thirdparty/linux/include/opencv2/core/matx.hpp b/thirdparty/linux/include/opencv2/core/matx.hpp new file mode 100644 index 0000000..0d07c3f --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/matx.hpp @@ -0,0 +1,1407 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MATX_HPP +#define OPENCV_CORE_MATX_HPP + +#ifndef __cplusplus +# error matx.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" +#include "opencv2/core/traits.hpp" +#include "opencv2/core/saturate.hpp" + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +////////////////////////////// Small Matrix /////////////////////////// + +//! @cond IGNORED +struct CV_EXPORTS Matx_AddOp {}; +struct CV_EXPORTS Matx_SubOp {}; +struct CV_EXPORTS Matx_ScaleOp {}; +struct CV_EXPORTS Matx_MulOp {}; +struct CV_EXPORTS Matx_DivOp {}; +struct CV_EXPORTS Matx_MatMulOp {}; +struct CV_EXPORTS Matx_TOp {}; +//! @endcond + +/** @brief Template class for small matrices whose type and size are known at compilation time + +If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the +M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are +available. To do an operation on Matx that is not implemented, you can easily convert the matrix to +Mat and backwards: +@code + Matx33f m(1, 2, 3, + 4, 5, 6, + 7, 8, 9); + cout << sum(Mat(m*m.t())) << endl; + @endcode + */ +template class Matx +{ +public: + enum { depth = DataType<_Tp>::depth, + rows = m, + cols = n, + channels = rows*cols, + type = CV_MAKETYPE(depth, channels), + shortdim = (m < n ? m : n) + }; + + typedef _Tp value_type; + typedef Matx<_Tp, m, n> mat_type; + typedef Matx<_Tp, shortdim, 1> diag_type; + + //! default constructor + Matx(); + + Matx(_Tp v0); //!< 1x1 matrix + Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11, + _Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11, + _Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix + explicit Matx(const _Tp* vals); //!< initialize from a plain array + + static Matx all(_Tp alpha); + static Matx zeros(); + static Matx ones(); + static Matx eye(); + static Matx diag(const diag_type& d); + static Matx randu(_Tp a, _Tp b); + static Matx randn(_Tp a, _Tp b); + + //! dot product computed with the default precision + _Tp dot(const Matx<_Tp, m, n>& v) const; + + //! dot product computed in double-precision arithmetics + double ddot(const Matx<_Tp, m, n>& v) const; + + //! conversion to another data type + template operator Matx() const; + + //! change the matrix shape + template Matx<_Tp, m1, n1> reshape() const; + + //! extract part of the matrix + template Matx<_Tp, m1, n1> get_minor(int i, int j) const; + + //! extract the matrix row + Matx<_Tp, 1, n> row(int i) const; + + //! extract the matrix column + Matx<_Tp, m, 1> col(int i) const; + + //! extract the matrix diagonal + diag_type diag() const; + + //! transpose the matrix + Matx<_Tp, n, m> t() const; + + //! invert the matrix + Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const; + + //! solve linear system + template Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const; + Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const; + + //! multiply two matrices element-wise + Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const; + + //! divide two matrices element-wise + Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const; + + //! element access + const _Tp& operator ()(int i, int j) const; + _Tp& operator ()(int i, int j); + + //! 1D element access + const _Tp& operator ()(int i) const; + _Tp& operator ()(int i); + + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp); + template Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp); + template Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp); + Matx(const Matx<_Tp, n, m>& a, Matx_TOp); + + _Tp val[m*n]; //< matrix elements +}; + +typedef Matx Matx12f; +typedef Matx Matx12d; +typedef Matx Matx13f; +typedef Matx Matx13d; +typedef Matx Matx14f; +typedef Matx Matx14d; +typedef Matx Matx16f; +typedef Matx Matx16d; + +typedef Matx Matx21f; +typedef Matx Matx21d; +typedef Matx Matx31f; +typedef Matx Matx31d; +typedef Matx Matx41f; +typedef Matx Matx41d; +typedef Matx Matx61f; +typedef Matx Matx61d; + +typedef Matx Matx22f; +typedef Matx Matx22d; +typedef Matx Matx23f; +typedef Matx Matx23d; +typedef Matx Matx32f; +typedef Matx Matx32d; + +typedef Matx Matx33f; +typedef Matx Matx33d; + +typedef Matx Matx34f; +typedef Matx Matx34d; +typedef Matx Matx43f; +typedef Matx Matx43d; + +typedef Matx Matx44f; +typedef Matx Matx44d; +typedef Matx Matx66f; +typedef Matx Matx66d; + +/*! + traits +*/ +template class DataType< Matx<_Tp, m, n> > +{ +public: + typedef Matx<_Tp, m, n> value_type; + typedef Matx::work_type, m, n> work_type; + typedef _Tp channel_type; + typedef value_type vec_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = m * n, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; +}; + +/** @brief Comma-separated Matrix Initializer +*/ +template class MatxCommaInitializer +{ +public: + MatxCommaInitializer(Matx<_Tp, m, n>* _mtx); + template MatxCommaInitializer<_Tp, m, n>& operator , (T2 val); + Matx<_Tp, m, n> operator *() const; + + Matx<_Tp, m, n>* dst; + int idx; +}; + +/* + Utility methods +*/ +template static double determinant(const Matx<_Tp, m, m>& a); +template static double trace(const Matx<_Tp, m, n>& a); +template static double norm(const Matx<_Tp, m, n>& M); +template static double norm(const Matx<_Tp, m, n>& M, int normType); + + + +/////////////////////// Vec (used as element of multi-channel images ///////////////////// + +/** @brief Template class for short numerical vectors, a partial case of Matx + +This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you +can perform basic arithmetical operations, access individual elements using [] operator etc. The +vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which +elements are dynamically allocated in the heap. + +The template takes 2 parameters: +@tparam _Tp element type +@tparam cn the number of elements + +In addition to the universal notation like Vec, you can use shorter aliases +for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec. + +It is possible to convert Vec\ to/from Point_, Vec\ to/from Point3_ , and Vec\ +to CvScalar or Scalar_. Use operator[] to access the elements of Vec. + +All the expected vector operations are also implemented: +- v1 = v2 + v3 +- v1 = v2 - v3 +- v1 = v2 \* scale +- v1 = scale \* v2 +- v1 = -v2 +- v1 += v2 and other augmenting operations +- v1 == v2, v1 != v2 +- norm(v1) (euclidean norm) +The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details. +*/ +template class Vec : public Matx<_Tp, cn, 1> +{ +public: + typedef _Tp value_type; + enum { depth = Matx<_Tp, cn, 1>::depth, + channels = cn, + type = CV_MAKETYPE(depth, channels) + }; + + //! default constructor + Vec(); + + Vec(_Tp v0); //!< 1-element vector constructor + Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor + explicit Vec(const _Tp* values); + + Vec(const Vec<_Tp, cn>& v); + + static Vec all(_Tp alpha); + + //! per-element multiplication + Vec mul(const Vec<_Tp, cn>& v) const; + + //! conjugation (makes sense for complex numbers and quaternions) + Vec conj() const; + + /*! + cross product of the two 3D vectors. + + For other dimensionalities the exception is raised + */ + Vec cross(const Vec& v) const; + //! conversion to another data type + template operator Vec() const; + + /*! element access */ + const _Tp& operator [](int i) const; + _Tp& operator[](int i); + const _Tp& operator ()(int i) const; + _Tp& operator ()(int i); + + Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp); + Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp); + template Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp); +}; + +/** @name Shorter aliases for the most popular specializations of Vec + @{ +*/ +typedef Vec Vec2b; +typedef Vec Vec3b; +typedef Vec Vec4b; + +typedef Vec Vec2s; +typedef Vec Vec3s; +typedef Vec Vec4s; + +typedef Vec Vec2w; +typedef Vec Vec3w; +typedef Vec Vec4w; + +typedef Vec Vec2i; +typedef Vec Vec3i; +typedef Vec Vec4i; +typedef Vec Vec6i; +typedef Vec Vec8i; + +typedef Vec Vec2f; +typedef Vec Vec3f; +typedef Vec Vec4f; +typedef Vec Vec6f; + +typedef Vec Vec2d; +typedef Vec Vec3d; +typedef Vec Vec4d; +typedef Vec Vec6d; +/** @} */ + +/*! + traits +*/ +template class DataType< Vec<_Tp, cn> > +{ +public: + typedef Vec<_Tp, cn> value_type; + typedef Vec::work_type, cn> work_type; + typedef _Tp channel_type; + typedef value_type vec_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = cn, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; +}; + +/** @brief Comma-separated Vec Initializer +*/ +template class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1> +{ +public: + VecCommaInitializer(Vec<_Tp, m>* _vec); + template VecCommaInitializer<_Tp, m>& operator , (T2 val); + Vec<_Tp, m> operator *() const; +}; + +template static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v); + +//! @} core_basic + +//! @cond IGNORED + +///////////////////////////////////// helper classes ///////////////////////////////////// +namespace internal +{ + +template struct Matx_DetOp +{ + double operator ()(const Matx<_Tp, m, m>& a) const + { + Matx<_Tp, m, m> temp = a; + double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0); + if( p == 0 ) + return p; + for( int i = 0; i < m; i++ ) + p *= temp(i, i); + return p; + } +}; + +template struct Matx_DetOp<_Tp, 1> +{ + double operator ()(const Matx<_Tp, 1, 1>& a) const + { + return a(0,0); + } +}; + +template struct Matx_DetOp<_Tp, 2> +{ + double operator ()(const Matx<_Tp, 2, 2>& a) const + { + return a(0,0)*a(1,1) - a(0,1)*a(1,0); + } +}; + +template struct Matx_DetOp<_Tp, 3> +{ + double operator ()(const Matx<_Tp, 3, 3>& a) const + { + return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) - + a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) + + a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1)); + } +}; + +template Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v) +{ + return Vec<_Tp, 2>(v[0], -v[1]); +} + +template Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v) +{ + return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]); +} + +} // internal + + + +////////////////////////////////// Matx Implementation /////////////////////////////////// + +template inline +Matx<_Tp, m, n>::Matx() +{ + for(int i = 0; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0) +{ + val[0] = v0; + for(int i = 1; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1) +{ + CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements."); + val[0] = v0; val[1] = v1; + for(int i = 2; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2) +{ + CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; + for(int i = 3; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3) +{ + CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + for(int i = 4; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) +{ + CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4; + for(int i = 5; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) +{ + CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; + for(int i = 6; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) +{ + CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; + for(int i = 7; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) +{ + CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + for(int i = 8; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) +{ + CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; + for(int i = 9; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) +{ + CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; + for(int i = 10; i < channels; i++) val[i] = _Tp(0); +} + + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11) +{ + CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + for(int i = 12; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) +{ + CV_StaticAssert(channels == 14, "Matx should have at least 14 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + val[12] = v12; val[13] = v13; +} + + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15) +{ + CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15; + for(int i = 16; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(const _Tp* values) +{ + for( int i = 0; i < channels; i++ ) val[i] = values[i]; +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha) +{ + Matx<_Tp, m, n> M; + for( int i = 0; i < m*n; i++ ) M.val[i] = alpha; + return M; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros() +{ + return all(0); +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::ones() +{ + return all(1); +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::eye() +{ + Matx<_Tp,m,n> M; + for(int i = 0; i < shortdim; i++) + M(i,i) = 1; + return M; +} + +template inline +_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const +{ + _Tp s = 0; + for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i]; + return s; +} + +template inline +double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const +{ + double s = 0; + for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i]; + return s; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d) +{ + Matx<_Tp,m,n> M; + for(int i = 0; i < shortdim; i++) + M(i,i) = d(i, 0); + return M; +} + +template template +inline Matx<_Tp, m, n>::operator Matx() const +{ + Matx M; + for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast(val[i]); + return M; +} + +template template inline +Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const +{ + CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements"); + return (const Matx<_Tp, m1, n1>&)*this; +} + +template +template inline +Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int i, int j) const +{ + CV_DbgAssert(0 <= i && i+m1 <= m && 0 <= j && j+n1 <= n); + Matx<_Tp, m1, n1> s; + for( int di = 0; di < m1; di++ ) + for( int dj = 0; dj < n1; dj++ ) + s(di, dj) = (*this)(i+di, j+dj); + return s; +} + +template inline +Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const +{ + CV_DbgAssert((unsigned)i < (unsigned)m); + return Matx<_Tp, 1, n>(&val[i*n]); +} + +template inline +Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const +{ + CV_DbgAssert((unsigned)j < (unsigned)n); + Matx<_Tp, m, 1> v; + for( int i = 0; i < m; i++ ) + v.val[i] = val[i*n + j]; + return v; +} + +template inline +typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const +{ + diag_type d; + for( int i = 0; i < shortdim; i++ ) + d.val[i] = val[i*n + i]; + return d; +} + +template inline +const _Tp& Matx<_Tp, m, n>::operator()(int i, int j) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); + return this->val[i*n + j]; +} + +template inline +_Tp& Matx<_Tp, m, n>::operator ()(int i, int j) +{ + CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); + return val[i*n + j]; +} + +template inline +const _Tp& Matx<_Tp, m, n>::operator ()(int i) const +{ + CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); + CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); + return val[i]; +} + +template inline +_Tp& Matx<_Tp, m, n>::operator ()(int i) +{ + CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); + CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); + return val[i]; +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]); +} + +template template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] * alpha); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]); +} + +template template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp) +{ + for( int i = 0; i < m; i++ ) + for( int j = 0; j < n; j++ ) + { + _Tp s = 0; + for( int k = 0; k < l; k++ ) + s += a(i, k) * b(k, j); + val[i*n + j] = s; + } +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp) +{ + for( int i = 0; i < m; i++ ) + for( int j = 0; j < n; j++ ) + val[i*n + j] = a(j, i); +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const +{ + return Matx<_Tp, m, n>(*this, a, Matx_MulOp()); +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const +{ + return Matx<_Tp, m, n>(*this, a, Matx_DivOp()); +} + +template inline +Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const +{ + return Matx<_Tp, n, m>(*this, Matx_TOp()); +} + +template inline +Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const +{ + Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method); + return (Vec<_Tp, n>&)(x); +} + +template static inline +double determinant(const Matx<_Tp, m, m>& a) +{ + return cv::internal::Matx_DetOp<_Tp, m>()(a); +} + +template static inline +double trace(const Matx<_Tp, m, n>& a) +{ + _Tp s = 0; + for( int i = 0; i < std::min(m, n); i++ ) + s += a(i,i); + return s; +} + +template static inline +double norm(const Matx<_Tp, m, n>& M) +{ + return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n)); +} + +template static inline +double norm(const Matx<_Tp, m, n>& M, int normType) +{ + switch(normType) { + case NORM_INF: + return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + case NORM_L1: + return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + case NORM_L2SQR: + return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + default: + case NORM_L2: + return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n)); + } +} + + + +//////////////////////////////// matx comma initializer ////////////////////////////////// + +template static inline +MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val) +{ + MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx); + return (commaInitializer, val); +} + +template inline +MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx) + : dst(_mtx), idx(0) +{} + +template template inline +MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value) +{ + CV_DbgAssert( idx < m*n ); + dst->val[idx++] = saturate_cast<_Tp>(value); + return *this; +} + +template inline +Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const +{ + CV_DbgAssert( idx == n*m ); + return *dst; +} + + + +/////////////////////////////////// Vec Implementation /////////////////////////////////// + +template inline +Vec<_Tp, cn>::Vec() {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0) + : Matx<_Tp, cn, 1>(v0) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1) + : Matx<_Tp, cn, 1>(v0, v1) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2) + : Matx<_Tp, cn, 1>(v0, v1, v2) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {} + +template inline +Vec<_Tp, cn>::Vec(const _Tp* values) + : Matx<_Tp, cn, 1>(values) {} + +template inline +Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m) + : Matx<_Tp, cn, 1>(m.val) {} + +template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op) + : Matx<_Tp, cn, 1>(a, b, op) {} + +template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op) + : Matx<_Tp, cn, 1>(a, b, op) {} + +template template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op) + : Matx<_Tp, cn, 1>(a, alpha, op) {} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha) +{ + Vec v; + for( int i = 0; i < cn; i++ ) v.val[i] = alpha; + return v; +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const +{ + Vec<_Tp, cn> w; + for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]); + return w; +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const +{ + CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined"); + return Vec<_Tp, cn>(); +} + +template<> inline +Vec Vec::cross(const Vec& v) const +{ + return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], + this->val[2]*v.val[0] - this->val[0]*v.val[2], + this->val[0]*v.val[1] - this->val[1]*v.val[0]); +} + +template<> inline +Vec Vec::cross(const Vec& v) const +{ + return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], + this->val[2]*v.val[0] - this->val[0]*v.val[2], + this->val[0]*v.val[1] - this->val[1]*v.val[0]); +} + +template template inline +Vec<_Tp, cn>::operator Vec() const +{ + Vec v; + for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast(this->val[i]); + return v; +} + +template inline +const _Tp& Vec<_Tp, cn>::operator [](int i) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +_Tp& Vec<_Tp, cn>::operator [](int i) +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +const _Tp& Vec<_Tp, cn>::operator ()(int i) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +_Tp& Vec<_Tp, cn>::operator ()(int i) +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v) +{ + double nv = norm(v); + return v * (nv ? 1./nv : 0.); +} + + + +//////////////////////////////// matx comma initializer ////////////////////////////////// + + +template static inline +VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val) +{ + VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec); + return (commaInitializer, val); +} + +template inline +VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec) + : MatxCommaInitializer<_Tp, cn, 1>(_vec) +{} + +template template inline +VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value) +{ + CV_DbgAssert( this->idx < cn ); + this->dst->val[this->idx++] = saturate_cast<_Tp>(value); + return *this; +} + +template inline +Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const +{ + CV_DbgAssert( this->idx == cn ); + return *this->dst; +} + +//! @endcond + +///////////////////////////// Matx out-of-class operators //////////////////////////////// + +//! @relates cv::Matx +//! @{ + +template static inline +Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); + return a; +} + +template static inline +Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); + return a; +} + +template static inline +Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_AddOp()); +} + +template static inline +Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_SubOp()); +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_MatMulOp()); +} + +template static inline +Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b) +{ + Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp()); + return (const Vec<_Tp, m>&)(c); +} + +template static inline +bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + if( a.val[i] != b.val[i] ) return false; + return true; +} + +template static inline +bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return !(a == b); +} + +//! @} + +////////////////////////////// Vec out-of-class operators //////////////////////////////// + +//! @relates cv::Vec +//! @{ + +template static inline +Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) +{ + for( int i = 0; i < cn; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); + return a; +} + +template static inline +Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) +{ + for( int i = 0; i < cn; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); + return a; +} + +template static inline +Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) +{ + return Vec<_Tp, cn>(a, b, Matx_AddOp()); +} + +template static inline +Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) +{ + return Vec<_Tp, cn>(a, b, Matx_SubOp()); +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha) +{ + double ialpha = 1./alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha) +{ + float ialpha = 1.f/alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha) +{ + double ialpha = 1./alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha) +{ + return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha) +{ + return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha) +{ + return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a) +{ + Vec<_Tp,cn> t; + for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]); + return t; +} + +template inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) +{ + return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]), + saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]), + saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]), + saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0])); +} + +template inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) +{ + v1 = v1 * v2; + return v1; +} + +//! @} + +} // cv + +#endif // OPENCV_CORE_MATX_HPP diff --git a/thirdparty/linux/include/opencv2/core/neon_utils.hpp b/thirdparty/linux/include/opencv2/core/neon_utils.hpp new file mode 100644 index 0000000..573ba99 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/neon_utils.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_NEON_UTILS_HPP +#define OPENCV_HAL_NEON_UTILS_HPP + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils_neon +//! @{ + +#if CV_NEON + +inline int32x2_t cv_vrnd_s32_f32(float32x2_t v) +{ + static int32x2_t v_sign = vdup_n_s32(1 << 31), + v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f)); + + int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v))); + return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition))); +} + +inline int32x4_t cv_vrndq_s32_f32(float32x4_t v) +{ + static int32x4_t v_sign = vdupq_n_s32(1 << 31), + v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); + + int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v))); + return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition))); +} + +inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v) +{ + static float32x2_t v_05 = vdup_n_f32(0.5f); + return vcvt_u32_f32(vadd_f32(v, v_05)); +} + +inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v) +{ + static float32x4_t v_05 = vdupq_n_f32(0.5f); + return vcvtq_u32_f32(vaddq_f32(v, v_05)); +} + +inline float32x4_t cv_vrecpq_f32(float32x4_t val) +{ + float32x4_t reciprocal = vrecpeq_f32(val); + reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); + return reciprocal; +} + +inline float32x2_t cv_vrecp_f32(float32x2_t val) +{ + float32x2_t reciprocal = vrecpe_f32(val); + reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); + reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); + return reciprocal; +} + +inline float32x4_t cv_vrsqrtq_f32(float32x4_t val) +{ + float32x4_t e = vrsqrteq_f32(val); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); + return e; +} + +inline float32x2_t cv_vrsqrt_f32(float32x2_t val) +{ + float32x2_t e = vrsqrte_f32(val); + e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); + e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); + return e; +} + +inline float32x4_t cv_vsqrtq_f32(float32x4_t val) +{ + return cv_vrecpq_f32(cv_vrsqrtq_f32(val)); +} + +inline float32x2_t cv_vsqrt_f32(float32x2_t val) +{ + return cv_vrecp_f32(cv_vrsqrt_f32(val)); +} + +#endif + +//! @} + +#endif // OPENCV_HAL_NEON_UTILS_HPP diff --git a/thirdparty/linux/include/opencv2/core/ocl.hpp b/thirdparty/linux/include/opencv2/core/ocl.hpp new file mode 100644 index 0000000..1a9549d --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/ocl.hpp @@ -0,0 +1,757 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPENCL_HPP +#define OPENCV_OPENCL_HPP + +#include "opencv2/core.hpp" + +namespace cv { namespace ocl { + +//! @addtogroup core_opencl +//! @{ + +CV_EXPORTS_W bool haveOpenCL(); +CV_EXPORTS_W bool useOpenCL(); +CV_EXPORTS_W bool haveAmdBlas(); +CV_EXPORTS_W bool haveAmdFft(); +CV_EXPORTS_W void setUseOpenCL(bool flag); +CV_EXPORTS_W void finish(); + +CV_EXPORTS bool haveSVM(); + +class CV_EXPORTS Context; +class CV_EXPORTS Device; +class CV_EXPORTS Kernel; +class CV_EXPORTS Program; +class CV_EXPORTS ProgramSource; +class CV_EXPORTS Queue; +class CV_EXPORTS PlatformInfo; +class CV_EXPORTS Image2D; + +class CV_EXPORTS Device +{ +public: + Device(); + explicit Device(void* d); + Device(const Device& d); + Device& operator = (const Device& d); + ~Device(); + + void set(void* d); + + enum + { + TYPE_DEFAULT = (1 << 0), + TYPE_CPU = (1 << 1), + TYPE_GPU = (1 << 2), + TYPE_ACCELERATOR = (1 << 3), + TYPE_DGPU = TYPE_GPU + (1 << 16), + TYPE_IGPU = TYPE_GPU + (1 << 17), + TYPE_ALL = 0xFFFFFFFF + }; + + String name() const; + String extensions() const; + String version() const; + String vendorName() const; + String OpenCL_C_Version() const; + String OpenCLVersion() const; + int deviceVersionMajor() const; + int deviceVersionMinor() const; + String driverVersion() const; + void* ptr() const; + + int type() const; + + int addressBits() const; + bool available() const; + bool compilerAvailable() const; + bool linkerAvailable() const; + + enum + { + FP_DENORM=(1 << 0), + FP_INF_NAN=(1 << 1), + FP_ROUND_TO_NEAREST=(1 << 2), + FP_ROUND_TO_ZERO=(1 << 3), + FP_ROUND_TO_INF=(1 << 4), + FP_FMA=(1 << 5), + FP_SOFT_FLOAT=(1 << 6), + FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7) + }; + int doubleFPConfig() const; + int singleFPConfig() const; + int halfFPConfig() const; + + bool endianLittle() const; + bool errorCorrectionSupport() const; + + enum + { + EXEC_KERNEL=(1 << 0), + EXEC_NATIVE_KERNEL=(1 << 1) + }; + int executionCapabilities() const; + + size_t globalMemCacheSize() const; + + enum + { + NO_CACHE=0, + READ_ONLY_CACHE=1, + READ_WRITE_CACHE=2 + }; + int globalMemCacheType() const; + int globalMemCacheLineSize() const; + size_t globalMemSize() const; + + size_t localMemSize() const; + enum + { + NO_LOCAL_MEM=0, + LOCAL_IS_LOCAL=1, + LOCAL_IS_GLOBAL=2 + }; + int localMemType() const; + bool hostUnifiedMemory() const; + + bool imageSupport() const; + + bool imageFromBufferSupport() const; + uint imagePitchAlignment() const; + uint imageBaseAddressAlignment() const; + + size_t image2DMaxWidth() const; + size_t image2DMaxHeight() const; + + size_t image3DMaxWidth() const; + size_t image3DMaxHeight() const; + size_t image3DMaxDepth() const; + + size_t imageMaxBufferSize() const; + size_t imageMaxArraySize() const; + + enum + { + UNKNOWN_VENDOR=0, + VENDOR_AMD=1, + VENDOR_INTEL=2, + VENDOR_NVIDIA=3 + }; + int vendorID() const; + // FIXIT + // dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform. + // This method should use platform name instead of vendor name. + // After fix restore code in arithm.cpp: ocl_compare() + inline bool isAMD() const { return vendorID() == VENDOR_AMD; } + inline bool isIntel() const { return vendorID() == VENDOR_INTEL; } + inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; } + + int maxClockFrequency() const; + int maxComputeUnits() const; + int maxConstantArgs() const; + size_t maxConstantBufferSize() const; + + size_t maxMemAllocSize() const; + size_t maxParameterSize() const; + + int maxReadImageArgs() const; + int maxWriteImageArgs() const; + int maxSamplers() const; + + size_t maxWorkGroupSize() const; + int maxWorkItemDims() const; + void maxWorkItemSizes(size_t*) const; + + int memBaseAddrAlign() const; + + int nativeVectorWidthChar() const; + int nativeVectorWidthShort() const; + int nativeVectorWidthInt() const; + int nativeVectorWidthLong() const; + int nativeVectorWidthFloat() const; + int nativeVectorWidthDouble() const; + int nativeVectorWidthHalf() const; + + int preferredVectorWidthChar() const; + int preferredVectorWidthShort() const; + int preferredVectorWidthInt() const; + int preferredVectorWidthLong() const; + int preferredVectorWidthFloat() const; + int preferredVectorWidthDouble() const; + int preferredVectorWidthHalf() const; + + size_t printfBufferSize() const; + size_t profilingTimerResolution() const; + + static const Device& getDefault(); + +protected: + struct Impl; + Impl* p; +}; + + +class CV_EXPORTS Context +{ +public: + Context(); + explicit Context(int dtype); + ~Context(); + Context(const Context& c); + Context& operator = (const Context& c); + + bool create(); + bool create(int dtype); + size_t ndevices() const; + const Device& device(size_t idx) const; + Program getProg(const ProgramSource& prog, + const String& buildopt, String& errmsg); + + static Context& getDefault(bool initialize = true); + void* ptr() const; + + friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); + + bool useSVM() const; + void setUseSVM(bool enabled); + + struct Impl; + Impl* p; +}; + +class CV_EXPORTS Platform +{ +public: + Platform(); + ~Platform(); + Platform(const Platform& p); + Platform& operator = (const Platform& p); + + void* ptr() const; + static Platform& getDefault(); + + friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); +protected: + struct Impl; + Impl* p; +}; + +/* +//! @brief Attaches OpenCL context to OpenCV +// +//! @note Note: +// OpenCV will check if available OpenCL platform has platformName name, +// then assign context to OpenCV and call clRetainContext function. +// The deviceID device will be used as target device and new command queue +// will be created. +// +// Params: +//! @param platformName - name of OpenCL platform to attach, +//! this string is used to check if platform is available +//! to OpenCV at runtime +//! @param platfromID - ID of platform attached context was created for +//! @param context - OpenCL context to be attached to OpenCV +//! @param deviceID - ID of device, must be created from attached context +*/ +CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID); + +/* +//! @brief Convert OpenCL buffer to UMat +// +//! @note Note: +// OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. +// Memory content is not copied from clBuffer to UMat. Instead, buffer handle assigned +// to UMat and clRetainMemObject is called. +// +// Params: +//! @param cl_mem_buffer - source clBuffer handle +//! @param step - num of bytes in single row +//! @param rows - number of rows +//! @param cols - number of cols +//! @param type - OpenCV type of image +//! @param dst - destination UMat +*/ +CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst); + +/* +//! @brief Convert OpenCL image2d_t to UMat +// +//! @note Note: +// OpenCL image2d_t (cl_mem_image), should be compatible with OpenCV +// UMat formats. +// Memory content is copied from image to UMat with +// clEnqueueCopyImageToBuffer function. +// +// Params: +//! @param cl_mem_image - source image2d_t handle +//! @param dst - destination UMat +*/ +CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst); + +// TODO Move to internal header +void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); + +class CV_EXPORTS Queue +{ +public: + Queue(); + explicit Queue(const Context& c, const Device& d=Device()); + ~Queue(); + Queue(const Queue& q); + Queue& operator = (const Queue& q); + + bool create(const Context& c=Context(), const Device& d=Device()); + void finish(); + void* ptr() const; + static Queue& getDefault(); + +protected: + struct Impl; + Impl* p; +}; + + +class CV_EXPORTS KernelArg +{ +public: + enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 }; + KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0); + KernelArg(); + + static KernelArg Local() { return KernelArg(LOCAL, 0); } + static KernelArg PtrWriteOnly(const UMat& m) + { return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); } + static KernelArg PtrReadOnly(const UMat& m) + { return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); } + static KernelArg PtrReadWrite(const UMat& m) + { return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); } + static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); } + static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg Constant(const Mat& m); + template static KernelArg Constant(const _Tp* arr, size_t n) + { return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); } + + int flags; + UMat* m; + const void* obj; + size_t sz; + int wscale, iwscale; +}; + + +class CV_EXPORTS Kernel +{ +public: + Kernel(); + Kernel(const char* kname, const Program& prog); + Kernel(const char* kname, const ProgramSource& prog, + const String& buildopts = String(), String* errmsg=0); + ~Kernel(); + Kernel(const Kernel& k); + Kernel& operator = (const Kernel& k); + + bool empty() const; + bool create(const char* kname, const Program& prog); + bool create(const char* kname, const ProgramSource& prog, + const String& buildopts, String* errmsg=0); + + int set(int i, const void* value, size_t sz); + int set(int i, const Image2D& image2D); + int set(int i, const UMat& m); + int set(int i, const KernelArg& arg); + template int set(int i, const _Tp& value) + { return set(i, &value, sizeof(value)); } + + template + Kernel& args(const _Tp0& a0) + { + set(0, a0); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1) + { + int i = set(0, a0); set(i, a1); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2) + { + int i = set(0, a0); i = set(i, a1); set(i, a2); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, + const _Tp3& a3, const _Tp4& a4) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); + i = set(i, a3); set(i, a4); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, + const _Tp3& a3, const _Tp4& a4, const _Tp5& a5) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); + i = set(i, a3); i = set(i, a4); set(i, a5); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); + i = set(i, a4); i = set(i, a5); set(i, a6); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); + i = set(i, a4); i = set(i, a5); i = set(i, a6); set(i, a7); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); + i = set(i, a5); i = set(i, a6); i = set(i, a7); set(i, a8); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); set(i, a9); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); set(i, a10); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); set(i, a11); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + set(i, a12); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); set(i, a13); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13, const _Tp14& a14) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); i = set(i, a13); set(i, a14); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13, const _Tp14& a14, const _Tp15& a15) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this; + } + /* + Run the OpenCL kernel. + @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. + @param globalsize work items for each dimension. + It is not the final globalsize passed to OpenCL. + Each dimension will be adjusted to the nearest integer divisible by the corresponding value in localsize. + If localsize is NULL, it will still be adjusted depending on dims. + The adjusted values are greater than or equal to the original values. + @param localsize work-group size for each dimension. + @param sync specify whether to wait for OpenCL computation to finish before return. + @param q command queue + */ + bool run(int dims, size_t globalsize[], + size_t localsize[], bool sync, const Queue& q=Queue()); + bool runTask(bool sync, const Queue& q=Queue()); + + size_t workGroupSize() const; + size_t preferedWorkGroupSizeMultiple() const; + bool compileWorkGroupSize(size_t wsz[]) const; + size_t localMemSize() const; + + void* ptr() const; + struct Impl; + +protected: + Impl* p; +}; + +class CV_EXPORTS Program +{ +public: + Program(); + Program(const ProgramSource& src, + const String& buildflags, String& errmsg); + explicit Program(const String& buf); + Program(const Program& prog); + + Program& operator = (const Program& prog); + ~Program(); + + bool create(const ProgramSource& src, + const String& buildflags, String& errmsg); + bool read(const String& buf, const String& buildflags); + bool write(String& buf) const; + + const ProgramSource& source() const; + void* ptr() const; + + String getPrefix() const; + static String getPrefix(const String& buildflags); + +protected: + struct Impl; + Impl* p; +}; + + +class CV_EXPORTS ProgramSource +{ +public: + typedef uint64 hash_t; + + ProgramSource(); + explicit ProgramSource(const String& prog); + explicit ProgramSource(const char* prog); + ~ProgramSource(); + ProgramSource(const ProgramSource& prog); + ProgramSource& operator = (const ProgramSource& prog); + + const String& source() const; + hash_t hash() const; + +protected: + struct Impl; + Impl* p; +}; + +class CV_EXPORTS PlatformInfo +{ +public: + PlatformInfo(); + explicit PlatformInfo(void* id); + ~PlatformInfo(); + + PlatformInfo(const PlatformInfo& i); + PlatformInfo& operator =(const PlatformInfo& i); + + String name() const; + String vendor() const; + String version() const; + int deviceNumber() const; + void getDevice(Device& device, int d) const; + +protected: + struct Impl; + Impl* p; +}; + +CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf); +CV_EXPORTS const char* typeToStr(int t); +CV_EXPORTS const char* memopTypeToStr(int t); +CV_EXPORTS const char* vecopTypeToStr(int t); +CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL); +CV_EXPORTS void getPlatfomsInfo(std::vector& platform_info); + + +enum OclVectorStrategy +{ + // all matrices have its own vector width + OCL_VECTOR_OWN = 0, + // all matrices have maximal vector width among all matrices + // (useful for cases when matrices have different data types) + OCL_VECTOR_MAX = 1, + + // default strategy + OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN +}; + +CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), + OclVectorStrategy strat = OCL_VECTOR_DEFAULT); + +CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths, + InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), + OclVectorStrategy strat = OCL_VECTOR_DEFAULT); + +// with OCL_VECTOR_MAX strategy +CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray()); + +CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m); + +class CV_EXPORTS Image2D +{ +public: + Image2D(); + + // src: The UMat from which to get image properties and data + // norm: Flag to enable the use of normalized channel data types + // alias: Flag indicating that the image should alias the src UMat. + // If true, changes to the image or src will be reflected in + // both objects. + explicit Image2D(const UMat &src, bool norm = false, bool alias = false); + Image2D(const Image2D & i); + ~Image2D(); + + Image2D & operator = (const Image2D & i); + + // Indicates if creating an aliased image should succeed. Depends on the + // underlying platform and the dimensions of the UMat. + static bool canCreateAlias(const UMat &u); + + // Indicates if the image format is supported. + static bool isFormatSupported(int depth, int cn, bool norm); + + void* ptr() const; +protected: + struct Impl; + Impl* p; +}; + + +CV_EXPORTS MatAllocator* getOpenCLAllocator(); + + +#ifdef __OPENCV_BUILD +namespace internal { + +CV_EXPORTS bool isOpenCLForced(); +#define OCL_FORCE_CHECK(condition) (cv::ocl::internal::isOpenCLForced() || (condition)) + +CV_EXPORTS bool isPerformanceCheckBypassed(); +#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition)) + +CV_EXPORTS bool isCLBuffer(UMat& u); + +} // namespace internal +#endif + +//! @} + +}} + +#endif diff --git a/thirdparty/linux/include/opencv2/core/ocl_genbase.hpp b/thirdparty/linux/include/opencv2/core/ocl_genbase.hpp new file mode 100644 index 0000000..5408958 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/ocl_genbase.hpp @@ -0,0 +1,64 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPENCL_GENBASE_HPP +#define OPENCV_OPENCL_GENBASE_HPP + +namespace cv +{ +namespace ocl +{ + +//! @cond IGNORED + +struct ProgramEntry +{ + const char* name; + const char* programStr; + const char* programHash; +}; + +//! @endcond + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/core/opengl.hpp b/thirdparty/linux/include/opencv2/core/opengl.hpp new file mode 100644 index 0000000..8b63d6c --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/opengl.hpp @@ -0,0 +1,729 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OPENGL_HPP +#define OPENCV_CORE_OPENGL_HPP + +#ifndef __cplusplus +# error opengl.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "ocl.hpp" + +namespace cv { namespace ogl { + +/** @addtogroup core_opengl +This section describes OpenGL interoperability. + +To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is +supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not +supported). For GTK backend gtkglext-1.0 library is required. + +To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can +do this with namedWindow function or with other OpenGL toolkit (GLUT, for example). +*/ +//! @{ + +/////////////////// OpenGL Objects /////////////////// + +/** @brief Smart pointer for OpenGL buffer object with reference counting. + +Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL +context. These can be used to store vertex data, pixel data retrieved from images or the +framebuffer, and a variety of other things. + +ogl::Buffer has interface similar with Mat interface and represents 2D array memory. + +ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory. + */ +class CV_EXPORTS Buffer +{ +public: + /** @brief The target defines how you intend to use the buffer object. + */ + enum Target + { + ARRAY_BUFFER = 0x8892, //!< The buffer will be used as a source for vertex data + ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example) + PIXEL_PACK_BUFFER = 0x88EB, //!< The buffer will be used for reading from OpenGL textures + PIXEL_UNPACK_BUFFER = 0x88EC //!< The buffer will be used for writing to OpenGL textures + }; + + enum Access + { + READ_ONLY = 0x88B8, + WRITE_ONLY = 0x88B9, + READ_WRITE = 0x88BA + }; + + /** @brief The constructors. + + Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId + parameter), allocates memory for ogl::Buffer object or copies from host/device memory. + */ + Buffer(); + + /** @overload + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param abufId Buffer object name. + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param abufId Buffer object name. + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false); + + /** @overload + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Allocates memory for ogl::Buffer object. + + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Decrements the reference counter and destroys the buffer object if needed. + + The function will call setAutoRelease(true) . + */ + void release(); + + /** @brief Sets auto release mode. + + The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was + bound to a window it could be released at any time (user can close a window). If object's destructor + is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy + OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context). + This function can force ogl::Buffer destructor to destroy OpenGL object. + @param flag Auto release mode (if true, release will be called in object's destructor). + */ + void setAutoRelease(bool flag); + + /** @brief Copies from host/device memory to OpenGL buffer. + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload */ + void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object. + + @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or + ogl::Buffer ). + */ + void copyTo(OutputArray arr) const; + + /** @overload */ + void copyTo(OutputArray arr, cuda::Stream& stream) const; + + /** @brief Creates a full copy of the buffer object and the underlying data. + + @param target Buffer usage for destination buffer. + @param autoRelease Auto release mode for destination buffer. + */ + Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const; + + /** @brief Binds OpenGL buffer to the specified buffer binding point. + + @param target Binding point. See cv::ogl::Buffer::Target . + */ + void bind(Target target) const; + + /** @brief Unbind any buffers from the specified binding point. + + @param target Binding point. See cv::ogl::Buffer::Target . + */ + static void unbind(Target target); + + /** @brief Maps OpenGL buffer to host memory. + + mapHost maps to the client's address space the entire data store of the buffer object. The data can + then be directly read and/or written relative to the returned pointer, depending on the specified + access policy. + + A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used. + + This operation can lead to memory transfers between host and device. + + Only one buffer object can be mapped at a time. + @param access Access policy, indicating whether it will be possible to read from, write to, or both + read from and write to the buffer object's mapped data store. The symbolic constant must be + ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE . + */ + Mat mapHost(Access access); + + /** @brief Unmaps OpenGL buffer. + */ + void unmapHost(); + + //! map to device memory (blocking) + cuda::GpuMat mapDevice(); + void unmapDevice(); + + /** @brief Maps OpenGL buffer to CUDA device memory. + + This operatation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time. + + A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used. + */ + cuda::GpuMat mapDevice(cuda::Stream& stream); + + /** @brief Unmaps OpenGL buffer. + */ + void unmapDevice(cuda::Stream& stream); + + int rows() const; + int cols() const; + Size size() const; + bool empty() const; + + int type() const; + int depth() const; + int channels() const; + int elemSize() const; + int elemSize1() const; + + //! get OpenGL opject id + unsigned int bufId() const; + + class Impl; + +private: + Ptr impl_; + int rows_; + int cols_; + int type_; +}; + +/** @brief Smart pointer for OpenGL 2D texture memory with reference counting. + */ +class CV_EXPORTS Texture2D +{ +public: + /** @brief An Image Format describes the way that the images in Textures store their data. + */ + enum Format + { + NONE = 0, + DEPTH_COMPONENT = 0x1902, //!< Depth + RGB = 0x1907, //!< Red, Green, Blue + RGBA = 0x1908 //!< Red, Green, Blue, Alpha + }; + + /** @brief The constructors. + + Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from + host/device memory. + */ + Texture2D(); + + /** @overload */ + Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false); + + /** @overload */ + Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false); + + /** @overload + @param arows Number of rows. + @param acols Number of columns. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Texture2D(int arows, int acols, Format aformat, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Texture2D(Size asize, Format aformat, bool autoRelease = false); + + /** @overload + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + explicit Texture2D(InputArray arr, bool autoRelease = false); + + /** @brief Allocates memory for ogl::Texture2D object. + + @param arows Number of rows. + @param acols Number of columns. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(int arows, int acols, Format aformat, bool autoRelease = false); + /** @overload + @param asize 2D array size. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(Size asize, Format aformat, bool autoRelease = false); + + /** @brief Decrements the reference counter and destroys the texture object if needed. + + The function will call setAutoRelease(true) . + */ + void release(); + + /** @brief Sets auto release mode. + + @param flag Auto release mode (if true, release will be called in object's destructor). + + The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was + bound to a window it could be released at any time (user can close a window). If object's destructor + is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't + destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL + context). This function can force ogl::Texture2D destructor to destroy OpenGL object. + */ + void setAutoRelease(bool flag); + + /** @brief Copies from host/device memory to OpenGL texture. + + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void copyFrom(InputArray arr, bool autoRelease = false); + + /** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object. + + @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or + ogl::Texture2D ). + @param ddepth Destination depth. + @param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture). + */ + void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const; + + /** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target. + */ + void bind() const; + + int rows() const; + int cols() const; + Size size() const; + bool empty() const; + + Format format() const; + + //! get OpenGL opject id + unsigned int texId() const; + + class Impl; + +private: + Ptr impl_; + int rows_; + int cols_; + Format format_; +}; + +/** @brief Wrapper for OpenGL Client-Side Vertex arrays. + +ogl::Arrays stores vertex data in ogl::Buffer objects. + */ +class CV_EXPORTS Arrays +{ +public: + /** @brief Default constructor + */ + Arrays(); + + /** @brief Sets an array of vertex coordinates. + @param vertex array with vertex coordinates, can be both host and device memory. + */ + void setVertexArray(InputArray vertex); + + /** @brief Resets vertex coordinates. + */ + void resetVertexArray(); + + /** @brief Sets an array of vertex colors. + @param color array with vertex colors, can be both host and device memory. + */ + void setColorArray(InputArray color); + + /** @brief Resets vertex colors. + */ + void resetColorArray(); + + /** @brief Sets an array of vertex normals. + @param normal array with vertex normals, can be both host and device memory. + */ + void setNormalArray(InputArray normal); + + /** @brief Resets vertex normals. + */ + void resetNormalArray(); + + /** @brief Sets an array of vertex texture coordinates. + @param texCoord array with vertex texture coordinates, can be both host and device memory. + */ + void setTexCoordArray(InputArray texCoord); + + /** @brief Resets vertex texture coordinates. + */ + void resetTexCoordArray(); + + /** @brief Releases all inner buffers. + */ + void release(); + + /** @brief Sets auto release mode all inner buffers. + @param flag Auto release mode. + */ + void setAutoRelease(bool flag); + + /** @brief Binds all vertex arrays. + */ + void bind() const; + + /** @brief Returns the vertex count. + */ + int size() const; + bool empty() const; + +private: + int size_; + Buffer vertex_; + Buffer color_; + Buffer normal_; + Buffer texCoord_; +}; + +/////////////////// Render Functions /////////////////// + +//! render mode +enum RenderModes { + POINTS = 0x0000, + LINES = 0x0001, + LINE_LOOP = 0x0002, + LINE_STRIP = 0x0003, + TRIANGLES = 0x0004, + TRIANGLE_STRIP = 0x0005, + TRIANGLE_FAN = 0x0006, + QUADS = 0x0007, + QUAD_STRIP = 0x0008, + POLYGON = 0x0009 +}; + +/** @brief Render OpenGL texture or primitives. +@param tex Texture to draw. +@param wndRect Region of window, where to draw a texture (normalized coordinates). +@param texRect Region of texture to draw (normalized coordinates). + */ +CV_EXPORTS void render(const Texture2D& tex, + Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), + Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); + +/** @overload +@param arr Array of privitives vertices. +@param mode Render mode. One of cv::ogl::RenderModes +@param color Color for all vertices. Will be used if arr doesn't contain color array. +*/ +CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255)); + +/** @overload +@param arr Array of privitives vertices. +@param indices Array of vertices indices (host or device memory). +@param mode Render mode. One of cv::ogl::RenderModes +@param color Color for all vertices. Will be used if arr doesn't contain color array. +*/ +CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255)); + +/////////////////// CL-GL Interoperability Functions /////////////////// + +namespace ocl { +using namespace cv::ocl; + +// TODO static functions in the Context class +/** @brief Creates OpenCL context from GL. +@return Returns reference to OpenCL Context + */ +CV_EXPORTS Context& initializeContextFromGL(); + +} // namespace cv::ogl::ocl + +/** @brief Converts InputArray to Texture2D object. +@param src - source InputArray. +@param texture - destination Texture2D object. + */ +CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture); + +/** @brief Converts Texture2D object to OutputArray. +@param texture - source Texture2D object. +@param dst - destination OutputArray. + */ +CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst); + +/** @brief Maps Buffer object to process on CL side (convert to UMat). + +Function creates CL buffer from GL one, and then constructs UMat that can be used +to process buffer data with OpenCV functions. Note that in current implementation +UMat constructed this way doesn't own corresponding GL buffer object, so it is +the user responsibility to close down CL/GL buffers relationships by explicitly +calling unmapGLBuffer() function. +@param buffer - source Buffer object. +@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE). +@return Returns UMat object + */ +CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, int accessFlags = ACCESS_READ|ACCESS_WRITE); + +/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer). + +Function must be called explicitly by the user for each UMat previously constructed +by the call to mapGLBuffer() function. +@param u - source UMat, created by mapGLBuffer(). + */ +CV_EXPORTS void unmapGLBuffer(UMat& u); + +}} // namespace cv::ogl + +namespace cv { namespace cuda { + +//! @addtogroup cuda +//! @{ + +/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability. + +This function should be explicitly called after OpenGL context creation and before any CUDA calls. +@param device System index of a CUDA device starting with 0. +@ingroup core_opengl + */ +CV_EXPORTS void setGlDevice(int device = 0); + +//! @} + +}} + +//! @cond IGNORED + +//////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////// + +inline +cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) +{ + create(arows, acols, atype, target, autoRelease); +} + +inline +cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) +{ + create(asize, atype, target, autoRelease); +} + +inline +void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease) +{ + create(asize.height, asize.width, atype, target, autoRelease); +} + +inline +int cv::ogl::Buffer::rows() const +{ + return rows_; +} + +inline +int cv::ogl::Buffer::cols() const +{ + return cols_; +} + +inline +cv::Size cv::ogl::Buffer::size() const +{ + return Size(cols_, rows_); +} + +inline +bool cv::ogl::Buffer::empty() const +{ + return rows_ == 0 || cols_ == 0; +} + +inline +int cv::ogl::Buffer::type() const +{ + return type_; +} + +inline +int cv::ogl::Buffer::depth() const +{ + return CV_MAT_DEPTH(type_); +} + +inline +int cv::ogl::Buffer::channels() const +{ + return CV_MAT_CN(type_); +} + +inline +int cv::ogl::Buffer::elemSize() const +{ + return CV_ELEM_SIZE(type_); +} + +inline +int cv::ogl::Buffer::elemSize1() const +{ + return CV_ELEM_SIZE1(type_); +} + +/////// + +inline +cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) +{ + create(arows, acols, aformat, autoRelease); +} + +inline +cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) +{ + create(asize, aformat, autoRelease); +} + +inline +void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease) +{ + create(asize.height, asize.width, aformat, autoRelease); +} + +inline +int cv::ogl::Texture2D::rows() const +{ + return rows_; +} + +inline +int cv::ogl::Texture2D::cols() const +{ + return cols_; +} + +inline +cv::Size cv::ogl::Texture2D::size() const +{ + return Size(cols_, rows_); +} + +inline +bool cv::ogl::Texture2D::empty() const +{ + return rows_ == 0 || cols_ == 0; +} + +inline +cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const +{ + return format_; +} + +/////// + +inline +cv::ogl::Arrays::Arrays() : size_(0) +{ +} + +inline +int cv::ogl::Arrays::size() const +{ + return size_; +} + +inline +bool cv::ogl::Arrays::empty() const +{ + return size_ == 0; +} + +//! @endcond + +#endif /* OPENCV_CORE_OPENGL_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/operations.hpp b/thirdparty/linux/include/opencv2/core/operations.hpp new file mode 100644 index 0000000..4a4ad9e --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/operations.hpp @@ -0,0 +1,530 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OPERATIONS_HPP +#define OPENCV_CORE_OPERATIONS_HPP + +#ifndef __cplusplus +# error operations.hpp header must be compiled as C++ +#endif + +#include + +//! @cond IGNORED + +namespace cv +{ + +////////////////////////////// Matx methods depending on core API ///////////////////////////// + +namespace internal +{ + +template struct Matx_FastInvOp +{ + bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const + { + Matx<_Tp, m, m> temp = a; + + // assume that b is all 0's on input => make it a unity matrix + for( int i = 0; i < m; i++ ) + b(i, i) = (_Tp)1; + + if( method == DECOMP_CHOLESKY ) + return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m); + + return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0; + } +}; + +template struct Matx_FastInvOp<_Tp, 2> +{ + bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int) const + { + _Tp d = determinant(a); + if( d == 0 ) + return false; + d = 1/d; + b(1,1) = a(0,0)*d; + b(0,0) = a(1,1)*d; + b(0,1) = -a(0,1)*d; + b(1,0) = -a(1,0)*d; + return true; + } +}; + +template struct Matx_FastInvOp<_Tp, 3> +{ + bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int) const + { + _Tp d = (_Tp)determinant(a); + if( d == 0 ) + return false; + d = 1/d; + b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d; + b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d; + b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d; + + b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d; + b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d; + b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d; + + b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d; + b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d; + b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d; + return true; + } +}; + + +template struct Matx_FastSolveOp +{ + bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b, + Matx<_Tp, m, n>& x, int method) const + { + Matx<_Tp, m, m> temp = a; + x = b; + if( method == DECOMP_CHOLESKY ) + return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n); + + return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0; + } +}; + +template struct Matx_FastSolveOp<_Tp, 2, 1> +{ + bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b, + Matx<_Tp, 2, 1>& x, int) const + { + _Tp d = determinant(a); + if( d == 0 ) + return false; + d = 1/d; + x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d; + x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d; + return true; + } +}; + +template struct Matx_FastSolveOp<_Tp, 3, 1> +{ + bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b, + Matx<_Tp, 3, 1>& x, int) const + { + _Tp d = (_Tp)determinant(a); + if( d == 0 ) + return false; + d = 1/d; + x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) - + a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) + + a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2))); + + x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) - + b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) + + a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0))); + + x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) - + a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) + + b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0))); + return true; + } +}; + +} // internal + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b) +{ + Matx<_Tp,m,n> M; + cv::randu(M, Scalar(a), Scalar(b)); + return M; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) +{ + Matx<_Tp,m,n> M; + cv::randn(M, Scalar(a), Scalar(b)); + return M; +} + +template inline +Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const +{ + Matx<_Tp, n, m> b; + bool ok; + if( method == DECOMP_LU || method == DECOMP_CHOLESKY ) + ok = cv::internal::Matx_FastInvOp<_Tp, m>()(*this, b, method); + else + { + Mat A(*this, false), B(b, false); + ok = (invert(A, B, method) != 0); + } + if( NULL != p_is_ok ) { *p_is_ok = ok; } + return ok ? b : Matx<_Tp, n, m>::zeros(); +} + +template template inline +Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const +{ + Matx<_Tp, n, l> x; + bool ok; + if( method == DECOMP_LU || method == DECOMP_CHOLESKY ) + ok = cv::internal::Matx_FastSolveOp<_Tp, m, l>()(*this, rhs, x, method); + else + { + Mat A(*this, false), B(rhs, false), X(x, false); + ok = cv::solve(A, B, X, method); + } + + return ok ? x : Matx<_Tp, n, l>::zeros(); +} + + + +////////////////////////// Augmenting algebraic & logical operations ////////////////////////// + +#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + static inline A& operator op (A& a, const B& b) { cvop; return a; } + +#define CV_MAT_AUG_OPERATOR(op, cvop, A, B) \ + CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) + +#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B) \ + template CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + template CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) + +CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat) +CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>) +CV_MAT_AUG_OPERATOR (*=, a.convertTo(a, -1, b), Mat, double) +CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double) + +CV_MAT_AUG_OPERATOR (/=, cv::divide(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat_<_Tp>) +CV_MAT_AUG_OPERATOR (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double) +CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double) + +CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +#undef CV_MAT_AUG_OPERATOR_T +#undef CV_MAT_AUG_OPERATOR +#undef CV_MAT_AUG_OPERATOR1 + + + +///////////////////////////////////////////// SVD ///////////////////////////////////////////// + +inline SVD::SVD() {} +inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); } +inline void SVD::solveZ( InputArray m, OutputArray _dst ) +{ + Mat mtx = m.getMat(); + SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV)); + _dst.create(svd.vt.cols, 1, svd.vt.type()); + Mat dst = _dst.getMat(); + svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst); +} + +template inline void + SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false); + SVD::compute(_a, _w, _u, _vt); + CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]); +} + +template inline void +SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _a(a, false), _w(w, false); + SVD::compute(_a, _w); + CV_Assert(_w.data == (uchar*)&w.val[0]); +} + +template inline void +SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, + const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, + Matx<_Tp, n, nb>& dst ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false); + SVD::backSubst(_w, _u, _vt, _rhs, _dst); + CV_Assert(_dst.data == (uchar*)&dst.val[0]); +} + + + +/////////////////////////////////// Multiply-with-Carry RNG /////////////////////////////////// + +inline RNG::RNG() { state = 0xffffffff; } +inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; } + +inline RNG::operator uchar() { return (uchar)next(); } +inline RNG::operator schar() { return (schar)next(); } +inline RNG::operator ushort() { return (ushort)next(); } +inline RNG::operator short() { return (short)next(); } +inline RNG::operator int() { return (int)next(); } +inline RNG::operator unsigned() { return next(); } +inline RNG::operator float() { return next()*2.3283064365386962890625e-10f; } +inline RNG::operator double() { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; } + +inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); } +inline unsigned RNG::operator ()() { return next(); } + +inline int RNG::uniform(int a, int b) { return a == b ? a : (int)(next() % (b - a) + a); } +inline float RNG::uniform(float a, float b) { return ((float)*this)*(b - a) + a; } +inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; } + +inline unsigned RNG::next() +{ + state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32); + return (unsigned)state; +} + +//! returns the next unifomly-distributed random number of the specified type +template static inline _Tp randu() +{ + return (_Tp)theRNG(); +} + +///////////////////////////////// Formatted string generation ///////////////////////////////// + +CV_EXPORTS String format( const char* fmt, ... ); + +///////////////////////////////// Formatted output of cv::Mat ///////////////////////////////// + +static inline +Ptr format(InputArray mtx, int fmt) +{ + return Formatter::get(fmt)->format(mtx.getMat()); +} + +static inline +int print(Ptr fmtd, FILE* stream = stdout) +{ + int written = 0; + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + written += fputs(str, stream); + + return written; +} + +static inline +int print(const Mat& mtx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(mtx), stream); +} + +static inline +int print(const UMat& mtx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream); +} + +template static inline +int print(const std::vector >& vec, FILE* stream = stdout) +{ + return print(Formatter::get()->format(Mat(vec)), stream); +} + +template static inline +int print(const std::vector >& vec, FILE* stream = stdout) +{ + return print(Formatter::get()->format(Mat(vec)), stream); +} + +template static inline +int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(cv::Mat(matx)), stream); +} + +//! @endcond + +/****************************************************************************************\ +* Auxiliary algorithms * +\****************************************************************************************/ + +/** @brief Splits an element set into equivalency classes. + +The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements +into one or more equivalency classes, as described in + . The function returns the number of +equivalency classes. +@param _vec Set of elements stored as a vector. +@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is +a 0-based cluster index of `vec[i]`. +@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an +instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The +predicate returns true when the elements are certainly in the same class, and returns false if they +may or may not be in the same class. +@ingroup core_cluster +*/ +template int +partition( const std::vector<_Tp>& _vec, std::vector& labels, + _EqPredicate predicate=_EqPredicate()) +{ + int i, j, N = (int)_vec.size(); + const _Tp* vec = &_vec[0]; + + const int PARENT=0; + const int RANK=1; + + std::vector _nodes(N*2); + int (*nodes)[2] = (int(*)[2])&_nodes[0]; + + // The first O(N) pass: create N single-vertex trees + for(i = 0; i < N; i++) + { + nodes[i][PARENT]=-1; + nodes[i][RANK] = 0; + } + + // The main O(N^2) pass: merge connected components + for( i = 0; i < N; i++ ) + { + int root = i; + + // find root + while( nodes[root][PARENT] >= 0 ) + root = nodes[root][PARENT]; + + for( j = 0; j < N; j++ ) + { + if( i == j || !predicate(vec[i], vec[j])) + continue; + int root2 = j; + + while( nodes[root2][PARENT] >= 0 ) + root2 = nodes[root2][PARENT]; + + if( root2 != root ) + { + // unite both trees + int rank = nodes[root][RANK], rank2 = nodes[root2][RANK]; + if( rank > rank2 ) + nodes[root2][PARENT] = root; + else + { + nodes[root][PARENT] = root2; + nodes[root2][RANK] += rank == rank2; + root = root2; + } + CV_Assert( nodes[root][PARENT] < 0 ); + + int k = j, parent; + + // compress the path from node2 to root + while( (parent = nodes[k][PARENT]) >= 0 ) + { + nodes[k][PARENT] = root; + k = parent; + } + + // compress the path from node to root + k = i; + while( (parent = nodes[k][PARENT]) >= 0 ) + { + nodes[k][PARENT] = root; + k = parent; + } + } + } + } + + // Final O(N) pass: enumerate classes + labels.resize(N); + int nclasses = 0; + + for( i = 0; i < N; i++ ) + { + int root = i; + while( nodes[root][PARENT] >= 0 ) + root = nodes[root][PARENT]; + // re-use the rank as the class label + if( nodes[root][RANK] >= 0 ) + nodes[root][RANK] = ~nclasses++; + labels[i] = ~nodes[root][RANK]; + } + + return nclasses; +} + +} // cv + +#endif diff --git a/thirdparty/linux/include/opencv2/core/optim.hpp b/thirdparty/linux/include/opencv2/core/optim.hpp new file mode 100644 index 0000000..7249e0f --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/optim.hpp @@ -0,0 +1,302 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPTIM_HPP +#define OPENCV_OPTIM_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ + +/** @addtogroup core_optim +The algorithms in this section minimize or maximize function value within specified constraints or +without any constraints. +@{ +*/ + +/** @brief Basic interface for all solvers + */ +class CV_EXPORTS MinProblemSolver : public Algorithm +{ +public: + /** @brief Represents function being optimized + */ + class CV_EXPORTS Function + { + public: + virtual ~Function() {} + virtual int getDims() const = 0; + virtual double getGradientEps() const; + virtual double calc(const double* x) const = 0; + virtual void getGradient(const double* x,double* grad); + }; + + /** @brief Getter for the optimized function. + + The optimized function is represented by Function interface, which requires derivatives to + implement the sole method calc(double*) to evaluate the function. + + @return Smart-pointer to an object that implements Function interface - it represents the + function that is being optimized. It can be empty, if no function was given so far. + */ + virtual Ptr getFunction() const = 0; + + /** @brief Setter for the optimized function. + + *It should be called at least once before the call to* minimize(), as default value is not usable. + + @param f The new function to optimize. + */ + virtual void setFunction(const Ptr& f) = 0; + + /** @brief Getter for the previously set terminal criteria for this algorithm. + + @return Deep copy of the terminal criteria used at the moment. + */ + virtual TermCriteria getTermCriteria() const = 0; + + /** @brief Set terminal criteria for solver. + + This method *is not necessary* to be called before the first call to minimize(), as the default + value is sensible. + + Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when + the function values at the vertices of simplex are within termcrit.epsilon range or simplex + becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes + first. + @param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure. + */ + virtual void setTermCriteria(const TermCriteria& termcrit) = 0; + + /** @brief actually runs the algorithm and performs the minimization. + + The sole input parameter determines the centroid of the starting simplex (roughly, it tells + where to start), all the others (terminal criteria, initial step, function to be minimized) are + supposed to be set via the setters before the call to this method or the default values (not + always sensible) will be used. + + @param x The initial point, that will become a centroid of an initial simplex. After the algorithm + will terminate, it will be setted to the point where the algorithm stops, the point of possible + minimum. + @return The value of a function at the point found. + */ + virtual double minimize(InputOutputArray x) = 0; +}; + +/** @brief This class is used to perform the non-linear non-constrained minimization of a function, + +defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as +**downhill simplex method**. The basic idea about the method can be obtained from +. + +It should be noted, that this method, although deterministic, is rather a heuristic and therefore +may converge to a local minima, not necessary a global one. It is iterative optimization technique, +which at each step uses an information about the values of a function evaluated only at `n+1` +points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At +each step new point is chosen to evaluate function at, obtained value is compared with previous +ones and based on this information simplex changes it's shape , slowly moving to the local minimum. +Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear +Conjugate Gradient method (which is also implemented in optim). + +Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the +function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so +small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some +defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon. + +@note DownhillSolver is a derivative of the abstract interface +cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to +encapsulate the functionality, common to all non-linear optimization algorithms in the optim +module. + +@note term criteria should meet following condition: +@code + termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 +@endcode + */ +class CV_EXPORTS DownhillSolver : public MinProblemSolver +{ +public: + /** @brief Returns the initial step that will be used in downhill simplex algorithm. + + @param step Initial step that will be used in algorithm. Note, that although corresponding setter + accepts column-vectors as well as row-vectors, this method will return a row-vector. + @see DownhillSolver::setInitStep + */ + virtual void getInitStep(OutputArray step) const=0; + + /** @brief Sets the initial step that will be used in downhill simplex algorithm. + + Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional + vectors that are used to determine the shape of initial simplex. Roughly said, initial point + determines the position of a simplex (it will become simplex's centroid), while step determines the + spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are + the initial step and initial point respectively, the vertices of a simplex will be: + \f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes + projections of the initial step of *n*-th coordinate (the result of projection is treated to be + vector given by \f$s_i:=e_i\cdot\left\f$, where \f$e_i\f$ form canonical basis) + + @param step Initial step that will be used in algorithm. Roughly said, it determines the spread + (size in each dimension) of an initial simplex. + */ + virtual void setInitStep(InputArray step)=0; + + /** @brief This function returns the reference to the ready-to-use DownhillSolver object. + + All the parameters are optional, so this procedure can be called even without parameters at + all. In this case, the default values will be used. As default value for terminal criteria are + the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() + should be called upon the obtained object, if the respective parameters were not given to + create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out + and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely + equivalent (and will drop the same errors in the same way, should invalid input be detected). + @param f Pointer to the function that will be minimized, similarly to the one you submit via + MinProblemSolver::setFunction. + @param initStep Initial step, that will be used to construct the initial simplex, similarly to the one + you submit via MinProblemSolver::setInitStep. + @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via + MinProblemSolver::setTermCriteria. + */ + static Ptr create(const Ptr& f=Ptr(), + InputArray initStep=Mat_(1,1,0.0), + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); +}; + +/** @brief This class is used to perform the non-linear non-constrained minimization of a function +with known gradient, + +defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**. +The implementation was done based on the beautifully clear explanatory article [An Introduction to +the Conjugate Gradient Method Without the Agonizing +Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard +Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for +example ) for numerically solving the +systems of linear equations. + +It should be noted, that this method, although deterministic, is rather a heuristic method and +therefore may converge to a local minima, not necessary a global one. What is even more disastrous, +most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between +local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may +converge to it. Another obvious restriction is that it should be possible to compute the gradient of +a function at any point, thus it is preferable to have analytic expression for gradient and +computational burden should be born by the user. + +The latter responsibility is accompilished via the getGradient method of a +MinProblemSolver::Function interface (which represents function being optimized). This method takes +point a point in *n*-dimensional space (first argument represents the array of coordinates of that +point) and comput its gradient (it should be stored in the second argument as an array). + +@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface. + +@note term criteria should meet following condition: +@code + termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 + // or + termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0 +@endcode + */ +class CV_EXPORTS ConjGradSolver : public MinProblemSolver +{ +public: + /** @brief This function returns the reference to the ready-to-use ConjGradSolver object. + + All the parameters are optional, so this procedure can be called even without parameters at + all. In this case, the default values will be used. As default value for terminal criteria are + the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained + object, if the function was not given to create(). Otherwise, the two ways (submit it to + create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent + (and will drop the same errors in the same way, should invalid input be detected). + @param f Pointer to the function that will be minimized, similarly to the one you submit via + MinProblemSolver::setFunction. + @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via + MinProblemSolver::setTermCriteria. + */ + static Ptr create(const Ptr& f=Ptr(), + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); +}; + +//! return codes for cv::solveLP() function +enum SolveLPResult +{ + SOLVELP_UNBOUNDED = -2, //!< problem is unbounded (target function can achieve arbitrary high values) + SOLVELP_UNFEASIBLE = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed) + SOLVELP_SINGLE = 0, //!< there is only one maximum for target function + SOLVELP_MULTI = 1 //!< there are multiple maxima for target function - the arbitrary one is returned +}; + +/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). + +What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: + +\f[\mbox{Maximize } c\cdot x\\ + \mbox{Subject to:}\\ + Ax\leq b\\ + x\geq 0\f] + +Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1` +column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. + +Simplex algorithm is one of many algorithms that are designed to handle this sort of problems +efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve +any problem written as above in polynomial time, while simplex method degenerates to exponential +time for some special cases), it is well-studied, easy to implement and is shown to work well for +real-life purposes. + +The particular implementation is taken almost verbatim from **Introduction to Algorithms, third +edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the +Bland's rule is used to prevent cycling. + +@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should +contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, +in the latter case it is understood to correspond to \f$c^T\f$. +@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above +and the remaining to \f$A\f$. It should containt 32- or 64-bit floating point numbers. +@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the +formulation above. It will contain 64-bit floating point numbers. +@return One of cv::SolveLPResult + */ +CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z); + +//! @} + +}// cv + +#endif diff --git a/thirdparty/linux/include/opencv2/core/ovx.hpp b/thirdparty/linux/include/opencv2/core/ovx.hpp new file mode 100644 index 0000000..8bb7d54 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/ovx.hpp @@ -0,0 +1,28 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2016, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. + +// OpenVX related definitions and declarations + +#pragma once +#ifndef OPENCV_OVX_HPP +#define OPENCV_OVX_HPP + +#include "cvdef.h" + +namespace cv +{ +/// Check if use of OpenVX is possible +CV_EXPORTS_W bool haveOpenVX(); + +/// Check if use of OpenVX is enabled +CV_EXPORTS_W bool useOpenVX(); + +/// Enable/disable use of OpenVX +CV_EXPORTS_W void setUseOpenVX(bool flag); +} // namespace cv + +#endif // OPENCV_OVX_HPP diff --git a/thirdparty/linux/include/opencv2/core/persistence.hpp b/thirdparty/linux/include/opencv2/core/persistence.hpp new file mode 100644 index 0000000..3f18d54 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/persistence.hpp @@ -0,0 +1,1274 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PERSISTENCE_HPP +#define OPENCV_CORE_PERSISTENCE_HPP + +#ifndef __cplusplus +# error persistence.hpp header must be compiled as C++ +#endif + +//! @addtogroup core_c +//! @{ + +/** @brief "black box" representation of the file storage associated with a file on disk. + +Several functions that are described below take CvFileStorage\* as inputs and allow the user to +save or to load hierarchical collections that consist of scalar values, standard CXCore objects +(such as matrices, sequences, graphs), and user-defined objects. + +OpenCV can read and write data in XML (), YAML () or +JSON () formats. Below is an example of 3x3 floating-point identity matrix A, +stored in XML and YAML files +using CXCore functions: +XML: +@code{.xml} + + + + 3 + 3 +
f
+ 1. 0. 0. 0. 1. 0. 0. 0. 1. + + +@endcode +YAML: +@code{.yaml} + %YAML:1.0 + A: !!opencv-matrix + rows: 3 + cols: 3 + dt: f + data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1.] +@endcode +As it can be seen from the examples, XML uses nested tags to represent hierarchy, while YAML uses +indentation for that purpose (similar to the Python programming language). + +The same functions can read and write data in both formats; the particular format is determined by +the extension of the opened file, ".xml" for XML files, ".yml" or ".yaml" for YAML and ".json" for +JSON. + */ +typedef struct CvFileStorage CvFileStorage; +typedef struct CvFileNode CvFileNode; +typedef struct CvMat CvMat; +typedef struct CvMatND CvMatND; + +//! @} core_c + +#include "opencv2/core/types.hpp" +#include "opencv2/core/mat.hpp" + +namespace cv { + +/** @addtogroup core_xml + +XML/YAML/JSON file storages. {#xml_storage} +======================= +Writing to a file storage. +-------------------------- +You can store and then restore various OpenCV data structures to/from XML (), +YAML () or JSON () formats. Also, it is possible store +and load arbitrarily complex data structures, which include OpenCV data structures, as well as +primitive data types (integer and floating-point numbers and text strings) as their elements. + +Use the following procedure to write something to XML, YAML or JSON: +-# Create new FileStorage and open it for writing. It can be done with a single call to +FileStorage::FileStorage constructor that takes a filename, or you can use the default constructor +and then call FileStorage::open. Format of the file (XML, YAML or JSON) is determined from the filename +extension (".xml", ".yml"/".yaml" and ".json", respectively) +-# Write all the data you want using the streaming operator `<<`, just like in the case of STL +streams. +-# Close the file using FileStorage::release. FileStorage destructor also closes the file. + +Here is an example: +@code + #include "opencv2/opencv.hpp" + #include + + using namespace cv; + + int main(int, char** argv) + { + FileStorage fs("test.yml", FileStorage::WRITE); + + fs << "frameCount" << 5; + time_t rawtime; time(&rawtime); + fs << "calibrationDate" << asctime(localtime(&rawtime)); + Mat cameraMatrix = (Mat_(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1); + Mat distCoeffs = (Mat_(5,1) << 0.1, 0.01, -0.001, 0, 0); + fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs; + fs << "features" << "["; + for( int i = 0; i < 3; i++ ) + { + int x = rand() % 640; + int y = rand() % 480; + uchar lbp = rand() % 256; + + fs << "{:" << "x" << x << "y" << y << "lbp" << "[:"; + for( int j = 0; j < 8; j++ ) + fs << ((lbp >> j) & 1); + fs << "]" << "}"; + } + fs << "]"; + fs.release(); + return 0; + } +@endcode +The sample above stores to XML and integer, text string (calibration date), 2 matrices, and a custom +structure "feature", which includes feature coordinates and LBP (local binary pattern) value. Here +is output of the sample: +@code{.yaml} +%YAML:1.0 +frameCount: 5 +calibrationDate: "Fri Jun 17 14:09:29 2011\n" +cameraMatrix: !!opencv-matrix + rows: 3 + cols: 3 + dt: d + data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ] +distCoeffs: !!opencv-matrix + rows: 5 + cols: 1 + dt: d + data: [ 1.0000000000000001e-01, 1.0000000000000000e-02, + -1.0000000000000000e-03, 0., 0. ] +features: + - { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] } + - { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] } + - { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] } +@endcode + +As an exercise, you can replace ".yml" with ".xml" or ".json" in the sample above and see, how the +corresponding XML file will look like. + +Several things can be noted by looking at the sample code and the output: + +- The produced YAML (and XML/JSON) consists of heterogeneous collections that can be nested. There are + 2 types of collections: named collections (mappings) and unnamed collections (sequences). In mappings + each element has a name and is accessed by name. This is similar to structures and std::map in + C/C++ and dictionaries in Python. In sequences elements do not have names, they are accessed by + indices. This is similar to arrays and std::vector in C/C++ and lists, tuples in Python. + "Heterogeneous" means that elements of each single collection can have different types. + + Top-level collection in YAML/XML/JSON is a mapping. Each matrix is stored as a mapping, and the matrix + elements are stored as a sequence. Then, there is a sequence of features, where each feature is + represented a mapping, and lbp value in a nested sequence. + +- When you write to a mapping (a structure), you write element name followed by its value. When you + write to a sequence, you simply write the elements one by one. OpenCV data structures (such as + cv::Mat) are written in absolutely the same way as simple C data structures - using `<<` + operator. + +- To write a mapping, you first write the special string `{` to the storage, then write the + elements as pairs (`fs << << `) and then write the closing + `}`. + +- To write a sequence, you first write the special string `[`, then write the elements, then + write the closing `]`. + +- In YAML/JSON (but not XML), mappings and sequences can be written in a compact Python-like inline + form. In the sample above matrix elements, as well as each feature, including its lbp value, is + stored in such inline form. To store a mapping/sequence in a compact form, put `:` after the + opening character, e.g. use `{:` instead of `{` and `[:` instead of `[`. When the + data is written to XML, those extra `:` are ignored. + +Reading data from a file storage. +--------------------------------- +To read the previously written XML, YAML or JSON file, do the following: +-# Open the file storage using FileStorage::FileStorage constructor or FileStorage::open method. + In the current implementation the whole file is parsed and the whole representation of file + storage is built in memory as a hierarchy of file nodes (see FileNode) + +-# Read the data you are interested in. Use FileStorage::operator [], FileNode::operator [] + and/or FileNodeIterator. + +-# Close the storage using FileStorage::release. + +Here is how to read the file created by the code sample above: +@code + FileStorage fs2("test.yml", FileStorage::READ); + + // first method: use (type) operator on FileNode. + int frameCount = (int)fs2["frameCount"]; + + String date; + // second method: use FileNode::operator >> + fs2["calibrationDate"] >> date; + + Mat cameraMatrix2, distCoeffs2; + fs2["cameraMatrix"] >> cameraMatrix2; + fs2["distCoeffs"] >> distCoeffs2; + + cout << "frameCount: " << frameCount << endl + << "calibration date: " << date << endl + << "camera matrix: " << cameraMatrix2 << endl + << "distortion coeffs: " << distCoeffs2 << endl; + + FileNode features = fs2["features"]; + FileNodeIterator it = features.begin(), it_end = features.end(); + int idx = 0; + std::vector lbpval; + + // iterate through a sequence using FileNodeIterator + for( ; it != it_end; ++it, idx++ ) + { + cout << "feature #" << idx << ": "; + cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: ("; + // you can also easily read numerical arrays using FileNode >> std::vector operator. + (*it)["lbp"] >> lbpval; + for( int i = 0; i < (int)lbpval.size(); i++ ) + cout << " " << (int)lbpval[i]; + cout << ")" << endl; + } + fs2.release(); +@endcode + +Format specification {#format_spec} +-------------------- +`([count]{u|c|w|s|i|f|d})`... where the characters correspond to fundamental C++ types: +- `u` 8-bit unsigned number +- `c` 8-bit signed number +- `w` 16-bit unsigned number +- `s` 16-bit signed number +- `i` 32-bit signed number +- `f` single precision floating-point number +- `d` double precision floating-point number +- `r` pointer, 32 lower bits of which are written as a signed integer. The type can be used to + store structures with links between the elements. + +`count` is the optional counter of values of a given type. For example, `2if` means that each array +element is a structure of 2 integers, followed by a single-precision floating-point number. The +equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u` +means that the array consists of bytes, and `2d` means the array consists of pairs of doubles. + +@see @ref filestorage.cpp +*/ + +//! @{ + +/** @example filestorage.cpp +A complete example using the FileStorage interface +*/ + +////////////////////////// XML & YAML I/O ////////////////////////// + +class CV_EXPORTS FileNode; +class CV_EXPORTS FileNodeIterator; + +/** @brief XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or +reading data to/from a file. + */ +class CV_EXPORTS_W FileStorage +{ +public: + //! file storage mode + enum Mode + { + READ = 0, //!< value, open the file for reading + WRITE = 1, //!< value, open the file for writing + APPEND = 2, //!< value, open the file for appending + MEMORY = 4, //!< flag, read data from source or write data to the internal buffer (which is + //!< returned by FileStorage::release) + FORMAT_MASK = (7<<3), //!< mask for format flags + FORMAT_AUTO = 0, //!< flag, auto format + FORMAT_XML = (1<<3), //!< flag, XML format + FORMAT_YAML = (2<<3), //!< flag, YAML format + FORMAT_JSON = (3<<3), //!< flag, JSON format + + BASE64 = 64, //!< flag, write rawdata in Base64 by default. (consider using WRITE_BASE64) + WRITE_BASE64 = BASE64 | WRITE, //!< flag, enable both WRITE and BASE64 + }; + enum + { + UNDEFINED = 0, + VALUE_EXPECTED = 1, + NAME_EXPECTED = 2, + INSIDE_MAP = 4 + }; + + /** @brief The constructors. + + The full constructor opens the file. Alternatively you can use the default constructor and then + call FileStorage::open. + */ + CV_WRAP FileStorage(); + + /** @overload + @param source Name of the file to open or the text string to read the data from. Extension of the + file (.xml, .yml/.yaml, or .json) determines its format (XML, YAML or JSON respectively). Also you can + append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both FileStorage::WRITE + and FileStorage::MEMORY flags are specified, source is used just to specify the output file format (e.g. + mydata.xml, .yml etc.). + @param flags Mode of operation. See FileStorage::Mode + @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and + you should use 8-bit encoding instead of it. + */ + CV_WRAP FileStorage(const String& source, int flags, const String& encoding=String()); + + /** @overload */ + FileStorage(CvFileStorage* fs, bool owning=true); + + //! the destructor. calls release() + virtual ~FileStorage(); + + /** @brief Opens a file. + + See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release + before opening the file. + @param filename Name of the file to open or the text string to read the data from. + Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON + respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both + FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify + the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters. + You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to + FileStorage::BASE64 flag. + @param flags Mode of operation. One of FileStorage::Mode + @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and + you should use 8-bit encoding instead of it. + */ + CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String()); + + /** @brief Checks whether the file is opened. + + @returns true if the object is associated with the current file and false otherwise. It is a + good practice to call this method after you tried to open a file. + */ + CV_WRAP virtual bool isOpened() const; + + /** @brief Closes the file and releases all the memory buffers. + + Call this method after all I/O operations with the storage are finished. + */ + CV_WRAP virtual void release(); + + /** @brief Closes the file and releases all the memory buffers. + + Call this method after all I/O operations with the storage are finished. If the storage was + opened for writing data and FileStorage::WRITE was specified + */ + CV_WRAP virtual String releaseAndGetString(); + + /** @brief Returns the first element of the top-level mapping. + @returns The first element of the top-level mapping. + */ + CV_WRAP FileNode getFirstTopLevelNode() const; + + /** @brief Returns the top-level mapping + @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file. + However, YAML supports multiple streams and so there can be several. + @returns The top-level mapping. + */ + CV_WRAP FileNode root(int streamidx=0) const; + + /** @brief Returns the specified element of the top-level mapping. + @param nodename Name of the file node. + @returns Node with the given name. + */ + FileNode operator[](const String& nodename) const; + + /** @overload */ + CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; + + /** @brief Returns the obsolete C FileStorage structure. + @returns Pointer to the underlying C FileStorage structure + */ + CvFileStorage* operator *() { return fs.get(); } + + /** @overload */ + const CvFileStorage* operator *() const { return fs.get(); } + + /** @brief Writes multiple numbers. + + Writes one or more numbers of the specified format to the currently written structure. Usually it is + more convenient to use operator `<<` instead of this method. + @param fmt Specification of each array element, see @ref format_spec "format specification" + @param vec Pointer to the written array. + @param len Number of the uchar elements to write. + */ + void writeRaw( const String& fmt, const uchar* vec, size_t len ); + + /** @brief Writes the registered C structure (CvMat, CvMatND, CvSeq). + @param name Name of the written object. + @param obj Pointer to the object. + @see ocvWrite for details. + */ + void writeObj( const String& name, const void* obj ); + + /** + * @brief Simplified writing API to use with bindings. + * @param name Name of the written object + * @param val Value of the written object + */ + CV_WRAP void write(const String& name, double val); + /// @overload + CV_WRAP void write(const String& name, const String& val); + /// @overload + CV_WRAP void write(const String& name, InputArray val); + + /** @brief Writes a comment. + + The function writes a comment into file storage. The comments are skipped when the storage is read. + @param comment The written comment, single-line or multi-line + @param append If true, the function tries to put the comment at the end of current line. + Else if the comment is multi-line, or if it does not fit at the end of the current + line, the comment starts a new line. + */ + CV_WRAP void writeComment(const String& comment, bool append = false); + + /** @brief Returns the normalized object name for the specified name of a file. + @param filename Name of a file + @returns The normalized object name. + */ + static String getDefaultObjectName(const String& filename); + + Ptr fs; //!< the underlying C FileStorage structure + String elname; //!< the currently written element + std::vector structs; //!< the stack of written structures + int state; //!< the writer state +}; + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvFileStorage* obj) const; + +/** @brief File Storage Node class. + +The node is used to store each and every element of the file storage opened for reading. When +XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of +nodes. Each node can be a “leaf†that is contain a single number or a string, or be a collection of +other nodes. There can be named collections (mappings) where each element has a name and it is +accessed by a name, and ordered collections (sequences) where elements do not have names but rather +accessed by index. Type of the file node can be determined using FileNode::type method. + +Note that file nodes are only used for navigating file storages opened for reading. When a file +storage is opened for writing, no data is stored in memory after it is written. + */ +class CV_EXPORTS_W_SIMPLE FileNode +{ +public: + //! type of the file storage node + enum Type + { + NONE = 0, //!< empty node + INT = 1, //!< an integer + REAL = 2, //!< floating-point number + FLOAT = REAL, //!< synonym or REAL + STR = 3, //!< text string in UTF-8 encoding + STRING = STR, //!< synonym for STR + REF = 4, //!< integer of size size_t. Typically used for storing complex dynamic structures where some elements reference the others + SEQ = 5, //!< sequence + MAP = 6, //!< mapping + TYPE_MASK = 7, + FLOW = 8, //!< compact representation of a sequence or mapping. Used only by YAML writer + USER = 16, //!< a registered object (e.g. a matrix) + EMPTY = 32, //!< empty structure (sequence or mapping) + NAMED = 64 //!< the node has a name (i.e. it is element of a mapping) + }; + /** @brief The constructors. + + These constructors are used to create a default file node, construct it from obsolete structures or + from the another file node. + */ + CV_WRAP FileNode(); + + /** @overload + @param fs Pointer to the obsolete file storage structure. + @param node File node to be used as initialization for the created file node. + */ + FileNode(const CvFileStorage* fs, const CvFileNode* node); + + /** @overload + @param node File node to be used as initialization for the created file node. + */ + FileNode(const FileNode& node); + + /** @brief Returns element of a mapping node or a sequence node. + @param nodename Name of an element in the mapping node. + @returns Returns the element with the given identifier. + */ + FileNode operator[](const String& nodename) const; + + /** @overload + @param nodename Name of an element in the mapping node. + */ + CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; + + /** @overload + @param i Index of an element in the sequence node. + */ + CV_WRAP_AS(at) FileNode operator[](int i) const; + + /** @brief Returns type of the node. + @returns Type of the node. See FileNode::Type + */ + CV_WRAP int type() const; + + //! returns true if the node is empty + CV_WRAP bool empty() const; + //! returns true if the node is a "none" object + CV_WRAP bool isNone() const; + //! returns true if the node is a sequence + CV_WRAP bool isSeq() const; + //! returns true if the node is a mapping + CV_WRAP bool isMap() const; + //! returns true if the node is an integer + CV_WRAP bool isInt() const; + //! returns true if the node is a floating-point number + CV_WRAP bool isReal() const; + //! returns true if the node is a text string + CV_WRAP bool isString() const; + //! returns true if the node has a name + CV_WRAP bool isNamed() const; + //! returns the node name or an empty string if the node is nameless + CV_WRAP String name() const; + //! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise. + CV_WRAP size_t size() const; + //! returns the node content as an integer. If the node stores floating-point number, it is rounded. + operator int() const; + //! returns the node content as float + operator float() const; + //! returns the node content as double + operator double() const; + //! returns the node content as text string + operator String() const; +#ifndef OPENCV_NOSTL + operator std::string() const; +#endif + + //! returns pointer to the underlying file node + CvFileNode* operator *(); + //! returns pointer to the underlying file node + const CvFileNode* operator* () const; + + //! returns iterator pointing to the first node element + FileNodeIterator begin() const; + //! returns iterator pointing to the element following the last node element + FileNodeIterator end() const; + + /** @brief Reads node elements to the buffer with the specified format. + + Usually it is more convenient to use operator `>>` instead of this method. + @param fmt Specification of each array element. See @ref format_spec "format specification" + @param vec Pointer to the destination array. + @param len Number of elements to read. If it is greater than number of remaining elements then all + of them will be read. + */ + void readRaw( const String& fmt, uchar* vec, size_t len ) const; + + //! reads the registered object and returns pointer to it + void* readObj() const; + + //! Simplified reading API to use with bindings. + CV_WRAP double real() const; + //! Simplified reading API to use with bindings. + CV_WRAP String string() const; + //! Simplified reading API to use with bindings. + CV_WRAP Mat mat() const; + + // do not use wrapper pointer classes for better efficiency + const CvFileStorage* fs; + const CvFileNode* node; +}; + + +/** @brief used to iterate through sequences and mappings. + +A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a +sequence, stored in node. See the data reading sample in the beginning of the section. + */ +class CV_EXPORTS FileNodeIterator +{ +public: + /** @brief The constructors. + + These constructors are used to create a default iterator, set it to specific element in a file node + or construct it from another iterator. + */ + FileNodeIterator(); + + /** @overload + @param fs File storage for the iterator. + @param node File node for the iterator. + @param ofs Index of the element in the node. The created iterator will point to this element. + */ + FileNodeIterator(const CvFileStorage* fs, const CvFileNode* node, size_t ofs=0); + + /** @overload + @param it Iterator to be used as initialization for the created iterator. + */ + FileNodeIterator(const FileNodeIterator& it); + + //! returns the currently observed element + FileNode operator *() const; + //! accesses the currently observed element methods + FileNode operator ->() const; + + //! moves iterator to the next node + FileNodeIterator& operator ++ (); + //! moves iterator to the next node + FileNodeIterator operator ++ (int); + //! moves iterator to the previous node + FileNodeIterator& operator -- (); + //! moves iterator to the previous node + FileNodeIterator operator -- (int); + //! moves iterator forward by the specified offset (possibly negative) + FileNodeIterator& operator += (int ofs); + //! moves iterator backward by the specified offset (possibly negative) + FileNodeIterator& operator -= (int ofs); + + /** @brief Reads node elements to the buffer with the specified format. + + Usually it is more convenient to use operator `>>` instead of this method. + @param fmt Specification of each array element. See @ref format_spec "format specification" + @param vec Pointer to the destination array. + @param maxCount Number of elements to read. If it is greater than number of remaining elements then + all of them will be read. + */ + FileNodeIterator& readRaw( const String& fmt, uchar* vec, + size_t maxCount=(size_t)INT_MAX ); + + struct SeqReader + { + int header_size; + void* seq; /* sequence, beign read; CvSeq */ + void* block; /* current block; CvSeqBlock */ + schar* ptr; /* pointer to element be read next */ + schar* block_min; /* pointer to the beginning of block */ + schar* block_max; /* pointer to the end of block */ + int delta_index;/* = seq->first->start_index */ + schar* prev_elem; /* pointer to previous element */ + }; + + const CvFileStorage* fs; + const CvFileNode* container; + SeqReader reader; + size_t remaining; +}; + +//! @} core_xml + +/////////////////// XML & YAML I/O implementation ////////////////// + +//! @relates cv::FileStorage +//! @{ + +CV_EXPORTS void write( FileStorage& fs, const String& name, int value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, float value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, double value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); +CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); + +CV_EXPORTS void writeScalar( FileStorage& fs, int value ); +CV_EXPORTS void writeScalar( FileStorage& fs, float value ); +CV_EXPORTS void writeScalar( FileStorage& fs, double value ); +CV_EXPORTS void writeScalar( FileStorage& fs, const String& value ); + +//! @} + +//! @relates cv::FileNode +//! @{ + +CV_EXPORTS void read(const FileNode& node, int& value, int default_value); +CV_EXPORTS void read(const FileNode& node, float& value, float default_value); +CV_EXPORTS void read(const FileNode& node, double& value, double default_value); +CV_EXPORTS void read(const FileNode& node, String& value, const String& default_value); +CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() ); +CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() ); +CV_EXPORTS void read(const FileNode& node, std::vector& keypoints); +CV_EXPORTS void read(const FileNode& node, std::vector& matches); + +template static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2])); +} + +template static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); +} + +template static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]); +} + +template static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); +} + +static inline void read(const FileNode& node, Range& value, const Range& default_value) +{ + Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end); + read(node, temp, default_temp); + value.start = temp.x; value.end = temp.y; +} + +//! @} + +/** @brief Writes string to a file storage. +@relates cv::FileStorage + */ +CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str); + +//! @cond IGNORED + +namespace internal +{ + class CV_EXPORTS WriteStructContext + { + public: + WriteStructContext(FileStorage& _fs, const String& name, int flags, const String& typeName = String()); + ~WriteStructContext(); + private: + FileStorage* fs; + }; + + template class VecWriterProxy + { + public: + VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} + void operator()(const std::vector<_Tp>& vec) const + { + size_t count = vec.size(); + for (size_t i = 0; i < count; i++) + write(*fs, vec[i]); + } + private: + FileStorage* fs; + }; + + template class VecWriterProxy<_Tp, 1> + { + public: + VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} + void operator()(const std::vector<_Tp>& vec) const + { + int _fmt = DataType<_Tp>::fmt; + char fmt[] = { (char)((_fmt >> 8) + '1'), (char)_fmt, '\0' }; + fs->writeRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, vec.size() * sizeof(_Tp)); + } + private: + FileStorage* fs; + }; + + template class VecReaderProxy + { + public: + VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} + void operator()(std::vector<_Tp>& vec, size_t count) const + { + count = std::min(count, it->remaining); + vec.resize(count); + for (size_t i = 0; i < count; i++, ++(*it)) + read(**it, vec[i], _Tp()); + } + private: + FileNodeIterator* it; + }; + + template class VecReaderProxy<_Tp, 1> + { + public: + VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} + void operator()(std::vector<_Tp>& vec, size_t count) const + { + size_t remaining = it->remaining; + size_t cn = DataType<_Tp>::channels; + int _fmt = DataType<_Tp>::fmt; + char fmt[] = { (char)((_fmt >> 8)+'1'), (char)_fmt, '\0' }; + size_t remaining1 = remaining / cn; + count = count < remaining1 ? count : remaining1; + vec.resize(count); + it->readRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, count*sizeof(_Tp)); + } + private: + FileNodeIterator* it; + }; + +} // internal + +//! @endcond + +//! @relates cv::FileStorage +//! @{ + +template static inline +void write(FileStorage& fs, const _Tp& value) +{ + write(fs, String(), value); +} + +template<> inline +void write( FileStorage& fs, const int& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const float& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const double& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const String& value ) +{ + writeScalar(fs, value); +} + +template static inline +void write(FileStorage& fs, const Point_<_Tp>& pt ) +{ + write(fs, pt.x); + write(fs, pt.y); +} + +template static inline +void write(FileStorage& fs, const Point3_<_Tp>& pt ) +{ + write(fs, pt.x); + write(fs, pt.y); + write(fs, pt.z); +} + +template static inline +void write(FileStorage& fs, const Size_<_Tp>& sz ) +{ + write(fs, sz.width); + write(fs, sz.height); +} + +template static inline +void write(FileStorage& fs, const Complex<_Tp>& c ) +{ + write(fs, c.re); + write(fs, c.im); +} + +template static inline +void write(FileStorage& fs, const Rect_<_Tp>& r ) +{ + write(fs, r.x); + write(fs, r.y); + write(fs, r.width); + write(fs, r.height); +} + +template static inline +void write(FileStorage& fs, const Vec<_Tp, cn>& v ) +{ + for(int i = 0; i < cn; i++) + write(fs, v.val[i]); +} + +template static inline +void write(FileStorage& fs, const Scalar_<_Tp>& s ) +{ + write(fs, s.val[0]); + write(fs, s.val[1]); + write(fs, s.val[2]); + write(fs, s.val[3]); +} + +static inline +void write(FileStorage& fs, const Range& r ) +{ + write(fs, r.start); + write(fs, r.end); +} + +template static inline +void write( FileStorage& fs, const std::vector<_Tp>& vec ) +{ + cv::internal::VecWriterProxy<_Tp, DataType<_Tp>::fmt != 0> w(&fs); + w(vec); +} + +template static inline +void write(FileStorage& fs, const String& name, const Point_<_Tp>& pt ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, pt); +} + +template static inline +void write(FileStorage& fs, const String& name, const Point3_<_Tp>& pt ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, pt); +} + +template static inline +void write(FileStorage& fs, const String& name, const Size_<_Tp>& sz ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, sz); +} + +template static inline +void write(FileStorage& fs, const String& name, const Complex<_Tp>& c ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, c); +} + +template static inline +void write(FileStorage& fs, const String& name, const Rect_<_Tp>& r ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, r); +} + +template static inline +void write(FileStorage& fs, const String& name, const Vec<_Tp, cn>& v ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, v); +} + +template static inline +void write(FileStorage& fs, const String& name, const Scalar_<_Tp>& s ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, s); +} + +static inline +void write(FileStorage& fs, const String& name, const Range& r ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, r); +} + +template static inline +void write( FileStorage& fs, const String& name, const std::vector<_Tp>& vec ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+(DataType<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); + write(fs, vec); +} + +template static inline +void write( FileStorage& fs, const String& name, const std::vector< std::vector<_Tp> >& vec ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ); + for(size_t i = 0; i < vec.size(); i++) + { + cv::internal::WriteStructContext ws_(fs, name, FileNode::SEQ+(DataType<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); + write(fs, vec[i]); + } +} + +//! @} FileStorage + +//! @relates cv::FileNode +//! @{ + +static inline +void read(const FileNode& node, bool& value, bool default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = temp != 0; +} + +static inline +void read(const FileNode& node, uchar& value, uchar default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, schar& value, schar default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, ushort& value, ushort default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, short& value, short default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +template static inline +void read( FileNodeIterator& it, std::vector<_Tp>& vec, size_t maxCount = (size_t)INT_MAX ) +{ + cv::internal::VecReaderProxy<_Tp, DataType<_Tp>::fmt != 0> r(&it); + r(vec, maxCount); +} + +template static inline +void read( const FileNode& node, std::vector<_Tp>& vec, const std::vector<_Tp>& default_value = std::vector<_Tp>() ) +{ + if(!node.node) + vec = default_value; + else + { + FileNodeIterator it = node.begin(); + read( it, vec ); + } +} + +//! @} FileNode + +//! @relates cv::FileStorage +//! @{ + +/** @brief Writes data to a file storage. + */ +template static inline +FileStorage& operator << (FileStorage& fs, const _Tp& value) +{ + if( !fs.isOpened() ) + return fs; + if( fs.state == FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP ) + CV_Error( Error::StsError, "No element name has been given" ); + write( fs, fs.elname, value ); + if( fs.state & FileStorage::INSIDE_MAP ) + fs.state = FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP; + return fs; +} + +/** @brief Writes data to a file storage. + */ +static inline +FileStorage& operator << (FileStorage& fs, const char* str) +{ + return (fs << String(str)); +} + +/** @brief Writes data to a file storage. + */ +static inline +FileStorage& operator << (FileStorage& fs, char* value) +{ + return (fs << String(value)); +} + +//! @} FileStorage + +//! @relates cv::FileNodeIterator +//! @{ + +/** @brief Reads data from a file storage. + */ +template static inline +FileNodeIterator& operator >> (FileNodeIterator& it, _Tp& value) +{ + read( *it, value, _Tp()); + return ++it; +} + +/** @brief Reads data from a file storage. + */ +template static inline +FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec) +{ + cv::internal::VecReaderProxy<_Tp, DataType<_Tp>::fmt != 0> r(&it); + r(vec, (size_t)INT_MAX); + return it; +} + +//! @} FileNodeIterator + +//! @relates cv::FileNode +//! @{ + +/** @brief Reads data from a file storage. + */ +template static inline +void operator >> (const FileNode& n, _Tp& value) +{ + read( n, value, _Tp()); +} + +/** @brief Reads data from a file storage. + */ +template static inline +void operator >> (const FileNode& n, std::vector<_Tp>& vec) +{ + FileNodeIterator it = n.begin(); + it >> vec; +} + +/** @brief Reads KeyPoint from a file storage. +*/ +//It needs special handling because it contains two types of fields, int & float. +static inline +void operator >> (const FileNode& n, std::vector& vec) +{ + read(n, vec); +} +/** @brief Reads DMatch from a file storage. +*/ +//It needs special handling because it contains two types of fields, int & float. +static inline +void operator >> (const FileNode& n, std::vector& vec) +{ + read(n, vec); +} + +//! @} FileNode + +//! @relates cv::FileNodeIterator +//! @{ + +static inline +bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it1.fs == it2.fs && it1.container == it2.container && + it1.reader.ptr == it2.reader.ptr && it1.remaining == it2.remaining; +} + +static inline +bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return !(it1 == it2); +} + +static inline +ptrdiff_t operator - (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it2.remaining - it1.remaining; +} + +static inline +bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it1.remaining > it2.remaining; +} + +//! @} FileNodeIterator + +//! @cond IGNORED + +inline FileNode FileStorage::getFirstTopLevelNode() const { FileNode r = root(); FileNodeIterator it = r.begin(); return it != r.end() ? *it : FileNode(); } +inline FileNode::FileNode() : fs(0), node(0) {} +inline FileNode::FileNode(const CvFileStorage* _fs, const CvFileNode* _node) : fs(_fs), node(_node) {} +inline FileNode::FileNode(const FileNode& _node) : fs(_node.fs), node(_node.node) {} +inline bool FileNode::empty() const { return node == 0; } +inline bool FileNode::isNone() const { return type() == NONE; } +inline bool FileNode::isSeq() const { return type() == SEQ; } +inline bool FileNode::isMap() const { return type() == MAP; } +inline bool FileNode::isInt() const { return type() == INT; } +inline bool FileNode::isReal() const { return type() == REAL; } +inline bool FileNode::isString() const { return type() == STR; } +inline CvFileNode* FileNode::operator *() { return (CvFileNode*)node; } +inline const CvFileNode* FileNode::operator* () const { return node; } +inline FileNode::operator int() const { int value; read(*this, value, 0); return value; } +inline FileNode::operator float() const { float value; read(*this, value, 0.f); return value; } +inline FileNode::operator double() const { double value; read(*this, value, 0.); return value; } +inline FileNode::operator String() const { String value; read(*this, value, value); return value; } +inline double FileNode::real() const { return double(*this); } +inline String FileNode::string() const { return String(*this); } +inline Mat FileNode::mat() const { Mat value; read(*this, value, value); return value; } +inline FileNodeIterator FileNode::begin() const { return FileNodeIterator(fs, node); } +inline FileNodeIterator FileNode::end() const { return FileNodeIterator(fs, node, size()); } +inline void FileNode::readRaw( const String& fmt, uchar* vec, size_t len ) const { begin().readRaw( fmt, vec, len ); } +inline FileNode FileNodeIterator::operator *() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } +inline FileNode FileNodeIterator::operator ->() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } +inline String::String(const FileNode& fn): cstr_(0), len_(0) { read(fn, *this, *this); } + +//! @endcond + + +CV_EXPORTS void cvStartWriteRawData_Base64(::CvFileStorage * fs, const char* name, int len, const char* dt); + +CV_EXPORTS void cvWriteRawData_Base64(::CvFileStorage * fs, const void* _data, int len); + +CV_EXPORTS void cvEndWriteRawData_Base64(::CvFileStorage * fs); + +CV_EXPORTS void cvWriteMat_Base64(::CvFileStorage* fs, const char* name, const ::CvMat* mat); + +CV_EXPORTS void cvWriteMatND_Base64(::CvFileStorage* fs, const char* name, const ::CvMatND* mat); + +} // cv + +#endif // OPENCV_CORE_PERSISTENCE_HPP diff --git a/thirdparty/linux/include/opencv2/core/private.cuda.hpp b/thirdparty/linux/include/opencv2/core/private.cuda.hpp new file mode 100644 index 0000000..01a4ab3 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/private.cuda.hpp @@ -0,0 +1,172 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PRIVATE_CUDA_HPP +#define OPENCV_CORE_PRIVATE_CUDA_HPP + +#ifndef __OPENCV_BUILD +# error this is a private header which should not be used from outside of the OpenCV library +#endif + +#include "cvconfig.h" + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" + +#include "opencv2/core/cuda.hpp" + +#ifdef HAVE_CUDA +# include +# include +# include +# include "opencv2/core/cuda_stream_accessor.hpp" +# include "opencv2/core/cuda/common.hpp" + +# define NPP_VERSION (NPP_VERSION_MAJOR * 1000 + NPP_VERSION_MINOR * 100 + NPP_VERSION_BUILD) + +# define CUDART_MINIMUM_REQUIRED_VERSION 6050 + +# if (CUDART_VERSION < CUDART_MINIMUM_REQUIRED_VERSION) +# error "Insufficient Cuda Runtime library version, please update it." +# endif + +# if defined(CUDA_ARCH_BIN_OR_PTX_10) +# error "OpenCV CUDA module doesn't support NVIDIA compute capability 1.0" +# endif +#endif + +//! @cond IGNORED + +namespace cv { namespace cuda { + CV_EXPORTS cv::String getNppErrorMessage(int code); + CV_EXPORTS cv::String getCudaDriverApiErrorMessage(int code); + + CV_EXPORTS GpuMat getInputMat(InputArray _src, Stream& stream); + + CV_EXPORTS GpuMat getOutputMat(OutputArray _dst, int rows, int cols, int type, Stream& stream); + static inline GpuMat getOutputMat(OutputArray _dst, Size size, int type, Stream& stream) + { + return getOutputMat(_dst, size.height, size.width, type, stream); + } + + CV_EXPORTS void syncOutput(const GpuMat& dst, OutputArray _dst, Stream& stream); +}} + +#ifndef HAVE_CUDA + +static inline void throw_no_cuda() { CV_Error(cv::Error::GpuNotSupported, "The library is compiled without CUDA support"); } + +#else // HAVE_CUDA + +static inline void throw_no_cuda() { CV_Error(cv::Error::StsNotImplemented, "The called functionality is disabled for current build or platform"); } + +namespace cv { namespace cuda +{ + class CV_EXPORTS BufferPool + { + public: + explicit BufferPool(Stream& stream); + + GpuMat getBuffer(int rows, int cols, int type); + GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); } + + GpuMat::Allocator* getAllocator() const { return allocator_; } + + private: + GpuMat::Allocator* allocator_; + }; + + static inline void checkNppError(int code, const char* file, const int line, const char* func) + { + if (code < 0) + cv::error(cv::Error::GpuApiCallError, getNppErrorMessage(code), func, file, line); + } + + static inline void checkCudaDriverApiError(int code, const char* file, const int line, const char* func) + { + if (code != CUDA_SUCCESS) + cv::error(cv::Error::GpuApiCallError, getCudaDriverApiErrorMessage(code), func, file, line); + } + + template struct NPPTypeTraits; + template<> struct NPPTypeTraits { typedef Npp8u npp_type; }; + template<> struct NPPTypeTraits { typedef Npp8s npp_type; }; + template<> struct NPPTypeTraits { typedef Npp16u npp_type; }; + template<> struct NPPTypeTraits { typedef Npp16s npp_type; }; + template<> struct NPPTypeTraits { typedef Npp32s npp_type; }; + template<> struct NPPTypeTraits { typedef Npp32f npp_type; }; + template<> struct NPPTypeTraits { typedef Npp64f npp_type; }; + + class NppStreamHandler + { + public: + inline explicit NppStreamHandler(Stream& newStream) + { + oldStream = nppGetStream(); + nppSetStream(StreamAccessor::getStream(newStream)); + } + + inline explicit NppStreamHandler(cudaStream_t newStream) + { + oldStream = nppGetStream(); + nppSetStream(newStream); + } + + inline ~NppStreamHandler() + { + nppSetStream(oldStream); + } + + private: + cudaStream_t oldStream; + }; +}} + +#define nppSafeCall(expr) cv::cuda::checkNppError(expr, __FILE__, __LINE__, CV_Func) +#define cuSafeCall(expr) cv::cuda::checkCudaDriverApiError(expr, __FILE__, __LINE__, CV_Func) + +#endif // HAVE_CUDA + +//! @endcond + +#endif // OPENCV_CORE_PRIVATE_CUDA_HPP diff --git a/thirdparty/linux/include/opencv2/core/private.hpp b/thirdparty/linux/include/opencv2/core/private.hpp new file mode 100644 index 0000000..e428ecf --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/private.hpp @@ -0,0 +1,585 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PRIVATE_HPP +#define OPENCV_CORE_PRIVATE_HPP + +#ifndef __OPENCV_BUILD +# error this is a private header which should not be used from outside of the OpenCV library +#endif + +#include "opencv2/core.hpp" +#include "cvconfig.h" + +#ifdef HAVE_EIGEN +# if defined __GNUC__ && defined __APPLE__ +# pragma GCC diagnostic ignored "-Wshadow" +# endif +# include +# include "opencv2/core/eigen.hpp" +#endif + +#ifdef HAVE_TBB +# include "tbb/tbb.h" +# include "tbb/task.h" +# undef min +# undef max +#endif + +#if defined HAVE_FP16 && (defined __F16C__ || (defined _MSC_VER && _MSC_VER >= 1700)) +# include +# define CV_FP16 1 +#elif defined HAVE_FP16 && defined __GNUC__ +# define CV_FP16 1 +#endif + +#ifndef CV_FP16 +# define CV_FP16 0 +#endif + +//! @cond IGNORED + +namespace cv +{ +#ifdef HAVE_TBB + + typedef tbb::blocked_range BlockedRange; + + template static inline + void parallel_for( const BlockedRange& range, const Body& body ) + { + tbb::parallel_for(range, body); + } + + typedef tbb::split Split; + + template static inline + void parallel_reduce( const BlockedRange& range, Body& body ) + { + tbb::parallel_reduce(range, body); + } + + typedef tbb::concurrent_vector ConcurrentRectVector; +#else + class BlockedRange + { + public: + BlockedRange() : _begin(0), _end(0), _grainsize(0) {} + BlockedRange(int b, int e, int g=1) : _begin(b), _end(e), _grainsize(g) {} + int begin() const { return _begin; } + int end() const { return _end; } + int grainsize() const { return _grainsize; } + + protected: + int _begin, _end, _grainsize; + }; + + template static inline + void parallel_for( const BlockedRange& range, const Body& body ) + { + body(range); + } + typedef std::vector ConcurrentRectVector; + + class Split {}; + + template static inline + void parallel_reduce( const BlockedRange& range, Body& body ) + { + body(range); + } +#endif + + // Returns a static string if there is a parallel framework, + // NULL otherwise. + CV_EXPORTS const char* currentParallelFramework(); +} //namespace cv + +/****************************************************************************************\ +* Common declarations * +\****************************************************************************************/ + +/* the alignment of all the allocated buffers */ +#define CV_MALLOC_ALIGN 16 + +/* IEEE754 constants and macros */ +#define CV_TOGGLE_FLT(x) ((x)^((int)(x) < 0 ? 0x7fffffff : 0)) +#define CV_TOGGLE_DBL(x) ((x)^((int64)(x) < 0 ? CV_BIG_INT(0x7fffffffffffffff) : 0)) + +static inline void* cvAlignPtr( const void* ptr, int align = 32 ) +{ + CV_DbgAssert ( (align & (align-1)) == 0 ); + return (void*)( ((size_t)ptr + align - 1) & ~(size_t)(align-1) ); +} + +static inline int cvAlign( int size, int align ) +{ + CV_DbgAssert( (align & (align-1)) == 0 && size < INT_MAX ); + return (size + align - 1) & -align; +} + +#ifdef IPL_DEPTH_8U +static inline cv::Size cvGetMatSize( const CvMat* mat ) +{ + return cv::Size(mat->cols, mat->rows); +} +#endif + +namespace cv +{ +CV_EXPORTS void scalarToRawData(const cv::Scalar& s, void* buf, int type, int unroll_to = 0); +} + +// property implementation macros + +#define CV_IMPL_PROPERTY_RO(type, name, member) \ + inline type get##name() const { return member; } + +#define CV_HELP_IMPL_PROPERTY(r_type, w_type, name, member) \ + CV_IMPL_PROPERTY_RO(r_type, name, member) \ + inline void set##name(w_type val) { member = val; } + +#define CV_HELP_WRAP_PROPERTY(r_type, w_type, name, internal_name, internal_obj) \ + r_type get##name() const { return internal_obj.get##internal_name(); } \ + void set##name(w_type val) { internal_obj.set##internal_name(val); } + +#define CV_IMPL_PROPERTY(type, name, member) CV_HELP_IMPL_PROPERTY(type, type, name, member) +#define CV_IMPL_PROPERTY_S(type, name, member) CV_HELP_IMPL_PROPERTY(type, const type &, name, member) + +#define CV_WRAP_PROPERTY(type, name, internal_name, internal_obj) CV_HELP_WRAP_PROPERTY(type, type, name, internal_name, internal_obj) +#define CV_WRAP_PROPERTY_S(type, name, internal_name, internal_obj) CV_HELP_WRAP_PROPERTY(type, const type &, name, internal_name, internal_obj) + +#define CV_WRAP_SAME_PROPERTY(type, name, internal_obj) CV_WRAP_PROPERTY(type, name, name, internal_obj) +#define CV_WRAP_SAME_PROPERTY_S(type, name, internal_obj) CV_WRAP_PROPERTY_S(type, name, name, internal_obj) + +/****************************************************************************************\ +* Structures and macros for integration with IPP * +\****************************************************************************************/ + +#ifdef HAVE_IPP +#include "ipp.h" + +#ifndef IPP_VERSION_UPDATE // prior to 7.1 +#define IPP_VERSION_UPDATE 0 +#endif + +#define IPP_VERSION_X100 (IPP_VERSION_MAJOR * 100 + IPP_VERSION_MINOR*10 + IPP_VERSION_UPDATE) + +// General define for ipp function disabling +#define IPP_DISABLE_BLOCK 0 + +#ifdef CV_MALLOC_ALIGN +#undef CV_MALLOC_ALIGN +#endif +#define CV_MALLOC_ALIGN 32 // required for AVX optimization + +#define setIppErrorStatus() cv::ipp::setIppStatus(-1, CV_Func, __FILE__, __LINE__) + +static inline IppiSize ippiSize(int width, int height) +{ + IppiSize size = { width, height }; + return size; +} + +static inline IppiSize ippiSize(const cv::Size & _size) +{ + IppiSize size = { _size.width, _size.height }; + return size; +} + +static inline IppiPoint ippiPoint(const cv::Point & _point) +{ + IppiPoint point = { _point.x, _point.y }; + return point; +} + +static inline IppiPoint ippiPoint(int x, int y) +{ + IppiPoint point = { x, y }; + return point; +} + +static inline IppiBorderType ippiGetBorderType(int borderTypeNI) +{ + return borderTypeNI == cv::BORDER_CONSTANT ? ippBorderConst : + borderTypeNI == cv::BORDER_WRAP ? ippBorderWrap : + borderTypeNI == cv::BORDER_REPLICATE ? ippBorderRepl : + borderTypeNI == cv::BORDER_REFLECT_101 ? ippBorderMirror : + borderTypeNI == cv::BORDER_REFLECT ? ippBorderMirrorR : (IppiBorderType)-1; +} + +static inline IppDataType ippiGetDataType(int depth) +{ + return depth == CV_8U ? ipp8u : + depth == CV_8S ? ipp8s : + depth == CV_16U ? ipp16u : + depth == CV_16S ? ipp16s : + depth == CV_32S ? ipp32s : + depth == CV_32F ? ipp32f : + depth == CV_64F ? ipp64f : (IppDataType)-1; +} + +// IPP temporary buffer hepler +template +class IppAutoBuffer +{ +public: + IppAutoBuffer() { m_pBuffer = NULL; } + IppAutoBuffer(int size) { Alloc(size); } + ~IppAutoBuffer() { Release(); } + T* Alloc(int size) { m_pBuffer = (T*)ippMalloc(size); return m_pBuffer; } + void Release() { if(m_pBuffer) ippFree(m_pBuffer); } + inline operator T* () { return (T*)m_pBuffer;} + inline operator const T* () const { return (const T*)m_pBuffer;} +private: + // Disable copy operations + IppAutoBuffer(IppAutoBuffer &) {} + IppAutoBuffer& operator =(const IppAutoBuffer &) {return *this;} + + T* m_pBuffer; +}; + +#else +#define IPP_VERSION_X100 0 +#endif + +#if defined HAVE_IPP +#if IPP_VERSION_X100 >= 900 +#define IPP_INITIALIZER(FEAT) \ +{ \ + if(FEAT) \ + ippSetCpuFeatures(FEAT); \ + else \ + ippInit(); \ +} +#elif IPP_VERSION_X100 >= 800 +#define IPP_INITIALIZER(FEAT) \ +{ \ + ippInit(); \ +} +#else +#define IPP_INITIALIZER(FEAT) \ +{ \ + ippStaticInit(); \ +} +#endif + +#ifdef CVAPI_EXPORTS +#define IPP_INITIALIZER_AUTO \ +struct __IppInitializer__ \ +{ \ + __IppInitializer__() \ + {IPP_INITIALIZER(cv::ipp::getIppFeatures())} \ +}; \ +static struct __IppInitializer__ __ipp_initializer__; +#else +#define IPP_INITIALIZER_AUTO +#endif +#else +#define IPP_INITIALIZER +#define IPP_INITIALIZER_AUTO +#endif + +#define CV_IPP_CHECK_COND (cv::ipp::useIPP()) +#define CV_IPP_CHECK() if(CV_IPP_CHECK_COND) + +#ifdef HAVE_IPP + +#ifdef CV_IPP_RUN_VERBOSE +#define CV_IPP_RUN_(condition, func, ...) \ + { \ + if (cv::ipp::useIPP() && (condition) && (func)) \ + { \ + printf("%s: IPP implementation is running\n", CV_Func); \ + fflush(stdout); \ + CV_IMPL_ADD(CV_IMPL_IPP); \ + return __VA_ARGS__; \ + } \ + else \ + { \ + printf("%s: Plain implementation is running\n", CV_Func); \ + fflush(stdout); \ + } \ + } +#elif defined CV_IPP_RUN_ASSERT +#define CV_IPP_RUN_(condition, func, ...) \ + { \ + if (cv::ipp::useIPP() && (condition)) \ + { \ + if(func) \ + { \ + CV_IMPL_ADD(CV_IMPL_IPP); \ + } \ + else \ + { \ + setIppErrorStatus(); \ + CV_Error(cv::Error::StsAssert, #func); \ + } \ + return __VA_ARGS__; \ + } \ + } +#else +#define CV_IPP_RUN_(condition, func, ...) \ + if (cv::ipp::useIPP() && (condition) && (func)) \ + { \ + CV_IMPL_ADD(CV_IMPL_IPP); \ + return __VA_ARGS__; \ + } +#endif +#define CV_IPP_RUN_FAST(func, ...) \ + if (cv::ipp::useIPP() && (func)) \ + { \ + CV_IMPL_ADD(CV_IMPL_IPP); \ + return __VA_ARGS__; \ + } +#else +#define CV_IPP_RUN_(condition, func, ...) +#define CV_IPP_RUN_FAST(func, ...) +#endif + +#define CV_IPP_RUN(condition, func, ...) CV_IPP_RUN_((condition), (func), __VA_ARGS__) + + +#ifndef IPPI_CALL +# define IPPI_CALL(func) CV_Assert((func) >= 0) +#endif + +/* IPP-compatible return codes */ +typedef enum CvStatus +{ + CV_BADMEMBLOCK_ERR = -113, + CV_INPLACE_NOT_SUPPORTED_ERR= -112, + CV_UNMATCHED_ROI_ERR = -111, + CV_NOTFOUND_ERR = -110, + CV_BADCONVERGENCE_ERR = -109, + + CV_BADDEPTH_ERR = -107, + CV_BADROI_ERR = -106, + CV_BADHEADER_ERR = -105, + CV_UNMATCHED_FORMATS_ERR = -104, + CV_UNSUPPORTED_COI_ERR = -103, + CV_UNSUPPORTED_CHANNELS_ERR = -102, + CV_UNSUPPORTED_DEPTH_ERR = -101, + CV_UNSUPPORTED_FORMAT_ERR = -100, + + CV_BADARG_ERR = -49, //ipp comp + CV_NOTDEFINED_ERR = -48, //ipp comp + + CV_BADCHANNELS_ERR = -47, //ipp comp + CV_BADRANGE_ERR = -44, //ipp comp + CV_BADSTEP_ERR = -29, //ipp comp + + CV_BADFLAG_ERR = -12, + CV_DIV_BY_ZERO_ERR = -11, //ipp comp + CV_BADCOEF_ERR = -10, + + CV_BADFACTOR_ERR = -7, + CV_BADPOINT_ERR = -6, + CV_BADSCALE_ERR = -4, + CV_OUTOFMEM_ERR = -3, + CV_NULLPTR_ERR = -2, + CV_BADSIZE_ERR = -1, + CV_NO_ERR = 0, + CV_OK = CV_NO_ERR +} +CvStatus; + +#ifdef HAVE_TEGRA_OPTIMIZATION +namespace tegra { + +CV_EXPORTS bool useTegra(); +CV_EXPORTS void setUseTegra(bool flag); + +} +#endif + +#ifdef ENABLE_INSTRUMENTATION +namespace cv +{ +namespace instr +{ +struct InstrTLSStruct +{ + InstrTLSStruct() + { + pCurrentNode = NULL; + } + InstrNode* pCurrentNode; +}; + +class InstrStruct +{ +public: + InstrStruct() + { + useInstr = false; + flags = FLAGS_MAPPING; + maxDepth = 0; + + rootNode.m_payload = NodeData("ROOT", NULL, 0, NULL, false, TYPE_GENERAL, IMPL_PLAIN); + tlsStruct.get()->pCurrentNode = &rootNode; + } + + Mutex mutexCreate; + Mutex mutexCount; + + bool useInstr; + int flags; + int maxDepth; + InstrNode rootNode; + TLSData tlsStruct; +}; + +class CV_EXPORTS IntrumentationRegion +{ +public: + IntrumentationRegion(const char* funName, const char* fileName, int lineNum, void *retAddress, bool alwaysExpand, TYPE instrType = TYPE_GENERAL, IMPL implType = IMPL_PLAIN); + ~IntrumentationRegion(); + +private: + bool m_disabled; // region status + uint64 m_regionTicks; +}; + +CV_EXPORTS InstrStruct& getInstrumentStruct(); +InstrTLSStruct& getInstrumentTLSStruct(); +CV_EXPORTS InstrNode* getCurrentNode(); +} +} + +#ifdef _WIN32 +#define CV_INSTRUMENT_GET_RETURN_ADDRESS _ReturnAddress() +#else +#define CV_INSTRUMENT_GET_RETURN_ADDRESS __builtin_extract_return_addr(__builtin_return_address(0)) +#endif + +// Instrument region +#define CV_INSTRUMENT_REGION_META(NAME, ALWAYS_EXPAND, TYPE, IMPL) ::cv::instr::IntrumentationRegion __instr_region__(NAME, __FILE__, __LINE__, CV_INSTRUMENT_GET_RETURN_ADDRESS, ALWAYS_EXPAND, TYPE, IMPL); +#define CV_INSTRUMENT_REGION_CUSTOM_META(NAME, ALWAYS_EXPAND, TYPE, IMPL)\ + void *__curr_address__ = [&]() {return CV_INSTRUMENT_GET_RETURN_ADDRESS;}();\ + ::cv::instr::IntrumentationRegion __instr_region__(NAME, __FILE__, __LINE__, __curr_address__, false, ::cv::instr::TYPE_GENERAL, ::cv::instr::IMPL_PLAIN); +// Instrument functions with non-void return type +#define CV_INSTRUMENT_FUN_RT_META(TYPE, IMPL, ERROR_COND, FUN, ...) ([&]()\ +{\ + if(::cv::instr::useInstrumentation()){\ + ::cv::instr::IntrumentationRegion __instr__(#FUN, __FILE__, __LINE__, NULL, false, TYPE, IMPL);\ + try{\ + auto status = ((FUN)(__VA_ARGS__));\ + if(ERROR_COND){\ + ::cv::instr::getCurrentNode()->m_payload.m_funError = true;\ + CV_INSTRUMENT_MARK_META(IMPL, #FUN " - BadExit");\ + }\ + return status;\ + }catch(...){\ + ::cv::instr::getCurrentNode()->m_payload.m_funError = true;\ + CV_INSTRUMENT_MARK_META(IMPL, #FUN " - BadExit");\ + throw;\ + }\ + }else{\ + return ((FUN)(__VA_ARGS__));\ + }\ +}()) +// Instrument functions with void return type +#define CV_INSTRUMENT_FUN_RV_META(TYPE, IMPL, FUN, ...) ([&]()\ +{\ + if(::cv::instr::useInstrumentation()){\ + ::cv::instr::IntrumentationRegion __instr__(#FUN, __FILE__, __LINE__, NULL, false, TYPE, IMPL);\ + try{\ + (FUN)(__VA_ARGS__);\ + }catch(...){\ + ::cv::instr::getCurrentNode()->m_payload.m_funError = true;\ + CV_INSTRUMENT_MARK_META(IMPL, #FUN "- BadExit");\ + throw;\ + }\ + }else{\ + (FUN)(__VA_ARGS__);\ + }\ +}()) +// Instrumentation information marker +#define CV_INSTRUMENT_MARK_META(IMPL, NAME, ...) {::cv::instr::IntrumentationRegion __instr_mark__(NAME, __FILE__, __LINE__, NULL, false, ::cv::instr::TYPE_MARKER, IMPL);} + +///// General instrumentation +// General OpenCV region instrumentation macro +#define CV_INSTRUMENT_REGION() CV_INSTRUMENT_REGION_META(__FUNCTION__, false, ::cv::instr::TYPE_GENERAL, ::cv::instr::IMPL_PLAIN) +// Custom OpenCV region instrumentation macro +#define CV_INSTRUMENT_REGION_NAME(NAME) CV_INSTRUMENT_REGION_CUSTOM_META(NAME, false, ::cv::instr::TYPE_GENERAL, ::cv::instr::IMPL_PLAIN) +// Instrumentation for parallel_for_ or other regions which forks and gathers threads +#define CV_INSTRUMENT_REGION_MT_FORK() CV_INSTRUMENT_REGION_META(__FUNCTION__, true, ::cv::instr::TYPE_GENERAL, ::cv::instr::IMPL_PLAIN); + +///// IPP instrumentation +// Wrapper region instrumentation macro +#define CV_INSTRUMENT_REGION_IPP() CV_INSTRUMENT_REGION_META(__FUNCTION__, false, ::cv::instr::TYPE_WRAPPER, ::cv::instr::IMPL_IPP) +// Function instrumentation macro +#define CV_INSTRUMENT_FUN_IPP(FUN, ...) CV_INSTRUMENT_FUN_RT_META(::cv::instr::TYPE_FUN, ::cv::instr::IMPL_IPP, status < 0, FUN, __VA_ARGS__) +// Diagnostic markers +#define CV_INSTRUMENT_MARK_IPP(NAME) CV_INSTRUMENT_MARK_META(::cv::instr::IMPL_IPP, NAME) + +///// OpenCL instrumentation +// Wrapper region instrumentation macro +#define CV_INSTRUMENT_REGION_OPENCL() CV_INSTRUMENT_REGION_META(__FUNCTION__, false, ::cv::instr::TYPE_WRAPPER, ::cv::instr::IMPL_OPENCL) +// OpenCL kernel compilation wrapper +#define CV_INSTRUMENT_REGION_OPENCL_COMPILE(NAME) CV_INSTRUMENT_REGION_META(NAME, false, ::cv::instr::TYPE_WRAPPER, ::cv::instr::IMPL_OPENCL) +// OpenCL kernel run wrapper +#define CV_INSTRUMENT_REGION_OPENCL_RUN(NAME) CV_INSTRUMENT_REGION_META(NAME, false, ::cv::instr::TYPE_FUN, ::cv::instr::IMPL_OPENCL) +// Diagnostic markers +#define CV_INSTRUMENT_MARK_OPENCL(NAME) CV_INSTRUMENT_MARK_META(::cv::instr::IMPL_OPENCL, NAME) +#else +#define CV_INSTRUMENT_REGION_META(...) + +#define CV_INSTRUMENT_REGION() +#define CV_INSTRUMENT_REGION_NAME(...) +#define CV_INSTRUMENT_REGION_MT_FORK() + +#define CV_INSTRUMENT_REGION_IPP() +#define CV_INSTRUMENT_FUN_IPP(FUN, ...) ((FUN)(__VA_ARGS__)) +#define CV_INSTRUMENT_MARK_IPP(...) + +#define CV_INSTRUMENT_REGION_OPENCL() +#define CV_INSTRUMENT_REGION_OPENCL_COMPILE(...) +#define CV_INSTRUMENT_REGION_OPENCL_RUN(...) +#define CV_INSTRUMENT_MARK_OPENCL(...) +#endif + +//! @endcond + +#endif // OPENCV_CORE_PRIVATE_HPP diff --git a/thirdparty/linux/include/opencv2/core/ptr.inl.hpp b/thirdparty/linux/include/opencv2/core/ptr.inl.hpp new file mode 100644 index 0000000..3c095a1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/ptr.inl.hpp @@ -0,0 +1,379 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, NVIDIA Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the copyright holders or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PTR_INL_HPP +#define OPENCV_CORE_PTR_INL_HPP + +#include + +//! @cond IGNORED + +namespace cv { + +template +void DefaultDeleter::operator () (Y* p) const +{ + delete p; +} + +namespace detail +{ + +struct PtrOwner +{ + PtrOwner() : refCount(1) + {} + + void incRef() + { + CV_XADD(&refCount, 1); + } + + void decRef() + { + if (CV_XADD(&refCount, -1) == 1) deleteSelf(); + } + +protected: + /* This doesn't really need to be virtual, since PtrOwner is never deleted + directly, but it doesn't hurt and it helps avoid warnings. */ + virtual ~PtrOwner() + {} + + virtual void deleteSelf() = 0; + +private: + unsigned int refCount; + + // noncopyable + PtrOwner(const PtrOwner&); + PtrOwner& operator = (const PtrOwner&); +}; + +template +struct PtrOwnerImpl : PtrOwner +{ + PtrOwnerImpl(Y* p, D d) : owned(p), deleter(d) + {} + + void deleteSelf() + { + deleter(owned); + delete this; + } + +private: + Y* owned; + D deleter; +}; + + +} + +template +Ptr::Ptr() : owner(NULL), stored(NULL) +{} + +template +template +Ptr::Ptr(Y* p) + : owner(p + ? new detail::PtrOwnerImpl >(p, DefaultDeleter()) + : NULL), + stored(p) +{} + +template +template +Ptr::Ptr(Y* p, D d) + : owner(p + ? new detail::PtrOwnerImpl(p, d) + : NULL), + stored(p) +{} + +template +Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) +{ + if (owner) owner->incRef(); +} + +template +template +Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) +{ + if (owner) owner->incRef(); +} + +template +template +Ptr::Ptr(const Ptr& o, T* p) : owner(o.owner), stored(p) +{ + if (owner) owner->incRef(); +} + +template +Ptr::~Ptr() +{ + release(); +} + +template +Ptr& Ptr::operator = (const Ptr& o) +{ + Ptr(o).swap(*this); + return *this; +} + +template +template +Ptr& Ptr::operator = (const Ptr& o) +{ + Ptr(o).swap(*this); + return *this; +} + +template +void Ptr::release() +{ + if (owner) owner->decRef(); + owner = NULL; + stored = NULL; +} + +template +template +void Ptr::reset(Y* p) +{ + Ptr(p).swap(*this); +} + +template +template +void Ptr::reset(Y* p, D d) +{ + Ptr(p, d).swap(*this); +} + +template +void Ptr::swap(Ptr& o) +{ + std::swap(owner, o.owner); + std::swap(stored, o.stored); +} + +template +T* Ptr::get() const +{ + return stored; +} + +template +typename detail::RefOrVoid::type Ptr::operator * () const +{ + return *stored; +} + +template +T* Ptr::operator -> () const +{ + return stored; +} + +template +Ptr::operator T* () const +{ + return stored; +} + + +template +bool Ptr::empty() const +{ + return !stored; +} + +template +template +Ptr Ptr::staticCast() const +{ + return Ptr(*this, static_cast(stored)); +} + +template +template +Ptr Ptr::constCast() const +{ + return Ptr(*this, const_cast(stored)); +} + +template +template +Ptr Ptr::dynamicCast() const +{ + return Ptr(*this, dynamic_cast(stored)); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +template +Ptr::Ptr(Ptr&& o) : owner(o.owner), stored(o.stored) +{ + o.owner = NULL; + o.stored = NULL; +} + +template +Ptr& Ptr::operator = (Ptr&& o) +{ + if (this == &o) + return *this; + + release(); + owner = o.owner; + stored = o.stored; + o.owner = NULL; + o.stored = NULL; + return *this; +} + +#endif + + +template +void swap(Ptr& ptr1, Ptr& ptr2){ + ptr1.swap(ptr2); +} + +template +bool operator == (const Ptr& ptr1, const Ptr& ptr2) +{ + return ptr1.get() == ptr2.get(); +} + +template +bool operator != (const Ptr& ptr1, const Ptr& ptr2) +{ + return ptr1.get() != ptr2.get(); +} + +template +Ptr makePtr() +{ + return Ptr(new T()); +} + +template +Ptr makePtr(const A1& a1) +{ + return Ptr(new T(a1)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2) +{ + return Ptr(new T(a1, a2)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3) +{ + return Ptr(new T(a1, a2, a3)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4) +{ + return Ptr(new T(a1, a2, a3, a4)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5) +{ + return Ptr(new T(a1, a2, a3, a4, a5)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11, const A12& a12) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12)); +} +} // namespace cv + +//! @endcond + +#endif // OPENCV_CORE_PTR_INL_HPP diff --git a/thirdparty/linux/include/opencv2/core/saturate.hpp b/thirdparty/linux/include/opencv2/core/saturate.hpp new file mode 100644 index 0000000..79a9a66 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/saturate.hpp @@ -0,0 +1,150 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_SATURATE_HPP +#define OPENCV_CORE_SATURATE_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/fast_math.hpp" + +namespace cv +{ + +//! @addtogroup core_utils +//! @{ + +/////////////// saturate_cast (used in image & signal processing) /////////////////// + +/** @brief Template function for accurate conversion from one primitive type to another. + + The functions saturate_cast resemble the standard C++ cast operations, such as static_cast\() + and others. They perform an efficient and accurate conversion from one primitive type to another + (see the introduction chapter). saturate in the name means that when the input value v is out of the + range of the target type, the result is not formed just by taking low bits of the input, but instead + the value is clipped. For example: + @code + uchar a = saturate_cast(-100); // a = 0 (UCHAR_MIN) + short b = saturate_cast(33333.33333); // b = 32767 (SHRT_MAX) + @endcode + Such clipping is done when the target type is unsigned char , signed char , unsigned short or + signed short . For 32-bit integers, no clipping is done. + + When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit), + the floating-point value is first rounded to the nearest integer and then clipped if needed (when + the target type is 8- or 16-bit). + + This operation is used in the simplest or most complex image processing functions in OpenCV. + + @param v Function parameter. + @sa add, subtract, multiply, divide, Mat::convertTo + */ +template static inline _Tp saturate_cast(uchar v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(schar v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(ushort v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(short v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(unsigned v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(int v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(float v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(double v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(int64 v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(uint64 v) { return _Tp(v); } + +template<> inline uchar saturate_cast(schar v) { return (uchar)std::max((int)v, 0); } +template<> inline uchar saturate_cast(ushort v) { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); } +template<> inline uchar saturate_cast(int v) { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } +template<> inline uchar saturate_cast(short v) { return saturate_cast((int)v); } +template<> inline uchar saturate_cast(unsigned v) { return (uchar)std::min(v, (unsigned)UCHAR_MAX); } +template<> inline uchar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline uchar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline uchar saturate_cast(int64 v) { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } +template<> inline uchar saturate_cast(uint64 v) { return (uchar)std::min(v, (uint64)UCHAR_MAX); } + +template<> inline schar saturate_cast(uchar v) { return (schar)std::min((int)v, SCHAR_MAX); } +template<> inline schar saturate_cast(ushort v) { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); } +template<> inline schar saturate_cast(int v) { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } +template<> inline schar saturate_cast(short v) { return saturate_cast((int)v); } +template<> inline schar saturate_cast(unsigned v) { return (schar)std::min(v, (unsigned)SCHAR_MAX); } +template<> inline schar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline schar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline schar saturate_cast(int64 v) { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } +template<> inline schar saturate_cast(uint64 v) { return (schar)std::min(v, (uint64)SCHAR_MAX); } + +template<> inline ushort saturate_cast(schar v) { return (ushort)std::max((int)v, 0); } +template<> inline ushort saturate_cast(short v) { return (ushort)std::max((int)v, 0); } +template<> inline ushort saturate_cast(int v) { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } +template<> inline ushort saturate_cast(unsigned v) { return (ushort)std::min(v, (unsigned)USHRT_MAX); } +template<> inline ushort saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline ushort saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline ushort saturate_cast(int64 v) { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } +template<> inline ushort saturate_cast(uint64 v) { return (ushort)std::min(v, (uint64)USHRT_MAX); } + +template<> inline short saturate_cast(ushort v) { return (short)std::min((int)v, SHRT_MAX); } +template<> inline short saturate_cast(int v) { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } +template<> inline short saturate_cast(unsigned v) { return (short)std::min(v, (unsigned)SHRT_MAX); } +template<> inline short saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline short saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline short saturate_cast(int64 v) { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } +template<> inline short saturate_cast(uint64 v) { return (short)std::min(v, (uint64)SHRT_MAX); } + +template<> inline int saturate_cast(float v) { return cvRound(v); } +template<> inline int saturate_cast(double v) { return cvRound(v); } + +// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. +template<> inline unsigned saturate_cast(float v) { return cvRound(v); } +template<> inline unsigned saturate_cast(double v) { return cvRound(v); } + +//! @} + +} // cv + +#endif // OPENCV_CORE_SATURATE_HPP diff --git a/thirdparty/linux/include/opencv2/core/sse_utils.hpp b/thirdparty/linux/include/opencv2/core/sse_utils.hpp new file mode 100644 index 0000000..69efffe --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/sse_utils.hpp @@ -0,0 +1,652 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_SSE_UTILS_HPP +#define OPENCV_CORE_SSE_UTILS_HPP + +#ifndef __cplusplus +# error sse_utils.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils_sse +//! @{ + +#if CV_SSE2 + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g0); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g0); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_g1); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_g1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk3); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk3); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk3); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk3); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk2); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk2); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk3); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk3); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk2); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk2); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk3); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk3); +} + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g1); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g1); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b0); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b0); + __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_b1); + __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_b1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk5); + __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk5); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk5); + __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk5); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk3); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk3); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk4); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk4); + __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk5); + __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk5); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk3); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk3); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk4); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk4); + v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk5); + v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk5); +} + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_b0); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_b0); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b1); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b1); + __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_a0); + __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_a0); + __m128i layer1_chunk6 = _mm_unpacklo_epi8(v_g1, v_a1); + __m128i layer1_chunk7 = _mm_unpackhi_epi8(v_g1, v_a1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk6 = _mm_unpacklo_epi8(layer1_chunk3, layer1_chunk7); + __m128i layer2_chunk7 = _mm_unpackhi_epi8(layer1_chunk3, layer1_chunk7); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk6 = _mm_unpacklo_epi8(layer2_chunk3, layer2_chunk7); + __m128i layer3_chunk7 = _mm_unpackhi_epi8(layer2_chunk3, layer2_chunk7); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk4); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk4); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk5); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk5); + __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk6); + __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk6); + __m128i layer4_chunk6 = _mm_unpacklo_epi8(layer3_chunk3, layer3_chunk7); + __m128i layer4_chunk7 = _mm_unpackhi_epi8(layer3_chunk3, layer3_chunk7); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk4); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk4); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk5); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk5); + v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk6); + v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk6); + v_a0 = _mm_unpacklo_epi8(layer4_chunk3, layer4_chunk7); + v_a1 = _mm_unpackhi_epi8(layer4_chunk3, layer4_chunk7); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); + v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer4_chunk6 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); + __m128i layer4_chunk7 = _mm_packus_epi16(_mm_srli_epi16(v_a0, 8), _mm_srli_epi16(v_a1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); + __m128i layer3_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_and_si128(layer4_chunk6, v_mask), _mm_and_si128(layer4_chunk7, v_mask)); + __m128i layer3_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk6, 8), _mm_srli_epi16(layer4_chunk7, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); + __m128i layer2_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk6, 8), _mm_srli_epi16(layer3_chunk7, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); + __m128i layer1_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk6, 8), _mm_srli_epi16(layer2_chunk7, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); + v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_a0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); + v_g1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); + v_a1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk6, 8), _mm_srli_epi16(layer1_chunk7, 8)); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g0); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g0); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_g1); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_g1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk3); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk3); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk3); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk3); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk2); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk2); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk3); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk3); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g1); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g1); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b0); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b0); + __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_b1); + __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_b1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk5); + __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk5); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk5); + __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk5); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk3); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk3); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk4); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk4); + v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk5); + v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk5); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_b0); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_b0); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b1); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b1); + __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_a0); + __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_a0); + __m128i layer1_chunk6 = _mm_unpacklo_epi16(v_g1, v_a1); + __m128i layer1_chunk7 = _mm_unpackhi_epi16(v_g1, v_a1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk6 = _mm_unpacklo_epi16(layer1_chunk3, layer1_chunk7); + __m128i layer2_chunk7 = _mm_unpackhi_epi16(layer1_chunk3, layer1_chunk7); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk6 = _mm_unpacklo_epi16(layer2_chunk3, layer2_chunk7); + __m128i layer3_chunk7 = _mm_unpackhi_epi16(layer2_chunk3, layer2_chunk7); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk4); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk4); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk5); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk5); + v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk6); + v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk6); + v_a0 = _mm_unpacklo_epi16(layer3_chunk3, layer3_chunk7); + v_a1 = _mm_unpackhi_epi16(layer3_chunk3, layer3_chunk7); +} + +#if CV_SSE4_1 + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); +} + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); + v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); +} + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer3_chunk6 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); + __m128i layer3_chunk7 = _mm_packus_epi32(_mm_srli_epi32(v_a0, 16), _mm_srli_epi32(v_a1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); + __m128i layer2_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk6, 16), _mm_srli_epi32(layer3_chunk7, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); + __m128i layer1_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk6, 16), _mm_srli_epi32(layer2_chunk7, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); + v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_a0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); + v_g1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); + v_a1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk6, 16), _mm_srli_epi32(layer1_chunk7, 16)); +} + +#endif // CV_SSE4_1 + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g0); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g0); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_g1); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_g1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk2); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk2); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk3); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk3); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk2); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk2); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk3); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk3); +} + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, + __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g1); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g1); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b0); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b0); + __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_b1); + __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_b1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk3); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk3); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk4); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk4); + __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk5); + __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk5); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk3); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk3); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk4); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk4); + v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk5); + v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk5); +} + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, + __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_b0); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_b0); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b1); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b1); + __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_a0); + __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_a0); + __m128 layer1_chunk6 = _mm_unpacklo_ps(v_g1, v_a1); + __m128 layer1_chunk7 = _mm_unpackhi_ps(v_g1, v_a1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk4); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk4); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk5); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk5); + __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk6); + __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk6); + __m128 layer2_chunk6 = _mm_unpacklo_ps(layer1_chunk3, layer1_chunk7); + __m128 layer2_chunk7 = _mm_unpackhi_ps(layer1_chunk3, layer1_chunk7); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk4); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk4); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk5); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk5); + v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk6); + v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk6); + v_a0 = _mm_unpacklo_ps(layer2_chunk3, layer2_chunk7); + v_a1 = _mm_unpackhi_ps(layer2_chunk3, layer2_chunk7); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) +{ + const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1); + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_g0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_g1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, + __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) +{ + const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1); + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk4 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); + __m128 layer2_chunk5 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); + __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_g1 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_b0 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); + v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); + v_b1 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, + __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) +{ + const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1); + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk4 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk5 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); + __m128 layer2_chunk6 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_a0, v_a1, mask_lo); + __m128 layer2_chunk7 = _mm_shuffle_ps(v_a0, v_a1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); + __m128 layer1_chunk6 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_lo); + __m128 layer1_chunk7 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_b0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_b1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); + v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); + v_a0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); + v_g1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_lo); + v_a1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_hi); +} + +#endif // CV_SSE2 + +//! @} + +#endif //OPENCV_CORE_SSE_UTILS_HPP diff --git a/thirdparty/linux/include/opencv2/core/traits.hpp b/thirdparty/linux/include/opencv2/core/traits.hpp new file mode 100644 index 0000000..f83b05f --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/traits.hpp @@ -0,0 +1,326 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TRAITS_HPP +#define OPENCV_CORE_TRAITS_HPP + +#include "opencv2/core/cvdef.h" + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +/** @brief Template "trait" class for OpenCV primitive data types. + +A primitive OpenCV data type is one of unsigned char, bool, signed char, unsigned short, signed +short, int, float, double, or a tuple of values of one of these types, where all the values in the +tuple have the same type. Any primitive type from the list can be defined by an identifier in the +form CV_\{U|S|F}C(\), for example: uchar \~ CV_8UC1, 3-element +floating-point tuple \~ CV_32FC3, and so on. A universal OpenCV structure that is able to store a +single instance of such a primitive data type is Vec. Multiple instances of such a type can be +stored in a std::vector, Mat, Mat_, SparseMat, SparseMat_, or any other container that is able to +store Vec instances. + +The DataType class is basically used to provide a description of such primitive data types without +adding any fields or methods to the corresponding classes (and it is actually impossible to add +anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not +DataType itself that is used but its specialized versions, such as: +@code + template<> class DataType + { + typedef uchar value_type; + typedef int work_type; + typedef uchar channel_type; + enum { channel_type = CV_8U, channels = 1, fmt='u', type = CV_8U }; + }; + ... + template DataType > + { + typedef std::complex<_Tp> value_type; + typedef std::complex<_Tp> work_type; + typedef _Tp channel_type; + // DataDepth is another helper trait class + enum { depth = DataDepth<_Tp>::value, channels=2, + fmt=(channels-1)*256+DataDepth<_Tp>::fmt, + type=CV_MAKETYPE(depth, channels) }; + }; + ... +@endcode +The main purpose of this class is to convert compilation-time type information to an +OpenCV-compatible data type identifier, for example: +@code + // allocates a 30x40 floating-point matrix + Mat A(30, 40, DataType::type); + + Mat B = Mat_ >(3, 3); + // the statement below will print 6, 2 , that is depth == CV_64F, channels == 2 + cout << B.depth() << ", " << B.channels() << endl; +@endcode +So, such traits are used to tell OpenCV which data type you are working with, even if such a type is +not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV +defines the proper specialized template class DataType\ \> . This mechanism is also +useful (and used in OpenCV this way) for generic algorithms implementations. +*/ +template class DataType +{ +public: + typedef _Tp value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 1, + depth = -1, + channels = 1, + fmt = 0, + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef bool value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8U, + channels = 1, + fmt = (int)'u', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef uchar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8U, + channels = 1, + fmt = (int)'u', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef schar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8S, + channels = 1, + fmt = (int)'c', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef schar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8S, + channels = 1, + fmt = (int)'c', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef ushort value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_16U, + channels = 1, + fmt = (int)'w', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef short value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_16S, + channels = 1, + fmt = (int)'s', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef int value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_32S, + channels = 1, + fmt = (int)'i', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef float value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_32F, + channels = 1, + fmt = (int)'f', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef double value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_64F, + channels = 1, + fmt = (int)'d', + type = CV_MAKETYPE(depth, channels) + }; +}; + + +/** @brief A helper class for cv::DataType + +The class is specialized for each fundamental numerical data type supported by OpenCV. It provides +DataDepth::value constant. +*/ +template class DataDepth +{ +public: + enum + { + value = DataType<_Tp>::depth, + fmt = DataType<_Tp>::fmt + }; +}; + + + +template class TypeDepth +{ + enum { depth = CV_USRTYPE1 }; + typedef void value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_8U }; + typedef uchar value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_8S }; + typedef schar value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_16U }; + typedef ushort value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_16S }; + typedef short value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_32S }; + typedef int value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_32F }; + typedef float value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_64F }; + typedef double value_type; +}; + +//! @} + +} // cv + +#endif // OPENCV_CORE_TRAITS_HPP diff --git a/thirdparty/linux/include/opencv2/core/types.hpp b/thirdparty/linux/include/opencv2/core/types.hpp new file mode 100644 index 0000000..d5c64ca --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/types.hpp @@ -0,0 +1,2264 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TYPES_HPP +#define OPENCV_CORE_TYPES_HPP + +#ifndef __cplusplus +# error types.hpp header must be compiled as C++ +#endif + +#include +#include +#include +#include + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/matx.hpp" + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +//////////////////////////////// Complex ////////////////////////////// + +/** @brief A complex number class. + + The template class is similar and compatible with std::complex, however it provides slightly + more convenient access to the real and imaginary parts using through the simple field access, as opposite + to std::complex::real() and std::complex::imag(). +*/ +template class Complex +{ +public: + + //! constructors + Complex(); + Complex( _Tp _re, _Tp _im = 0 ); + + //! conversion to another data type + template operator Complex() const; + //! conjugation + Complex conj() const; + + _Tp re, im; //< the real and the imaginary parts +}; + +typedef Complex Complexf; +typedef Complex Complexd; + +template class DataType< Complex<_Tp> > +{ +public: + typedef Complex<_Tp> value_type; + typedef value_type work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) }; + + typedef Vec vec_type; +}; + + + +//////////////////////////////// Point_ //////////////////////////////// + +/** @brief Template class for 2D points specified by its coordinates `x` and `y`. + +An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is +also a cast operator to convert point coordinates to the specified type. The conversion from +floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion +uses this operation for each of the coordinates. Besides the class members listed in the +declaration above, the following operations on points are implemented: +@code + pt1 = pt2 + pt3; + pt1 = pt2 - pt3; + pt1 = pt2 * a; + pt1 = a * pt2; + pt1 = pt2 / a; + pt1 += pt2; + pt1 -= pt2; + pt1 *= a; + pt1 /= a; + double value = norm(pt); // L2 norm + pt1 == pt2; + pt1 != pt2; +@endcode +For your convenience, the following type aliases are defined: +@code + typedef Point_ Point2i; + typedef Point2i Point; + typedef Point_ Point2f; + typedef Point_ Point2d; +@endcode +Example: +@code + Point2f a(0.3f, 0.f), b(0.f, 0.4f); + Point pt = (a + b)*10.f; + cout << pt.x << ", " << pt.y << endl; +@endcode +*/ +template class Point_ +{ +public: + typedef _Tp value_type; + + // various constructors + Point_(); + Point_(_Tp _x, _Tp _y); + Point_(const Point_& pt); + Point_(const Size_<_Tp>& sz); + Point_(const Vec<_Tp, 2>& v); + + Point_& operator = (const Point_& pt); + //! conversion to another data type + template operator Point_<_Tp2>() const; + + //! conversion to the old-style C structures + operator Vec<_Tp, 2>() const; + + //! dot product + _Tp dot(const Point_& pt) const; + //! dot product computed in double-precision arithmetics + double ddot(const Point_& pt) const; + //! cross-product + double cross(const Point_& pt) const; + //! checks whether the point is inside the specified rectangle + bool inside(const Rect_<_Tp>& r) const; + + _Tp x, y; //< the point coordinates +}; + +typedef Point_ Point2i; +typedef Point_ Point2l; +typedef Point_ Point2f; +typedef Point_ Point2d; +typedef Point2i Point; + +template class DataType< Point_<_Tp> > +{ +public: + typedef Point_<_Tp> value_type; + typedef Point_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +//////////////////////////////// Point3_ //////////////////////////////// + +/** @brief Template class for 3D points specified by its coordinates `x`, `y` and `z`. + +An instance of the class is interchangeable with the C structure CvPoint2D32f . Similarly to +Point_ , the coordinates of 3D points can be converted to another type. The vector arithmetic and +comparison operations are also supported. + +The following Point3_\<\> aliases are available: +@code + typedef Point3_ Point3i; + typedef Point3_ Point3f; + typedef Point3_ Point3d; +@endcode +@see cv::Point3i, cv::Point3f and cv::Point3d +*/ +template class Point3_ +{ +public: + typedef _Tp value_type; + + // various constructors + Point3_(); + Point3_(_Tp _x, _Tp _y, _Tp _z); + Point3_(const Point3_& pt); + explicit Point3_(const Point_<_Tp>& pt); + Point3_(const Vec<_Tp, 3>& v); + + Point3_& operator = (const Point3_& pt); + //! conversion to another data type + template operator Point3_<_Tp2>() const; + //! conversion to cv::Vec<> +#if OPENCV_ABI_COMPATIBILITY > 300 + template operator Vec<_Tp2, 3>() const; +#else + operator Vec<_Tp, 3>() const; +#endif + + //! dot product + _Tp dot(const Point3_& pt) const; + //! dot product computed in double-precision arithmetics + double ddot(const Point3_& pt) const; + //! cross product of the 2 3D points + Point3_ cross(const Point3_& pt) const; + + _Tp x, y, z; //< the point coordinates +}; + +typedef Point3_ Point3i; +typedef Point3_ Point3f; +typedef Point3_ Point3d; + +template class DataType< Point3_<_Tp> > +{ +public: + typedef Point3_<_Tp> value_type; + typedef Point3_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 3, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +//////////////////////////////// Size_ //////////////////////////////// + +/** @brief Template class for specifying the size of an image or rectangle. + +The class includes two members called width and height. The structure can be converted to and from +the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison +operations as for Point_ is available. + +OpenCV defines the following Size_\<\> aliases: +@code + typedef Size_ Size2i; + typedef Size2i Size; + typedef Size_ Size2f; +@endcode +*/ +template class Size_ +{ +public: + typedef _Tp value_type; + + //! various constructors + Size_(); + Size_(_Tp _width, _Tp _height); + Size_(const Size_& sz); + Size_(const Point_<_Tp>& pt); + + Size_& operator = (const Size_& sz); + //! the area (width*height) + _Tp area() const; + + //! conversion of another data type. + template operator Size_<_Tp2>() const; + + _Tp width, height; // the width and the height +}; + +typedef Size_ Size2i; +typedef Size_ Size2l; +typedef Size_ Size2f; +typedef Size_ Size2d; +typedef Size2i Size; + +template class DataType< Size_<_Tp> > +{ +public: + typedef Size_<_Tp> value_type; + typedef Size_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +//////////////////////////////// Rect_ //////////////////////////////// + +/** @brief Template class for 2D rectangles + +described by the following parameters: +- Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y + in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner. +- Rectangle width and height. + +OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the +right and bottom boundaries are not. For example, the method Rect_::contains returns true if + +\f[x \leq pt.x < x+width, + y \leq pt.y < y+height\f] + +Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\ ) is +implemented as: +@code + for(int y = roi.y; y < roi.y + roi.height; y++) + for(int x = roi.x; x < roi.x + roi.width; x++) + { + // ... + } +@endcode +In addition to the class members, the following operations on rectangles are implemented: +- \f$\texttt{rect} = \texttt{rect} \pm \texttt{point}\f$ (shifting a rectangle by a certain offset) +- \f$\texttt{rect} = \texttt{rect} \pm \texttt{size}\f$ (expanding or shrinking a rectangle by a + certain amount) +- rect += point, rect -= point, rect += size, rect -= size (augmenting operations) +- rect = rect1 & rect2 (rectangle intersection) +- rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 ) +- rect &= rect1, rect |= rect1 (and the corresponding augmenting operations) +- rect == rect1, rect != rect1 (rectangle comparison) + +This is an example how the partial ordering on rectangles can be established (rect1 \f$\subseteq\f$ +rect2): +@code + template inline bool + operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2) + { + return (r1 & r2) == r1; + } +@endcode +For your convenience, the Rect_\<\> alias is available: cv::Rect +*/ +template class Rect_ +{ +public: + typedef _Tp value_type; + + //! various constructors + Rect_(); + Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height); + Rect_(const Rect_& r); + Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz); + Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2); + + Rect_& operator = ( const Rect_& r ); + //! the top-left corner + Point_<_Tp> tl() const; + //! the bottom-right corner + Point_<_Tp> br() const; + + //! size (width, height) of the rectangle + Size_<_Tp> size() const; + //! area (width*height) of the rectangle + _Tp area() const; + + //! conversion to another data type + template operator Rect_<_Tp2>() const; + + //! checks whether the rectangle contains the point + bool contains(const Point_<_Tp>& pt) const; + + _Tp x, y, width, height; //< the top-left corner, as well as width and height of the rectangle +}; + +typedef Rect_ Rect2i; +typedef Rect_ Rect2f; +typedef Rect_ Rect2d; +typedef Rect2i Rect; + +template class DataType< Rect_<_Tp> > +{ +public: + typedef Rect_<_Tp> value_type; + typedef Rect_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 4, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +///////////////////////////// RotatedRect ///////////////////////////// + +/** @brief The class represents rotated (i.e. not up-right) rectangles on a plane. + +Each rectangle is specified by the center point (mass center), length of each side (represented by +cv::Size2f structure) and the rotation angle in degrees. + +The sample below demonstrates how to use RotatedRect: +@code + Mat image(200, 200, CV_8UC3, Scalar(0)); + RotatedRect rRect = RotatedRect(Point2f(100,100), Size2f(100,50), 30); + + Point2f vertices[4]; + rRect.points(vertices); + for (int i = 0; i < 4; i++) + line(image, vertices[i], vertices[(i+1)%4], Scalar(0,255,0)); + + Rect brect = rRect.boundingRect(); + rectangle(image, brect, Scalar(255,0,0)); + + imshow("rectangles", image); + waitKey(0); +@endcode +![image](pics/rotatedrect.png) + +@sa CamShift, fitEllipse, minAreaRect, CvBox2D +*/ +class CV_EXPORTS RotatedRect +{ +public: + //! various constructors + RotatedRect(); + /** + @param center The rectangle mass center. + @param size Width and height of the rectangle. + @param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc., + the rectangle becomes an up-right rectangle. + */ + RotatedRect(const Point2f& center, const Size2f& size, float angle); + /** + Any 3 end points of the RotatedRect. They must be given in order (either clockwise or + anticlockwise). + */ + RotatedRect(const Point2f& point1, const Point2f& point2, const Point2f& point3); + + /** returns 4 vertices of the rectangle + @param pts The points array for storing rectangle vertices. + */ + void points(Point2f pts[]) const; + //! returns the minimal up-right integer rectangle containing the rotated rectangle + Rect boundingRect() const; + //! returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images + Rect_ boundingRect2f() const; + + Point2f center; //< the rectangle mass center + Size2f size; //< width and height of the rectangle + float angle; //< the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle. +}; + +template<> class DataType< RotatedRect > +{ +public: + typedef RotatedRect value_type; + typedef value_type work_type; + typedef float channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)sizeof(value_type)/sizeof(channel_type), // 5 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +//////////////////////////////// Range ///////////////////////////////// + +/** @brief Template class specifying a continuous subsequence (slice) of a sequence. + +The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. +Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an +inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a +half-opened interval is usually denoted as \f$[start,end)\f$ . + +The static method Range::all() returns a special variable that means "the whole sequence" or "the +whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in +OpenCV that take Range support this special Range::all() value. But, of course, in case of your own +custom processing, you will probably have to check and handle it explicitly: +@code + void my_function(..., const Range& r, ....) + { + if(r == Range::all()) { + // process all the data + } + else { + // process [r.start, r.end) + } + } +@endcode +*/ +class CV_EXPORTS Range +{ +public: + Range(); + Range(int _start, int _end); + int size() const; + bool empty() const; + static Range all(); + + int start, end; +}; + +template<> class DataType +{ +public: + typedef Range value_type; + typedef value_type work_type; + typedef int channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +//////////////////////////////// Scalar_ /////////////////////////////// + +/** @brief Template class for a 4-element vector derived from Vec. + +Being derived from Vec\<_Tp, 4\> , Scalar_ and Scalar can be used just as typical 4-element +vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in +OpenCV to pass pixel values. +*/ +template class Scalar_ : public Vec<_Tp, 4> +{ +public: + //! various constructors + Scalar_(); + Scalar_(_Tp v0, _Tp v1, _Tp v2=0, _Tp v3=0); + Scalar_(_Tp v0); + + template + Scalar_(const Vec<_Tp2, cn>& v); + + //! returns a scalar with all elements set to v0 + static Scalar_<_Tp> all(_Tp v0); + + //! conversion to another data type + template operator Scalar_() const; + + //! per-element product + Scalar_<_Tp> mul(const Scalar_<_Tp>& a, double scale=1 ) const; + + // returns (v0, -v1, -v2, -v3) + Scalar_<_Tp> conj() const; + + // returns true iff v1 == v2 == v3 == 0 + bool isReal() const; +}; + +typedef Scalar_ Scalar; + +template class DataType< Scalar_<_Tp> > +{ +public: + typedef Scalar_<_Tp> value_type; + typedef Scalar_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 4, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +/////////////////////////////// KeyPoint //////////////////////////////// + +/** @brief Data structure for salient point detectors. + +The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint +detectors, such as Harris corner detector, cv::FAST, cv::StarDetector, cv::SURF, cv::SIFT, +cv::LDetector etc. + +The keypoint is characterized by the 2D position, scale (proportional to the diameter of the +neighborhood that needs to be taken into account), orientation and some other parameters. The +keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually +represented as a feature vector). The keypoints representing the same object in different images +can then be matched using cv::KDTree or another method. +*/ +class CV_EXPORTS_W_SIMPLE KeyPoint +{ +public: + //! the default constructor + CV_WRAP KeyPoint(); + /** + @param _pt x & y coordinates of the keypoint + @param _size keypoint diameter + @param _angle keypoint orientation + @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param _octave pyramid octave in which the keypoint has been detected + @param _class_id object id + */ + KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); + /** + @param x x-coordinate of the keypoint + @param y y-coordinate of the keypoint + @param _size keypoint diameter + @param _angle keypoint orientation + @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param _octave pyramid octave in which the keypoint has been detected + @param _class_id object id + */ + CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); + + size_t hash() const; + + /** + This method converts vector of keypoints to vector of points or the reverse, where each keypoint is + assigned the same size and the same orientation. + + @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + @param points2f Array of (x,y) coordinates of each keypoint + @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to + convert only specified keypoints) + */ + CV_WRAP static void convert(const std::vector& keypoints, + CV_OUT std::vector& points2f, + const std::vector& keypointIndexes=std::vector()); + /** @overload + @param points2f Array of (x,y) coordinates of each keypoint + @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + @param size keypoint diameter + @param response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param octave pyramid octave in which the keypoint has been detected + @param class_id object id + */ + CV_WRAP static void convert(const std::vector& points2f, + CV_OUT std::vector& keypoints, + float size=1, float response=1, int octave=0, int class_id=-1); + + /** + This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint + regions' intersection and area of keypoint regions' union (considering keypoint region as circle). + If they don't overlap, we get zero. If they coincide at same location with same size, we get 1. + @param kp1 First keypoint + @param kp2 Second keypoint + */ + CV_WRAP static float overlap(const KeyPoint& kp1, const KeyPoint& kp2); + + CV_PROP_RW Point2f pt; //!< coordinates of the keypoints + CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood + CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable); + //!< it's in [0,360) degrees and measured relative to + //!< image coordinate system, ie in clockwise. + CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling + CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted + CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to) +}; + +template<> class DataType +{ +public: + typedef KeyPoint value_type; + typedef float work_type; + typedef float channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 7 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +//////////////////////////////// DMatch ///////////////////////////////// + +/** @brief Class for matching keypoint descriptors + +query descriptor index, train descriptor index, train image index, and distance between +descriptors. +*/ +class CV_EXPORTS_W_SIMPLE DMatch +{ +public: + CV_WRAP DMatch(); + CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance); + CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance); + + CV_PROP_RW int queryIdx; // query descriptor index + CV_PROP_RW int trainIdx; // train descriptor index + CV_PROP_RW int imgIdx; // train image index + + CV_PROP_RW float distance; + + // less is better + bool operator<(const DMatch &m) const; +}; + +template<> class DataType +{ +public: + typedef DMatch value_type; + typedef int work_type; + typedef int channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 4 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + + + +///////////////////////////// TermCriteria ////////////////////////////// + +/** @brief The class defining termination criteria for iterative algorithms. + +You can initialize it by default constructor and then override any parameters, or the structure may +be fully initialized using the advanced variant of the constructor. +*/ +class CV_EXPORTS TermCriteria +{ +public: + /** + Criteria type, can be one of: COUNT, EPS or COUNT + EPS + */ + enum Type + { + COUNT=1, //!< the maximum number of iterations or elements to compute + MAX_ITER=COUNT, //!< ditto + EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops + }; + + //! default constructor + TermCriteria(); + /** + @param type The type of termination criteria, one of TermCriteria::Type + @param maxCount The maximum number of iterations or elements to compute. + @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops. + */ + TermCriteria(int type, int maxCount, double epsilon); + + int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS + int maxCount; // the maximum number of iterations/elements + double epsilon; // the desired accuracy +}; + + +//! @} core_basic + +///////////////////////// raster image moments ////////////////////////// + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief struct returned by cv::moments + +The spatial moments \f$\texttt{Moments::m}_{ji}\f$ are computed as: + +\f[\texttt{m} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot x^j \cdot y^i \right )\f] + +The central moments \f$\texttt{Moments::mu}_{ji}\f$ are computed as: + +\f[\texttt{mu} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot (x - \bar{x} )^j \cdot (y - \bar{y} )^i \right )\f] + +where \f$(\bar{x}, \bar{y})\f$ is the mass center: + +\f[\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}\f] + +The normalized central moments \f$\texttt{Moments::nu}_{ij}\f$ are computed as: + +\f[\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .\f] + +@note +\f$\texttt{mu}_{00}=\texttt{m}_{00}\f$, \f$\texttt{nu}_{00}=1\f$ +\f$\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0\f$ , hence the values are not +stored. + +The moments of a contour are defined in the same way but computed using the Green's formula (see +). So, due to a limited raster resolution, the moments +computed for a contour are slightly different from the moments computed for the same rasterized +contour. + +@note +Since the contour moments are computed using Green formula, you may get seemingly odd results for +contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours. + */ +class CV_EXPORTS_W_MAP Moments +{ +public: + //! the default constructor + Moments(); + //! the full constructor + Moments(double m00, double m10, double m01, double m20, double m11, + double m02, double m30, double m21, double m12, double m03 ); + ////! the conversion from CvMoments + //Moments( const CvMoments& moments ); + ////! the conversion to CvMoments + //operator CvMoments() const; + + //! @name spatial moments + //! @{ + CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; + //! @} + + //! @name central moments + //! @{ + CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03; + //! @} + + //! @name central normalized moments + //! @{ + CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03; + //! @} +}; + +template<> class DataType +{ +public: + typedef Moments value_type; + typedef double work_type; + typedef double channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 24 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; + +//! @} imgproc_shape + +//! @cond IGNORED + +///////////////////////////////////////////////////////////////////////// +///////////////////////////// Implementation //////////////////////////// +///////////////////////////////////////////////////////////////////////// + +//////////////////////////////// Complex //////////////////////////////// + +template inline +Complex<_Tp>::Complex() + : re(0), im(0) {} + +template inline +Complex<_Tp>::Complex( _Tp _re, _Tp _im ) + : re(_re), im(_im) {} + +template template inline +Complex<_Tp>::operator Complex() const +{ + return Complex(saturate_cast(re), saturate_cast(im)); +} + +template inline +Complex<_Tp> Complex<_Tp>::conj() const +{ + return Complex<_Tp>(re, -im); +} + + +template static inline +bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return a.re == b.re && a.im == b.im; +} + +template static inline +bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return a.re != b.re || a.im != b.im; +} + +template static inline +Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re + b.re, a.im + b.im ); +} + +template static inline +Complex<_Tp>& operator += (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a.re += b.re; a.im += b.im; + return a; +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re - b.re, a.im - b.im ); +} + +template static inline +Complex<_Tp>& operator -= (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a.re -= b.re; a.im -= b.im; + return a; +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a) +{ + return Complex<_Tp>(-a.re, -a.im); +} + +template static inline +Complex<_Tp> operator * (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re*b.re - a.im*b.im, a.re*b.im + a.im*b.re ); +} + +template static inline +Complex<_Tp> operator * (const Complex<_Tp>& a, _Tp b) +{ + return Complex<_Tp>( a.re*b, a.im*b ); +} + +template static inline +Complex<_Tp> operator * (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( a.re*b, a.im*b ); +} + +template static inline +Complex<_Tp> operator + (const Complex<_Tp>& a, _Tp b) +{ + return Complex<_Tp>( a.re + b, a.im ); +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a, _Tp b) +{ return Complex<_Tp>( a.re - b, a.im ); } + +template static inline +Complex<_Tp> operator + (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( a.re + b, a.im ); +} + +template static inline +Complex<_Tp> operator - (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( b - a.re, -a.im ); +} + +template static inline +Complex<_Tp>& operator += (Complex<_Tp>& a, _Tp b) +{ + a.re += b; return a; +} + +template static inline +Complex<_Tp>& operator -= (Complex<_Tp>& a, _Tp b) +{ + a.re -= b; return a; +} + +template static inline +Complex<_Tp>& operator *= (Complex<_Tp>& a, _Tp b) +{ + a.re *= b; a.im *= b; return a; +} + +template static inline +double abs(const Complex<_Tp>& a) +{ + return std::sqrt( (double)a.re*a.re + (double)a.im*a.im); +} + +template static inline +Complex<_Tp> operator / (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + double t = 1./((double)b.re*b.re + (double)b.im*b.im); + return Complex<_Tp>( (_Tp)((a.re*b.re + a.im*b.im)*t), + (_Tp)((-a.re*b.im + a.im*b.re)*t) ); +} + +template static inline +Complex<_Tp>& operator /= (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return (a = a / b); +} + +template static inline +Complex<_Tp> operator / (const Complex<_Tp>& a, _Tp b) +{ + _Tp t = (_Tp)1/b; + return Complex<_Tp>( a.re*t, a.im*t ); +} + +template static inline +Complex<_Tp> operator / (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>(b)/a; +} + +template static inline +Complex<_Tp> operator /= (const Complex<_Tp>& a, _Tp b) +{ + _Tp t = (_Tp)1/b; + a.re *= t; a.im *= t; return a; +} + + + +//////////////////////////////// 2D Point /////////////////////////////// + +template inline +Point_<_Tp>::Point_() + : x(0), y(0) {} + +template inline +Point_<_Tp>::Point_(_Tp _x, _Tp _y) + : x(_x), y(_y) {} + +template inline +Point_<_Tp>::Point_(const Point_& pt) + : x(pt.x), y(pt.y) {} + +template inline +Point_<_Tp>::Point_(const Size_<_Tp>& sz) + : x(sz.width), y(sz.height) {} + +template inline +Point_<_Tp>::Point_(const Vec<_Tp,2>& v) + : x(v[0]), y(v[1]) {} + +template inline +Point_<_Tp>& Point_<_Tp>::operator = (const Point_& pt) +{ + x = pt.x; y = pt.y; + return *this; +} + +template template inline +Point_<_Tp>::operator Point_<_Tp2>() const +{ + return Point_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y)); +} + +template inline +Point_<_Tp>::operator Vec<_Tp, 2>() const +{ + return Vec<_Tp, 2>(x, y); +} + +template inline +_Tp Point_<_Tp>::dot(const Point_& pt) const +{ + return saturate_cast<_Tp>(x*pt.x + y*pt.y); +} + +template inline +double Point_<_Tp>::ddot(const Point_& pt) const +{ + return (double)x*pt.x + (double)y*pt.y; +} + +template inline +double Point_<_Tp>::cross(const Point_& pt) const +{ + return (double)x*pt.y - (double)y*pt.x; +} + +template inline bool +Point_<_Tp>::inside( const Rect_<_Tp>& r ) const +{ + return r.contains(*this); +} + + +template static inline +Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b) +{ + a.x += b.x; + a.y += b.y; + return a; +} + +template static inline +Point_<_Tp>& operator -= (Point_<_Tp>& a, const Point_<_Tp>& b) +{ + a.x -= b.x; + a.y -= b.y; + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +double norm(const Point_<_Tp>& pt) +{ + return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y); +} + +template static inline +bool operator == (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y; +} + +template static inline +bool operator != (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y; +} + +template static inline +Point_<_Tp> operator + (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y) ); +} + +template static inline +Point_<_Tp> operator - (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y) ); +} + +template static inline +Point_<_Tp> operator - (const Point_<_Tp>& a) +{ + return Point_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, int b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (int a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, float b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (float a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, double b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (double a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b) +{ + Matx<_Tp, 2, 1> tmp = a * Vec<_Tp,2>(b.x, b.y); + return Point_<_Tp>(tmp.val[0], tmp.val[1]); +} + +template static inline +Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b) +{ + Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, 1); + return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, int b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, float b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, double b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + + + +//////////////////////////////// 3D Point /////////////////////////////// + +template inline +Point3_<_Tp>::Point3_() + : x(0), y(0), z(0) {} + +template inline +Point3_<_Tp>::Point3_(_Tp _x, _Tp _y, _Tp _z) + : x(_x), y(_y), z(_z) {} + +template inline +Point3_<_Tp>::Point3_(const Point3_& pt) + : x(pt.x), y(pt.y), z(pt.z) {} + +template inline +Point3_<_Tp>::Point3_(const Point_<_Tp>& pt) + : x(pt.x), y(pt.y), z(_Tp()) {} + +template inline +Point3_<_Tp>::Point3_(const Vec<_Tp, 3>& v) + : x(v[0]), y(v[1]), z(v[2]) {} + +template template inline +Point3_<_Tp>::operator Point3_<_Tp2>() const +{ + return Point3_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(z)); +} + +#if OPENCV_ABI_COMPATIBILITY > 300 +template template inline +Point3_<_Tp>::operator Vec<_Tp2, 3>() const +{ + return Vec<_Tp2, 3>(x, y, z); +} +#else +template inline +Point3_<_Tp>::operator Vec<_Tp, 3>() const +{ + return Vec<_Tp, 3>(x, y, z); +} +#endif + +template inline +Point3_<_Tp>& Point3_<_Tp>::operator = (const Point3_& pt) +{ + x = pt.x; y = pt.y; z = pt.z; + return *this; +} + +template inline +_Tp Point3_<_Tp>::dot(const Point3_& pt) const +{ + return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z); +} + +template inline +double Point3_<_Tp>::ddot(const Point3_& pt) const +{ + return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z; +} + +template inline +Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const +{ + return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x); +} + + +template static inline +Point3_<_Tp>& operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + a.x += b.x; + a.y += b.y; + a.z += b.z; + return a; +} + +template static inline +Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + a.x -= b.x; + a.y -= b.y; + a.z -= b.z; + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +double norm(const Point3_<_Tp>& pt) +{ + return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z); +} + +template static inline +bool operator == (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y && a.z == b.z; +} + +template static inline +bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y || a.z != b.z; +} + +template static inline +Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), saturate_cast<_Tp>(a.z + b.z)); +} + +template static inline +Point3_<_Tp> operator - (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y), saturate_cast<_Tp>(a.z - b.z)); +} + +template static inline +Point3_<_Tp> operator - (const Point3_<_Tp>& a) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y), saturate_cast<_Tp>(-a.z) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, int b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b), saturate_cast<_Tp>(a.z*b) ); +} + +template static inline +Point3_<_Tp> operator * (int a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, float b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); +} + +template static inline +Point3_<_Tp> operator * (float a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, double b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); +} + +template static inline +Point3_<_Tp> operator * (double a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b) +{ + Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, b.z); + return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); +} + +template static inline +Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b) +{ + return a * Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1); +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, int b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, float b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, double b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + + + +////////////////////////////////// Size ///////////////////////////////// + +template inline +Size_<_Tp>::Size_() + : width(0), height(0) {} + +template inline +Size_<_Tp>::Size_(_Tp _width, _Tp _height) + : width(_width), height(_height) {} + +template inline +Size_<_Tp>::Size_(const Size_& sz) + : width(sz.width), height(sz.height) {} + +template inline +Size_<_Tp>::Size_(const Point_<_Tp>& pt) + : width(pt.x), height(pt.y) {} + +template template inline +Size_<_Tp>::operator Size_<_Tp2>() const +{ + return Size_<_Tp2>(saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); +} + +template inline +Size_<_Tp>& Size_<_Tp>::operator = (const Size_<_Tp>& sz) +{ + width = sz.width; height = sz.height; + return *this; +} + +template inline +_Tp Size_<_Tp>::area() const +{ + return width * height; +} + +template static inline +Size_<_Tp>& operator *= (Size_<_Tp>& a, _Tp b) +{ + a.width *= b; + a.height *= b; + return a; +} + +template static inline +Size_<_Tp> operator * (const Size_<_Tp>& a, _Tp b) +{ + Size_<_Tp> tmp(a); + tmp *= b; + return tmp; +} + +template static inline +Size_<_Tp>& operator /= (Size_<_Tp>& a, _Tp b) +{ + a.width /= b; + a.height /= b; + return a; +} + +template static inline +Size_<_Tp> operator / (const Size_<_Tp>& a, _Tp b) +{ + Size_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Size_<_Tp>& operator += (Size_<_Tp>& a, const Size_<_Tp>& b) +{ + a.width += b.width; + a.height += b.height; + return a; +} + +template static inline +Size_<_Tp> operator + (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + Size_<_Tp> tmp(a); + tmp += b; + return tmp; +} + +template static inline +Size_<_Tp>& operator -= (Size_<_Tp>& a, const Size_<_Tp>& b) +{ + a.width -= b.width; + a.height -= b.height; + return a; +} + +template static inline +Size_<_Tp> operator - (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + Size_<_Tp> tmp(a); + tmp -= b; + return tmp; +} + +template static inline +bool operator == (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + return a.width == b.width && a.height == b.height; +} + +template static inline +bool operator != (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + return !(a == b); +} + + + +////////////////////////////////// Rect ///////////////////////////////// + +template inline +Rect_<_Tp>::Rect_() + : x(0), y(0), width(0), height(0) {} + +template inline +Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height) + : x(_x), y(_y), width(_width), height(_height) {} + +template inline +Rect_<_Tp>::Rect_(const Rect_<_Tp>& r) + : x(r.x), y(r.y), width(r.width), height(r.height) {} + +template inline +Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz) + : x(org.x), y(org.y), width(sz.width), height(sz.height) {} + +template inline +Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2) +{ + x = std::min(pt1.x, pt2.x); + y = std::min(pt1.y, pt2.y); + width = std::max(pt1.x, pt2.x) - x; + height = std::max(pt1.y, pt2.y) - y; +} + +template inline +Rect_<_Tp>& Rect_<_Tp>::operator = ( const Rect_<_Tp>& r ) +{ + x = r.x; + y = r.y; + width = r.width; + height = r.height; + return *this; +} + +template inline +Point_<_Tp> Rect_<_Tp>::tl() const +{ + return Point_<_Tp>(x,y); +} + +template inline +Point_<_Tp> Rect_<_Tp>::br() const +{ + return Point_<_Tp>(x + width, y + height); +} + +template inline +Size_<_Tp> Rect_<_Tp>::size() const +{ + return Size_<_Tp>(width, height); +} + +template inline +_Tp Rect_<_Tp>::area() const +{ + return width * height; +} + +template template inline +Rect_<_Tp>::operator Rect_<_Tp2>() const +{ + return Rect_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); +} + +template inline +bool Rect_<_Tp>::contains(const Point_<_Tp>& pt) const +{ + return x <= pt.x && pt.x < x + width && y <= pt.y && pt.y < y + height; +} + + +template static inline +Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Point_<_Tp>& b ) +{ + a.x += b.x; + a.y += b.y; + return a; +} + +template static inline +Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Point_<_Tp>& b ) +{ + a.x -= b.x; + a.y -= b.y; + return a; +} + +template static inline +Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Size_<_Tp>& b ) +{ + a.width += b.width; + a.height += b.height; + return a; +} + +template static inline +Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b ) +{ + a.width -= b.width; + a.height -= b.height; + return a; +} + +template static inline +Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) +{ + _Tp x1 = std::max(a.x, b.x); + _Tp y1 = std::max(a.y, b.y); + a.width = std::min(a.x + a.width, b.x + b.width) - x1; + a.height = std::min(a.y + a.height, b.y + b.height) - y1; + a.x = x1; + a.y = y1; + if( a.width <= 0 || a.height <= 0 ) + a = Rect(); + return a; +} + +template static inline +Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) +{ + _Tp x1 = std::min(a.x, b.x); + _Tp y1 = std::min(a.y, b.y); + a.width = std::max(a.x + a.width, b.x + b.width) - x1; + a.height = std::max(a.y + a.height, b.y + b.height) - y1; + a.x = x1; + a.y = y1; + return a; +} + +template static inline +bool operator == (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y && a.width == b.width && a.height == b.height; +} + +template static inline +bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height; +} + +template static inline +Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b) +{ + return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height ); +} + +template static inline +Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Point_<_Tp>& b) +{ + return Rect_<_Tp>( a.x - b.x, a.y - b.y, a.width, a.height ); +} + +template static inline +Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Size_<_Tp>& b) +{ + return Rect_<_Tp>( a.x, a.y, a.width + b.width, a.height + b.height ); +} + +template static inline +Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + Rect_<_Tp> c = a; + return c &= b; +} + +template static inline +Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + Rect_<_Tp> c = a; + return c |= b; +} + +/** + * @brief measure dissimilarity between two sample sets + * + * computes the complement of the Jaccard Index as described in . + * For rectangles this reduces to computing the intersection over the union. + */ +template static inline +double jaccardDistance(const Rect_<_Tp>& a, const Rect_<_Tp>& b) { + _Tp Aa = a.area(); + _Tp Ab = b.area(); + + if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) { + // jaccard_index = 1 -> distance = 0 + return 0.0; + } + + double Aab = (a & b).area(); + // distance = 1 - jaccard_index + return 1.0 - Aab / (Aa + Ab - Aab); +} + +////////////////////////////// RotatedRect ////////////////////////////// + +inline +RotatedRect::RotatedRect() + : center(), size(), angle(0) {} + +inline +RotatedRect::RotatedRect(const Point2f& _center, const Size2f& _size, float _angle) + : center(_center), size(_size), angle(_angle) {} + + + +///////////////////////////////// Range ///////////////////////////////// + +inline +Range::Range() + : start(0), end(0) {} + +inline +Range::Range(int _start, int _end) + : start(_start), end(_end) {} + +inline +int Range::size() const +{ + return end - start; +} + +inline +bool Range::empty() const +{ + return start == end; +} + +inline +Range Range::all() +{ + return Range(INT_MIN, INT_MAX); +} + + +static inline +bool operator == (const Range& r1, const Range& r2) +{ + return r1.start == r2.start && r1.end == r2.end; +} + +static inline +bool operator != (const Range& r1, const Range& r2) +{ + return !(r1 == r2); +} + +static inline +bool operator !(const Range& r) +{ + return r.start == r.end; +} + +static inline +Range operator & (const Range& r1, const Range& r2) +{ + Range r(std::max(r1.start, r2.start), std::min(r1.end, r2.end)); + r.end = std::max(r.end, r.start); + return r; +} + +static inline +Range& operator &= (Range& r1, const Range& r2) +{ + r1 = r1 & r2; + return r1; +} + +static inline +Range operator + (const Range& r1, int delta) +{ + return Range(r1.start + delta, r1.end + delta); +} + +static inline +Range operator + (int delta, const Range& r1) +{ + return Range(r1.start + delta, r1.end + delta); +} + +static inline +Range operator - (const Range& r1, int delta) +{ + return r1 + (-delta); +} + + + +///////////////////////////////// Scalar //////////////////////////////// + +template inline +Scalar_<_Tp>::Scalar_() +{ + this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0; +} + +template inline +Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3) +{ + this->val[0] = v0; + this->val[1] = v1; + this->val[2] = v2; + this->val[3] = v3; +} + +template template inline +Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v) +{ + int i; + for( i = 0; i < (cn < 4 ? cn : 4); i++ ) + this->val[i] = cv::saturate_cast<_Tp>(v.val[i]); + for( ; i < 4; i++ ) + this->val[i] = 0; +} + +template inline +Scalar_<_Tp>::Scalar_(_Tp v0) +{ + this->val[0] = v0; + this->val[1] = this->val[2] = this->val[3] = 0; +} + +template inline +Scalar_<_Tp> Scalar_<_Tp>::all(_Tp v0) +{ + return Scalar_<_Tp>(v0, v0, v0, v0); +} + + +template inline +Scalar_<_Tp> Scalar_<_Tp>::mul(const Scalar_<_Tp>& a, double scale ) const +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0] * a.val[0] * scale), + saturate_cast<_Tp>(this->val[1] * a.val[1] * scale), + saturate_cast<_Tp>(this->val[2] * a.val[2] * scale), + saturate_cast<_Tp>(this->val[3] * a.val[3] * scale)); +} + +template inline +Scalar_<_Tp> Scalar_<_Tp>::conj() const +{ + return Scalar_<_Tp>(saturate_cast<_Tp>( this->val[0]), + saturate_cast<_Tp>(-this->val[1]), + saturate_cast<_Tp>(-this->val[2]), + saturate_cast<_Tp>(-this->val[3])); +} + +template inline +bool Scalar_<_Tp>::isReal() const +{ + return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0; +} + + +template template inline +Scalar_<_Tp>::operator Scalar_() const +{ + return Scalar_(saturate_cast(this->val[0]), + saturate_cast(this->val[1]), + saturate_cast(this->val[2]), + saturate_cast(this->val[3])); +} + + +template static inline +Scalar_<_Tp>& operator += (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a.val[0] += b.val[0]; + a.val[1] += b.val[1]; + a.val[2] += b.val[2]; + a.val[3] += b.val[3]; + return a; +} + +template static inline +Scalar_<_Tp>& operator -= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a.val[0] -= b.val[0]; + a.val[1] -= b.val[1]; + a.val[2] -= b.val[2]; + a.val[3] -= b.val[3]; + return a; +} + +template static inline +Scalar_<_Tp>& operator *= ( Scalar_<_Tp>& a, _Tp v ) +{ + a.val[0] *= v; + a.val[1] *= v; + a.val[2] *= v; + a.val[3] *= v; + return a; +} + +template static inline +bool operator == ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) +{ + return a.val[0] == b.val[0] && a.val[1] == b.val[1] && + a.val[2] == b.val[2] && a.val[3] == b.val[3]; +} + +template static inline +bool operator != ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) +{ + return a.val[0] != b.val[0] || a.val[1] != b.val[1] || + a.val[2] != b.val[2] || a.val[3] != b.val[3]; +} + +template static inline +Scalar_<_Tp> operator + (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(a.val[0] + b.val[0], + a.val[1] + b.val[1], + a.val[2] + b.val[2], + a.val[3] + b.val[3]); +} + +template static inline +Scalar_<_Tp> operator - (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(a.val[0] - b.val[0]), + saturate_cast<_Tp>(a.val[1] - b.val[1]), + saturate_cast<_Tp>(a.val[2] - b.val[2]), + saturate_cast<_Tp>(a.val[3] - b.val[3])); +} + +template static inline +Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, _Tp alpha) +{ + return Scalar_<_Tp>(a.val[0] * alpha, + a.val[1] * alpha, + a.val[2] * alpha, + a.val[3] * alpha); +} + +template static inline +Scalar_<_Tp> operator * (_Tp alpha, const Scalar_<_Tp>& a) +{ + return a*alpha; +} + +template static inline +Scalar_<_Tp> operator - (const Scalar_<_Tp>& a) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(-a.val[0]), + saturate_cast<_Tp>(-a.val[1]), + saturate_cast<_Tp>(-a.val[2]), + saturate_cast<_Tp>(-a.val[3])); +} + + +template static inline +Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(a[0]*b[0] - a[1]*b[1] - a[2]*b[2] - a[3]*b[3]), + saturate_cast<_Tp>(a[0]*b[1] + a[1]*b[0] + a[2]*b[3] - a[3]*b[2]), + saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]), + saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0])); +} + +template static inline +Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a = a * b; + return a; +} + +template static inline +Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha) +{ + return Scalar_<_Tp>(a.val[0] / alpha, + a.val[1] / alpha, + a.val[2] / alpha, + a.val[3] / alpha); +} + +template static inline +Scalar_ operator / (const Scalar_& a, float alpha) +{ + float s = 1 / alpha; + return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); +} + +template static inline +Scalar_ operator / (const Scalar_& a, double alpha) +{ + double s = 1 / alpha; + return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); +} + +template static inline +Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha) +{ + a = a / alpha; + return a; +} + +template static inline +Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b) +{ + _Tp s = a / (b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]); + return b.conj() * s; +} + +template static inline +Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return a * ((_Tp)1 / b); +} + +template static inline +Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a = a / b; + return a; +} + +template static inline +Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b) +{ + Matx c((Matx)a, b, Matx_MatMulOp()); + return reinterpret_cast(c); +} + +template<> inline +Scalar operator * (const Matx& a, const Scalar& b) +{ + Matx c(a, b, Matx_MatMulOp()); + return reinterpret_cast(c); +} + + + +//////////////////////////////// KeyPoint /////////////////////////////// + +inline +KeyPoint::KeyPoint() + : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {} + +inline +KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle, float _response, int _octave, int _class_id) + : pt(_pt), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} + +inline +KeyPoint::KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id) + : pt(x, y), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} + + + +///////////////////////////////// DMatch //////////////////////////////// + +inline +DMatch::DMatch() + : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {} + +inline +DMatch::DMatch(int _queryIdx, int _trainIdx, float _distance) + : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {} + +inline +DMatch::DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance) + : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {} + +inline +bool DMatch::operator < (const DMatch &m) const +{ + return distance < m.distance; +} + + + +////////////////////////////// TermCriteria ///////////////////////////// + +inline +TermCriteria::TermCriteria() + : type(0), maxCount(0), epsilon(0) {} + +inline +TermCriteria::TermCriteria(int _type, int _maxCount, double _epsilon) + : type(_type), maxCount(_maxCount), epsilon(_epsilon) {} + +//! @endcond + +} // cv + +#endif //OPENCV_CORE_TYPES_HPP diff --git a/thirdparty/linux/include/opencv2/core/types_c.h b/thirdparty/linux/include/opencv2/core/types_c.h new file mode 100644 index 0000000..f82a59e --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/types_c.h @@ -0,0 +1,1837 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TYPES_H +#define OPENCV_CORE_TYPES_H + +#ifdef HAVE_IPL +# ifndef __IPL_H__ +# if defined WIN32 || defined _WIN32 +# include +# else +# include +# endif +# endif +#elif defined __IPL_H__ +# define HAVE_IPL +#endif + +#include "opencv2/core/cvdef.h" + +#ifndef SKIP_INCLUDES +#include +#include +#include +#include +#endif // SKIP_INCLUDES + +#if defined WIN32 || defined _WIN32 +# define CV_CDECL __cdecl +# define CV_STDCALL __stdcall +#else +# define CV_CDECL +# define CV_STDCALL +#endif + +#ifndef CV_DEFAULT +# ifdef __cplusplus +# define CV_DEFAULT(val) = val +# else +# define CV_DEFAULT(val) +# endif +#endif + +#ifndef CV_EXTERN_C_FUNCPTR +# ifdef __cplusplus +# define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; } +# else +# define CV_EXTERN_C_FUNCPTR(x) typedef x +# endif +#endif + +#ifndef CVAPI +# define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL +#endif + +#ifndef CV_IMPL +# define CV_IMPL CV_EXTERN_C +#endif + +#ifdef __cplusplus +# include "opencv2/core.hpp" +#endif + +/** @addtogroup core_c + @{ +*/ + +/** @brief This is the "metatype" used *only* as a function parameter. + +It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even +CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4 +bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray. + */ +typedef void CvArr; + +typedef int CVStatus; + +/** @see cv::Error::Code */ +enum { + CV_StsOk= 0, /**< everything is ok */ + CV_StsBackTrace= -1, /**< pseudo error for back trace */ + CV_StsError= -2, /**< unknown /unspecified error */ + CV_StsInternal= -3, /**< internal error (bad state) */ + CV_StsNoMem= -4, /**< insufficient memory */ + CV_StsBadArg= -5, /**< function arg/param is bad */ + CV_StsBadFunc= -6, /**< unsupported function */ + CV_StsNoConv= -7, /**< iter. didn't converge */ + CV_StsAutoTrace= -8, /**< tracing */ + CV_HeaderIsNull= -9, /**< image header is NULL */ + CV_BadImageSize= -10, /**< image size is invalid */ + CV_BadOffset= -11, /**< offset is invalid */ + CV_BadDataPtr= -12, /**/ + CV_BadStep= -13, /**/ + CV_BadModelOrChSeq= -14, /**/ + CV_BadNumChannels= -15, /**/ + CV_BadNumChannel1U= -16, /**/ + CV_BadDepth= -17, /**/ + CV_BadAlphaChannel= -18, /**/ + CV_BadOrder= -19, /**/ + CV_BadOrigin= -20, /**/ + CV_BadAlign= -21, /**/ + CV_BadCallBack= -22, /**/ + CV_BadTileSize= -23, /**/ + CV_BadCOI= -24, /**/ + CV_BadROISize= -25, /**/ + CV_MaskIsTiled= -26, /**/ + CV_StsNullPtr= -27, /**< null pointer */ + CV_StsVecLengthErr= -28, /**< incorrect vector length */ + CV_StsFilterStructContentErr= -29, /**< incorr. filter structure content */ + CV_StsKernelStructContentErr= -30, /**< incorr. transform kernel content */ + CV_StsFilterOffsetErr= -31, /**< incorrect filter offset value */ + CV_StsBadSize= -201, /**< the input/output structure size is incorrect */ + CV_StsDivByZero= -202, /**< division by zero */ + CV_StsInplaceNotSupported= -203, /**< in-place operation is not supported */ + CV_StsObjectNotFound= -204, /**< request can't be completed */ + CV_StsUnmatchedFormats= -205, /**< formats of input/output arrays differ */ + CV_StsBadFlag= -206, /**< flag is wrong or not supported */ + CV_StsBadPoint= -207, /**< bad CvPoint */ + CV_StsBadMask= -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/ + CV_StsUnmatchedSizes= -209, /**< sizes of input/output structures do not match */ + CV_StsUnsupportedFormat= -210, /**< the data format/type is not supported by the function*/ + CV_StsOutOfRange= -211, /**< some of parameters are out of range */ + CV_StsParseError= -212, /**< invalid syntax/structure of the parsed file */ + CV_StsNotImplemented= -213, /**< the requested function/feature is not implemented */ + CV_StsBadMemBlock= -214, /**< an allocated block has been corrupted */ + CV_StsAssert= -215, /**< assertion failed */ + CV_GpuNotSupported= -216, + CV_GpuApiCallError= -217, + CV_OpenGlNotSupported= -218, + CV_OpenGlApiCallError= -219, + CV_OpenCLApiCallError= -220, + CV_OpenCLDoubleNotSupported= -221, + CV_OpenCLInitError= -222, + CV_OpenCLNoAMDBlasFft= -223 +}; + +/****************************************************************************************\ +* Common macros and inline functions * +\****************************************************************************************/ + +#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t)) + +/** min & max without jumps */ +#define CV_IMIN(a, b) ((a) ^ (((a)^(b)) & (((a) < (b)) - 1))) + +#define CV_IMAX(a, b) ((a) ^ (((a)^(b)) & (((a) > (b)) - 1))) + +/** absolute value without jumps */ +#ifndef __cplusplus +# define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0)) +#else +# define CV_IABS(a) abs(a) +#endif +#define CV_CMP(a,b) (((a) > (b)) - ((a) < (b))) +#define CV_SIGN(a) CV_CMP((a),0) + +#define cvInvSqrt(value) ((float)(1./sqrt(value))) +#define cvSqrt(value) ((float)sqrt(value)) + + +/*************** Random number generation *******************/ + +typedef uint64 CvRNG; + +#define CV_RNG_COEFF 4164903690U + +/** @brief Initializes a random number generator state. + +The function initializes a random number generator and returns the state. The pointer to the state +can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current +implementation a multiply-with-carry generator is used. +@param seed 64-bit value used to initiate a random sequence +@sa the C++ class RNG replaced CvRNG. + */ +CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1)) +{ + CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1; + return rng; +} + +/** @brief Returns a 32-bit unsigned integer and updates RNG. + +The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG +state. It is similar to the rand() function from the C runtime library, except that OpenCV functions +always generates a 32-bit random number, regardless of the platform. +@param rng CvRNG state initialized by cvRNG. + */ +CV_INLINE unsigned cvRandInt( CvRNG* rng ) +{ + uint64 temp = *rng; + temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32); + *rng = temp; + return (unsigned)temp; +} + +/** @brief Returns a floating-point random number and updates RNG. + +The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not +included). +@param rng RNG state initialized by cvRNG + */ +CV_INLINE double cvRandReal( CvRNG* rng ) +{ + return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */; +} + +/****************************************************************************************\ +* Image type (IplImage) * +\****************************************************************************************/ + +#ifndef HAVE_IPL + +/* + * The following definitions (until #endif) + * is an extract from IPL headers. + * Copyright (c) 1995 Intel Corporation. + */ +#define IPL_DEPTH_SIGN 0x80000000 + +#define IPL_DEPTH_1U 1 +#define IPL_DEPTH_8U 8 +#define IPL_DEPTH_16U 16 +#define IPL_DEPTH_32F 32 + +#define IPL_DEPTH_8S (IPL_DEPTH_SIGN| 8) +#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16) +#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32) + +#define IPL_DATA_ORDER_PIXEL 0 +#define IPL_DATA_ORDER_PLANE 1 + +#define IPL_ORIGIN_TL 0 +#define IPL_ORIGIN_BL 1 + +#define IPL_ALIGN_4BYTES 4 +#define IPL_ALIGN_8BYTES 8 +#define IPL_ALIGN_16BYTES 16 +#define IPL_ALIGN_32BYTES 32 + +#define IPL_ALIGN_DWORD IPL_ALIGN_4BYTES +#define IPL_ALIGN_QWORD IPL_ALIGN_8BYTES + +#define IPL_BORDER_CONSTANT 0 +#define IPL_BORDER_REPLICATE 1 +#define IPL_BORDER_REFLECT 2 +#define IPL_BORDER_WRAP 3 + +/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV +only supports a subset of possible IplImage formats, as outlined in the parameter list above. + +In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require +that the image size or ROI size of all source and destination images match exactly. On the other +hand, the Intel Image Processing Library processes the area of intersection between the source and +destination images (or ROIs), allowing them to vary independently. +*/ +typedef struct +#ifdef __cplusplus + CV_EXPORTS +#endif +_IplImage +{ + int nSize; /**< sizeof(IplImage) */ + int ID; /**< version (=0)*/ + int nChannels; /**< Most of OpenCV functions support 1,2,3 or 4 channels */ + int alphaChannel; /**< Ignored by OpenCV */ + int depth; /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S, + IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */ + char colorModel[4]; /**< Ignored by OpenCV */ + char channelSeq[4]; /**< ditto */ + int dataOrder; /**< 0 - interleaved color channels, 1 - separate color channels. + cvCreateImage can only create interleaved images */ + int origin; /**< 0 - top-left origin, + 1 - bottom-left origin (Windows bitmaps style). */ + int align; /**< Alignment of image rows (4 or 8). + OpenCV ignores it and uses widthStep instead. */ + int width; /**< Image width in pixels. */ + int height; /**< Image height in pixels. */ + struct _IplROI *roi; /**< Image ROI. If NULL, the whole image is selected. */ + struct _IplImage *maskROI; /**< Must be NULL. */ + void *imageId; /**< " " */ + struct _IplTileInfo *tileInfo; /**< " " */ + int imageSize; /**< Image data size in bytes + (==image->height*image->widthStep + in case of interleaved data)*/ + char *imageData; /**< Pointer to aligned image data. */ + int widthStep; /**< Size of aligned image row in bytes. */ + int BorderMode[4]; /**< Ignored by OpenCV. */ + int BorderConst[4]; /**< Ditto. */ + char *imageDataOrigin; /**< Pointer to very origin of image data + (not necessarily aligned) - + needed for correct deallocation */ + +#ifdef __cplusplus + _IplImage() {} + _IplImage(const cv::Mat& m); +#endif +} +IplImage; + +typedef struct _IplTileInfo IplTileInfo; + +typedef struct _IplROI +{ + int coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/ + int xOffset; + int yOffset; + int width; + int height; +} +IplROI; + +typedef struct _IplConvKernel +{ + int nCols; + int nRows; + int anchorX; + int anchorY; + int *values; + int nShiftR; +} +IplConvKernel; + +typedef struct _IplConvKernelFP +{ + int nCols; + int nRows; + int anchorX; + int anchorY; + float *values; +} +IplConvKernelFP; + +#define IPL_IMAGE_HEADER 1 +#define IPL_IMAGE_DATA 2 +#define IPL_IMAGE_ROI 4 + +#endif/*HAVE_IPL*/ + +/** extra border mode */ +#define IPL_BORDER_REFLECT_101 4 +#define IPL_BORDER_TRANSPARENT 5 + +#define IPL_IMAGE_MAGIC_VAL ((int)sizeof(IplImage)) +#define CV_TYPE_NAME_IMAGE "opencv-image" + +#define CV_IS_IMAGE_HDR(img) \ + ((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage)) + +#define CV_IS_IMAGE(img) \ + (CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL) + +/** for storing double-precision + floating point data in IplImage's */ +#define IPL_DEPTH_64F 64 + +/** get reference to pixel at (col,row), + for multi-channel images (col) should be multiplied by number of channels */ +#define CV_IMAGE_ELEM( image, elemtype, row, col ) \ + (((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)]) + +/****************************************************************************************\ +* Matrix type (CvMat) * +\****************************************************************************************/ + +#define CV_AUTO_STEP 0x7fffffff +#define CV_WHOLE_ARR cvSlice( 0, 0x3fffffff ) + +#define CV_MAGIC_MASK 0xFFFF0000 +#define CV_MAT_MAGIC_VAL 0x42420000 +#define CV_TYPE_NAME_MAT "opencv-matrix" + +/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column +index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro: + + uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j) + CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f; + +To access multiple-channel matrices, you can use +CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx). + +@deprecated CvMat is now obsolete; consider using Mat instead. + */ +typedef struct CvMat +{ + int type; + int step; + + /* for internal use only */ + int* refcount; + int hdr_refcount; + + union + { + uchar* ptr; + short* s; + int* i; + float* fl; + double* db; + } data; + +#ifdef __cplusplus + union + { + int rows; + int height; + }; + + union + { + int cols; + int width; + }; +#else + int rows; + int cols; +#endif + + +#ifdef __cplusplus + CvMat() {} + CvMat(const CvMat& m) { memcpy(this, &m, sizeof(CvMat));} + CvMat(const cv::Mat& m); +#endif + +} +CvMat; + + +#define CV_IS_MAT_HDR(mat) \ + ((mat) != NULL && \ + (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ + ((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0) + +#define CV_IS_MAT_HDR_Z(mat) \ + ((mat) != NULL && \ + (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ + ((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0) + +#define CV_IS_MAT(mat) \ + (CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL) + +#define CV_IS_MASK_ARR(mat) \ + (((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0) + +#define CV_ARE_TYPES_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0) + +#define CV_ARE_CNS_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0) + +#define CV_ARE_DEPTHS_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0) + +#define CV_ARE_SIZES_EQ(mat1, mat2) \ + ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols) + +#define CV_IS_MAT_CONST(mat) \ + (((mat)->rows|(mat)->cols) == 1) + +#define IPL2CV_DEPTH(depth) \ + ((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \ + (CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \ + (((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15) + +/** Inline constructor. No data is allocated internally!!! + * (Use together with cvCreateData, or use cvCreateMat instead to + * get a matrix with allocated data): + */ +CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL)) +{ + CvMat m; + + assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F ); + type = CV_MAT_TYPE(type); + m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type; + m.cols = cols; + m.rows = rows; + m.step = m.cols*CV_ELEM_SIZE(type); + m.data.ptr = (uchar*)data; + m.refcount = NULL; + m.hdr_refcount = 0; + + return m; +} + +#ifdef __cplusplus +inline CvMat::CvMat(const cv::Mat& m) +{ + CV_DbgAssert(m.dims <= 2); + *this = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data); + step = (int)m.step[0]; + type = (type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG); +} +#endif + + +#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \ + (assert( (unsigned)(row) < (unsigned)(mat).rows && \ + (unsigned)(col) < (unsigned)(mat).cols ), \ + (mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col)) + +#define CV_MAT_ELEM_PTR( mat, row, col ) \ + CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) ) + +#define CV_MAT_ELEM( mat, elemtype, row, col ) \ + (*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype))) + +/** @brief Returns the particular element of single-channel floating-point matrix. + +The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point +matrices. It is faster because it is inline, it does fewer checks for array type and array element +type, and it checks for the row and column ranges only in debug mode. +@param mat Input matrix +@param row The zero-based index of row +@param col The zero-based index of column + */ +CV_INLINE double cvmGet( const CvMat* mat, int row, int col ) +{ + int type; + + type = CV_MAT_TYPE(mat->type); + assert( (unsigned)row < (unsigned)mat->rows && + (unsigned)col < (unsigned)mat->cols ); + + if( type == CV_32FC1 ) + return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; + else + { + assert( type == CV_64FC1 ); + return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; + } +} + +/** @brief Sets a specific element of a single-channel floating-point matrix. + +The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point +matrices. It is faster because it is inline, it does fewer checks for array type and array element +type, and it checks for the row and column ranges only in debug mode. +@param mat The matrix +@param row The zero-based index of row +@param col The zero-based index of column +@param value The new value of the matrix element + */ +CV_INLINE void cvmSet( CvMat* mat, int row, int col, double value ) +{ + int type; + type = CV_MAT_TYPE(mat->type); + assert( (unsigned)row < (unsigned)mat->rows && + (unsigned)col < (unsigned)mat->cols ); + + if( type == CV_32FC1 ) + ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value; + else + { + assert( type == CV_64FC1 ); + ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value; + } +} + + +CV_INLINE int cvIplDepth( int type ) +{ + int depth = CV_MAT_DEPTH(type); + return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S || + depth == CV_32S ? IPL_DEPTH_SIGN : 0); +} + + +/****************************************************************************************\ +* Multi-dimensional dense array (CvMatND) * +\****************************************************************************************/ + +#define CV_MATND_MAGIC_VAL 0x42430000 +#define CV_TYPE_NAME_MATND "opencv-nd-matrix" + +#define CV_MAX_DIM 32 +#define CV_MAX_DIM_HEAP 1024 + +/** + @deprecated consider using cv::Mat instead + */ +typedef struct +#ifdef __cplusplus + CV_EXPORTS +#endif +CvMatND +{ + int type; + int dims; + + int* refcount; + int hdr_refcount; + + union + { + uchar* ptr; + float* fl; + double* db; + int* i; + short* s; + } data; + + struct + { + int size; + int step; + } + dim[CV_MAX_DIM]; + +#ifdef __cplusplus + CvMatND() {} + CvMatND(const cv::Mat& m); +#endif +} +CvMatND; + +#define CV_IS_MATND_HDR(mat) \ + ((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL) + +#define CV_IS_MATND(mat) \ + (CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL) + + +/****************************************************************************************\ +* Multi-dimensional sparse array (CvSparseMat) * +\****************************************************************************************/ + +#define CV_SPARSE_MAT_MAGIC_VAL 0x42440000 +#define CV_TYPE_NAME_SPARSE_MAT "opencv-sparse-matrix" + +struct CvSet; + +typedef struct +#ifdef __cplusplus + CV_EXPORTS +#endif +CvSparseMat +{ + int type; + int dims; + int* refcount; + int hdr_refcount; + + struct CvSet* heap; + void** hashtable; + int hashsize; + int valoffset; + int idxoffset; + int size[CV_MAX_DIM]; + +#ifdef __cplusplus + void copyToSparseMat(cv::SparseMat& m) const; +#endif +} +CvSparseMat; + +#ifdef __cplusplus + CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m); +#endif + +#define CV_IS_SPARSE_MAT_HDR(mat) \ + ((mat) != NULL && \ + (((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL) + +#define CV_IS_SPARSE_MAT(mat) \ + CV_IS_SPARSE_MAT_HDR(mat) + +/**************** iteration through a sparse array *****************/ + +typedef struct CvSparseNode +{ + unsigned hashval; + struct CvSparseNode* next; +} +CvSparseNode; + +typedef struct CvSparseMatIterator +{ + CvSparseMat* mat; + CvSparseNode* node; + int curidx; +} +CvSparseMatIterator; + +#define CV_NODE_VAL(mat,node) ((void*)((uchar*)(node) + (mat)->valoffset)) +#define CV_NODE_IDX(mat,node) ((int*)((uchar*)(node) + (mat)->idxoffset)) + +/****************************************************************************************\ +* Histogram * +\****************************************************************************************/ + +typedef int CvHistType; + +#define CV_HIST_MAGIC_VAL 0x42450000 +#define CV_HIST_UNIFORM_FLAG (1 << 10) + +/** indicates whether bin ranges are set already or not */ +#define CV_HIST_RANGES_FLAG (1 << 11) + +#define CV_HIST_ARRAY 0 +#define CV_HIST_SPARSE 1 +#define CV_HIST_TREE CV_HIST_SPARSE + +/** should be used as a parameter only, + it turns to CV_HIST_UNIFORM_FLAG of hist->type */ +#define CV_HIST_UNIFORM 1 + +typedef struct CvHistogram +{ + int type; + CvArr* bins; + float thresh[CV_MAX_DIM][2]; /**< For uniform histograms. */ + float** thresh2; /**< For non-uniform histograms. */ + CvMatND mat; /**< Embedded matrix header for array histograms. */ +} +CvHistogram; + +#define CV_IS_HIST( hist ) \ + ((hist) != NULL && \ + (((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \ + (hist)->bins != NULL) + +#define CV_IS_UNIFORM_HIST( hist ) \ + (((hist)->type & CV_HIST_UNIFORM_FLAG) != 0) + +#define CV_IS_SPARSE_HIST( hist ) \ + CV_IS_SPARSE_MAT((hist)->bins) + +#define CV_HIST_HAS_RANGES( hist ) \ + (((hist)->type & CV_HIST_RANGES_FLAG) != 0) + +/****************************************************************************************\ +* Other supplementary data type definitions * +\****************************************************************************************/ + +/*************************************** CvRect *****************************************/ +/** @sa Rect_ */ +typedef struct CvRect +{ + int x; + int y; + int width; + int height; + +#ifdef __cplusplus + CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {} + template + CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast(r.x)), y(cv::saturate_cast(r.y)), width(cv::saturate_cast(r.width)), height(cv::saturate_cast(r.height)) {} + template + operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); } +#endif +} +CvRect; + +/** constructs CvRect structure. */ +CV_INLINE CvRect cvRect( int x, int y, int width, int height ) +{ + CvRect r; + + r.x = x; + r.y = y; + r.width = width; + r.height = height; + + return r; +} + + +CV_INLINE IplROI cvRectToROI( CvRect rect, int coi ) +{ + IplROI roi; + roi.xOffset = rect.x; + roi.yOffset = rect.y; + roi.width = rect.width; + roi.height = rect.height; + roi.coi = coi; + + return roi; +} + + +CV_INLINE CvRect cvROIToRect( IplROI roi ) +{ + return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height ); +} + +/*********************************** CvTermCriteria *************************************/ + +#define CV_TERMCRIT_ITER 1 +#define CV_TERMCRIT_NUMBER CV_TERMCRIT_ITER +#define CV_TERMCRIT_EPS 2 + +/** @sa TermCriteria + */ +typedef struct CvTermCriteria +{ + int type; /**< may be combination of + CV_TERMCRIT_ITER + CV_TERMCRIT_EPS */ + int max_iter; + double epsilon; + +#ifdef __cplusplus + CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps) {} + CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon) {} + operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); } +#endif + +} +CvTermCriteria; + +CV_INLINE CvTermCriteria cvTermCriteria( int type, int max_iter, double epsilon ) +{ + CvTermCriteria t; + + t.type = type; + t.max_iter = max_iter; + t.epsilon = (float)epsilon; + + return t; +} + + +/******************************* CvPoint and variants ***********************************/ + +typedef struct CvPoint +{ + int x; + int y; + +#ifdef __cplusplus + CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {} + template + CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {} + template + operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } +#endif +} +CvPoint; + +/** constructs CvPoint structure. */ +CV_INLINE CvPoint cvPoint( int x, int y ) +{ + CvPoint p; + + p.x = x; + p.y = y; + + return p; +} + + +typedef struct CvPoint2D32f +{ + float x; + float y; + +#ifdef __cplusplus + CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {} + template + CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {} + template + operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } +#endif +} +CvPoint2D32f; + +/** constructs CvPoint2D32f structure. */ +CV_INLINE CvPoint2D32f cvPoint2D32f( double x, double y ) +{ + CvPoint2D32f p; + + p.x = (float)x; + p.y = (float)y; + + return p; +} + +/** converts CvPoint to CvPoint2D32f. */ +CV_INLINE CvPoint2D32f cvPointTo32f( CvPoint point ) +{ + return cvPoint2D32f( (float)point.x, (float)point.y ); +} + +/** converts CvPoint2D32f to CvPoint. */ +CV_INLINE CvPoint cvPointFrom32f( CvPoint2D32f point ) +{ + CvPoint ipt; + ipt.x = cvRound(point.x); + ipt.y = cvRound(point.y); + + return ipt; +} + + +typedef struct CvPoint3D32f +{ + float x; + float y; + float z; + +#ifdef __cplusplus + CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {} + template + CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {} + template + operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); } +#endif +} +CvPoint3D32f; + +/** constructs CvPoint3D32f structure. */ +CV_INLINE CvPoint3D32f cvPoint3D32f( double x, double y, double z ) +{ + CvPoint3D32f p; + + p.x = (float)x; + p.y = (float)y; + p.z = (float)z; + + return p; +} + + +typedef struct CvPoint2D64f +{ + double x; + double y; +} +CvPoint2D64f; + +/** constructs CvPoint2D64f structure.*/ +CV_INLINE CvPoint2D64f cvPoint2D64f( double x, double y ) +{ + CvPoint2D64f p; + + p.x = x; + p.y = y; + + return p; +} + + +typedef struct CvPoint3D64f +{ + double x; + double y; + double z; +} +CvPoint3D64f; + +/** constructs CvPoint3D64f structure. */ +CV_INLINE CvPoint3D64f cvPoint3D64f( double x, double y, double z ) +{ + CvPoint3D64f p; + + p.x = x; + p.y = y; + p.z = z; + + return p; +} + + +/******************************** CvSize's & CvBox **************************************/ + +typedef struct CvSize +{ + int width; + int height; + +#ifdef __cplusplus + CvSize(int w = 0, int h = 0): width(w), height(h) {} + template + CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} + template + operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } +#endif +} +CvSize; + +/** constructs CvSize structure. */ +CV_INLINE CvSize cvSize( int width, int height ) +{ + CvSize s; + + s.width = width; + s.height = height; + + return s; +} + +typedef struct CvSize2D32f +{ + float width; + float height; + +#ifdef __cplusplus + CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {} + template + CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} + template + operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } +#endif +} +CvSize2D32f; + +/** constructs CvSize2D32f structure. */ +CV_INLINE CvSize2D32f cvSize2D32f( double width, double height ) +{ + CvSize2D32f s; + + s.width = (float)width; + s.height = (float)height; + + return s; +} + +/** @sa RotatedRect + */ +typedef struct CvBox2D +{ + CvPoint2D32f center; /**< Center of the box. */ + CvSize2D32f size; /**< Box width and length. */ + float angle; /**< Angle between the horizontal axis */ + /**< and the first side (i.e. length) in degrees */ + +#ifdef __cplusplus + CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {} + CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {} + operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); } +#endif +} +CvBox2D; + + +/** Line iterator state: */ +typedef struct CvLineIterator +{ + /** Pointer to the current point: */ + uchar* ptr; + + /* Bresenham algorithm state: */ + int err; + int plus_delta; + int minus_delta; + int plus_step; + int minus_step; +} +CvLineIterator; + + + +/************************************* CvSlice ******************************************/ +#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff +#define CV_WHOLE_SEQ cvSlice(0, CV_WHOLE_SEQ_END_INDEX) + +typedef struct CvSlice +{ + int start_index, end_index; + +#if defined(__cplusplus) && !defined(__CUDACC__) + CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {} + CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); } + operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); } +#endif +} +CvSlice; + +CV_INLINE CvSlice cvSlice( int start, int end ) +{ + CvSlice slice; + slice.start_index = start; + slice.end_index = end; + + return slice; +} + + + +/************************************* CvScalar *****************************************/ +/** @sa Scalar_ + */ +typedef struct CvScalar +{ + double val[4]; + +#ifdef __cplusplus + CvScalar() {} + CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; } + template + CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; } + template + operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); } + template + CvScalar(const cv::Vec<_Tp, cn>& v) + { + int i; + for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i]; + for( ; i < 4; i++ ) val[i] = 0; + } +#endif +} +CvScalar; + +CV_INLINE CvScalar cvScalar( double val0, double val1 CV_DEFAULT(0), + double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0)) +{ + CvScalar scalar; + scalar.val[0] = val0; scalar.val[1] = val1; + scalar.val[2] = val2; scalar.val[3] = val3; + return scalar; +} + + +CV_INLINE CvScalar cvRealScalar( double val0 ) +{ + CvScalar scalar; + scalar.val[0] = val0; + scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; + return scalar; +} + +CV_INLINE CvScalar cvScalarAll( double val0123 ) +{ + CvScalar scalar; + scalar.val[0] = val0123; + scalar.val[1] = val0123; + scalar.val[2] = val0123; + scalar.val[3] = val0123; + return scalar; +} + +/****************************************************************************************\ +* Dynamic Data structures * +\****************************************************************************************/ + +/******************************** Memory storage ****************************************/ + +typedef struct CvMemBlock +{ + struct CvMemBlock* prev; + struct CvMemBlock* next; +} +CvMemBlock; + +#define CV_STORAGE_MAGIC_VAL 0x42890000 + +typedef struct CvMemStorage +{ + int signature; + CvMemBlock* bottom; /**< First allocated block. */ + CvMemBlock* top; /**< Current memory block - top of the stack. */ + struct CvMemStorage* parent; /**< We get new blocks from parent as needed. */ + int block_size; /**< Block size. */ + int free_space; /**< Remaining free space in current block. */ +} +CvMemStorage; + +#define CV_IS_STORAGE(storage) \ + ((storage) != NULL && \ + (((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL) + + +typedef struct CvMemStoragePos +{ + CvMemBlock* top; + int free_space; +} +CvMemStoragePos; + + +/*********************************** Sequence *******************************************/ + +typedef struct CvSeqBlock +{ + struct CvSeqBlock* prev; /**< Previous sequence block. */ + struct CvSeqBlock* next; /**< Next sequence block. */ + int start_index; /**< Index of the first element in the block + */ + /**< sequence->first->start_index. */ + int count; /**< Number of elements in the block. */ + schar* data; /**< Pointer to the first element of the block. */ +} +CvSeqBlock; + + +#define CV_TREE_NODE_FIELDS(node_type) \ + int flags; /**< Miscellaneous flags. */ \ + int header_size; /**< Size of sequence header. */ \ + struct node_type* h_prev; /**< Previous sequence. */ \ + struct node_type* h_next; /**< Next sequence. */ \ + struct node_type* v_prev; /**< 2nd previous sequence. */ \ + struct node_type* v_next /**< 2nd next sequence. */ + +/** + Read/Write sequence. + Elements can be dynamically inserted to or deleted from the sequence. +*/ +#define CV_SEQUENCE_FIELDS() \ + CV_TREE_NODE_FIELDS(CvSeq); \ + int total; /**< Total number of elements. */ \ + int elem_size; /**< Size of sequence element in bytes. */ \ + schar* block_max; /**< Maximal bound of the last block. */ \ + schar* ptr; /**< Current write pointer. */ \ + int delta_elems; /**< Grow seq this many at a time. */ \ + CvMemStorage* storage; /**< Where the seq is stored. */ \ + CvSeqBlock* free_blocks; /**< Free blocks list. */ \ + CvSeqBlock* first; /**< Pointer to the first sequence block. */ + +typedef struct CvSeq +{ + CV_SEQUENCE_FIELDS() +} +CvSeq; + +#define CV_TYPE_NAME_SEQ "opencv-sequence" +#define CV_TYPE_NAME_SEQ_TREE "opencv-sequence-tree" + +/*************************************** Set ********************************************/ +/** @brief Set + Order is not preserved. There can be gaps between sequence elements. + After the element has been inserted it stays in the same place all the time. + The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists. +*/ +#define CV_SET_ELEM_FIELDS(elem_type) \ + int flags; \ + struct elem_type* next_free; + +typedef struct CvSetElem +{ + CV_SET_ELEM_FIELDS(CvSetElem) +} +CvSetElem; + +#define CV_SET_FIELDS() \ + CV_SEQUENCE_FIELDS() \ + CvSetElem* free_elems; \ + int active_count; + +typedef struct CvSet +{ + CV_SET_FIELDS() +} +CvSet; + + +#define CV_SET_ELEM_IDX_MASK ((1 << 26) - 1) +#define CV_SET_ELEM_FREE_FLAG (1 << (sizeof(int)*8-1)) + +/** Checks whether the element pointed by ptr belongs to a set or not */ +#define CV_IS_SET_ELEM( ptr ) (((CvSetElem*)(ptr))->flags >= 0) + +/************************************* Graph ********************************************/ + +/** @name Graph + +We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly, +pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in +another set. There is a singly-linked list of incoming/outcoming edges for each vertex. + +Each edge consists of: + +- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively). + + A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not +distinguished during search operations. + +- Two pointers to next edges for the starting and ending vertices, where next[0] points to the +next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1] +adjacency list. + +@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph +@{ +*/ +#define CV_GRAPH_EDGE_FIELDS() \ + int flags; \ + float weight; \ + struct CvGraphEdge* next[2]; \ + struct CvGraphVtx* vtx[2]; + + +#define CV_GRAPH_VERTEX_FIELDS() \ + int flags; \ + struct CvGraphEdge* first; + + +typedef struct CvGraphEdge +{ + CV_GRAPH_EDGE_FIELDS() +} +CvGraphEdge; + +typedef struct CvGraphVtx +{ + CV_GRAPH_VERTEX_FIELDS() +} +CvGraphVtx; + +typedef struct CvGraphVtx2D +{ + CV_GRAPH_VERTEX_FIELDS() + CvPoint2D32f* ptr; +} +CvGraphVtx2D; + +/** + Graph is "derived" from the set (this is set a of vertices) + and includes another set (edges) +*/ +#define CV_GRAPH_FIELDS() \ + CV_SET_FIELDS() \ + CvSet* edges; + +typedef struct CvGraph +{ + CV_GRAPH_FIELDS() +} +CvGraph; + +#define CV_TYPE_NAME_GRAPH "opencv-graph" + +/** @} */ + +/*********************************** Chain/Countour *************************************/ + +typedef struct CvChain +{ + CV_SEQUENCE_FIELDS() + CvPoint origin; +} +CvChain; + +#define CV_CONTOUR_FIELDS() \ + CV_SEQUENCE_FIELDS() \ + CvRect rect; \ + int color; \ + int reserved[3]; + +typedef struct CvContour +{ + CV_CONTOUR_FIELDS() +} +CvContour; + +typedef CvContour CvPoint2DSeq; + +/****************************************************************************************\ +* Sequence types * +\****************************************************************************************/ + +#define CV_SEQ_MAGIC_VAL 0x42990000 + +#define CV_IS_SEQ(seq) \ + ((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL) + +#define CV_SET_MAGIC_VAL 0x42980000 +#define CV_IS_SET(set) \ + ((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL) + +#define CV_SEQ_ELTYPE_BITS 12 +#define CV_SEQ_ELTYPE_MASK ((1 << CV_SEQ_ELTYPE_BITS) - 1) + +#define CV_SEQ_ELTYPE_POINT CV_32SC2 /**< (x,y) */ +#define CV_SEQ_ELTYPE_CODE CV_8UC1 /**< freeman code: 0..7 */ +#define CV_SEQ_ELTYPE_GENERIC 0 +#define CV_SEQ_ELTYPE_PTR CV_USRTYPE1 +#define CV_SEQ_ELTYPE_PPOINT CV_SEQ_ELTYPE_PTR /**< &(x,y) */ +#define CV_SEQ_ELTYPE_INDEX CV_32SC1 /**< #(x,y) */ +#define CV_SEQ_ELTYPE_GRAPH_EDGE 0 /**< &next_o, &next_d, &vtx_o, &vtx_d */ +#define CV_SEQ_ELTYPE_GRAPH_VERTEX 0 /**< first_edge, &(x,y) */ +#define CV_SEQ_ELTYPE_TRIAN_ATR 0 /**< vertex of the binary tree */ +#define CV_SEQ_ELTYPE_CONNECTED_COMP 0 /**< connected component */ +#define CV_SEQ_ELTYPE_POINT3D CV_32FC3 /**< (x,y,z) */ + +#define CV_SEQ_KIND_BITS 2 +#define CV_SEQ_KIND_MASK (((1 << CV_SEQ_KIND_BITS) - 1)<flags & CV_SEQ_ELTYPE_MASK) +#define CV_SEQ_KIND( seq ) ((seq)->flags & CV_SEQ_KIND_MASK ) + +/** flag checking */ +#define CV_IS_SEQ_INDEX( seq ) ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC)) + +#define CV_IS_SEQ_CURVE( seq ) (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE) +#define CV_IS_SEQ_CLOSED( seq ) (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0) +#define CV_IS_SEQ_CONVEX( seq ) 0 +#define CV_IS_SEQ_HOLE( seq ) (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0) +#define CV_IS_SEQ_SIMPLE( seq ) 1 + +/** type checking macros */ +#define CV_IS_SEQ_POINT_SET( seq ) \ + ((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2)) + +#define CV_IS_SEQ_POINT_SUBSET( seq ) \ + (CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT) + +#define CV_IS_SEQ_POLYLINE( seq ) \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq)) + +#define CV_IS_SEQ_POLYGON( seq ) \ + (CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq)) + +#define CV_IS_SEQ_CHAIN( seq ) \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1) + +#define CV_IS_SEQ_CONTOUR( seq ) \ + (CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq))) + +#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \ + (CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq )) + +#define CV_IS_SEQ_POLYGON_TREE( seq ) \ + (CV_SEQ_ELTYPE (seq) == CV_SEQ_ELTYPE_TRIAN_ATR && \ + CV_SEQ_KIND( seq ) == CV_SEQ_KIND_BIN_TREE ) + +#define CV_IS_GRAPH( seq ) \ + (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH) + +#define CV_IS_GRAPH_ORIENTED( seq ) \ + (((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0) + +#define CV_IS_SUBDIV2D( seq ) \ + (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D) + +/****************************************************************************************/ +/* Sequence writer & reader */ +/****************************************************************************************/ + +#define CV_SEQ_WRITER_FIELDS() \ + int header_size; \ + CvSeq* seq; /**< the sequence written */ \ + CvSeqBlock* block; /**< current block */ \ + schar* ptr; /**< pointer to free space */ \ + schar* block_min; /**< pointer to the beginning of block*/\ + schar* block_max; /**< pointer to the end of block */ + +typedef struct CvSeqWriter +{ + CV_SEQ_WRITER_FIELDS() +} +CvSeqWriter; + + +#define CV_SEQ_READER_FIELDS() \ + int header_size; \ + CvSeq* seq; /**< sequence, beign read */ \ + CvSeqBlock* block; /**< current block */ \ + schar* ptr; /**< pointer to element be read next */ \ + schar* block_min; /**< pointer to the beginning of block */\ + schar* block_max; /**< pointer to the end of block */ \ + int delta_index;/**< = seq->first->start_index */ \ + schar* prev_elem; /**< pointer to previous element */ + +typedef struct CvSeqReader +{ + CV_SEQ_READER_FIELDS() +} +CvSeqReader; + +/****************************************************************************************/ +/* Operations on sequences */ +/****************************************************************************************/ + +#define CV_SEQ_ELEM( seq, elem_type, index ) \ +/** assert gives some guarantee that parameter is valid */ \ +( assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) && \ + (seq)->elem_size == sizeof(elem_type)), \ + (elem_type*)((seq)->first && (unsigned)index < \ + (unsigned)((seq)->first->count) ? \ + (seq)->first->data + (index) * sizeof(elem_type) : \ + cvGetSeqElem( (CvSeq*)(seq), (index) ))) +#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) ) + +/** Add element to sequence: */ +#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer ) \ +{ \ + if( (writer).ptr >= (writer).block_max ) \ + { \ + cvCreateSeqBlock( &writer); \ + } \ + memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\ + (writer).ptr += (writer).seq->elem_size; \ +} + +#define CV_WRITE_SEQ_ELEM( elem, writer ) \ +{ \ + assert( (writer).seq->elem_size == sizeof(elem)); \ + if( (writer).ptr >= (writer).block_max ) \ + { \ + cvCreateSeqBlock( &writer); \ + } \ + assert( (writer).ptr <= (writer).block_max - sizeof(elem));\ + memcpy((writer).ptr, &(elem), sizeof(elem)); \ + (writer).ptr += sizeof(elem); \ +} + + +/** Move reader position forward: */ +#define CV_NEXT_SEQ_ELEM( elem_size, reader ) \ +{ \ + if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \ + { \ + cvChangeSeqBlock( &(reader), 1 ); \ + } \ +} + + +/** Move reader position backward: */ +#define CV_PREV_SEQ_ELEM( elem_size, reader ) \ +{ \ + if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \ + { \ + cvChangeSeqBlock( &(reader), -1 ); \ + } \ +} + +/** Read element and move read position forward: */ +#define CV_READ_SEQ_ELEM( elem, reader ) \ +{ \ + assert( (reader).seq->elem_size == sizeof(elem)); \ + memcpy( &(elem), (reader).ptr, sizeof((elem))); \ + CV_NEXT_SEQ_ELEM( sizeof(elem), reader ) \ +} + +/** Read element and move read position backward: */ +#define CV_REV_READ_SEQ_ELEM( elem, reader ) \ +{ \ + assert( (reader).seq->elem_size == sizeof(elem)); \ + memcpy(&(elem), (reader).ptr, sizeof((elem))); \ + CV_PREV_SEQ_ELEM( sizeof(elem), reader ) \ +} + + +#define CV_READ_CHAIN_POINT( _pt, reader ) \ +{ \ + (_pt) = (reader).pt; \ + if( (reader).ptr ) \ + { \ + CV_READ_SEQ_ELEM( (reader).code, (reader)); \ + assert( ((reader).code & ~7) == 0 ); \ + (reader).pt.x += (reader).deltas[(int)(reader).code][0]; \ + (reader).pt.y += (reader).deltas[(int)(reader).code][1]; \ + } \ +} + +#define CV_CURRENT_POINT( reader ) (*((CvPoint*)((reader).ptr))) +#define CV_PREV_POINT( reader ) (*((CvPoint*)((reader).prev_elem))) + +#define CV_READ_EDGE( pt1, pt2, reader ) \ +{ \ + assert( sizeof(pt1) == sizeof(CvPoint) && \ + sizeof(pt2) == sizeof(CvPoint) && \ + reader.seq->elem_size == sizeof(CvPoint)); \ + (pt1) = CV_PREV_POINT( reader ); \ + (pt2) = CV_CURRENT_POINT( reader ); \ + (reader).prev_elem = (reader).ptr; \ + CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader)); \ +} + +/************ Graph macros ************/ + +/** Return next graph edge for given vertex: */ +#define CV_NEXT_GRAPH_EDGE( edge, vertex ) \ + (assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)), \ + (edge)->next[(edge)->vtx[1] == (vertex)]) + + + +/****************************************************************************************\ +* Data structures for persistence (a.k.a serialization) functionality * +\****************************************************************************************/ + +/** "black box" file storage */ +typedef struct CvFileStorage CvFileStorage; + +/** Storage flags: */ +#define CV_STORAGE_READ 0 +#define CV_STORAGE_WRITE 1 +#define CV_STORAGE_WRITE_TEXT CV_STORAGE_WRITE +#define CV_STORAGE_WRITE_BINARY CV_STORAGE_WRITE +#define CV_STORAGE_APPEND 2 +#define CV_STORAGE_MEMORY 4 +#define CV_STORAGE_FORMAT_MASK (7<<3) +#define CV_STORAGE_FORMAT_AUTO 0 +#define CV_STORAGE_FORMAT_XML 8 +#define CV_STORAGE_FORMAT_YAML 16 +#define CV_STORAGE_FORMAT_JSON 24 +#define CV_STORAGE_BASE64 64 +#define CV_STORAGE_WRITE_BASE64 (CV_STORAGE_BASE64 | CV_STORAGE_WRITE) + +/** @brief List of attributes. : + +In the current implementation, attributes are used to pass extra parameters when writing user +objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type +specification (type_id attribute). +@see cvAttrList, cvAttrValue + */ +typedef struct CvAttrList +{ + const char** attr; /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */ + struct CvAttrList* next; /**< Pointer to next chunk of the attributes list. */ +} +CvAttrList; + +/** initializes CvAttrList structure */ +CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL), + CvAttrList* next CV_DEFAULT(NULL) ) +{ + CvAttrList l; + l.attr = attr; + l.next = next; + + return l; +} + +struct CvTypeInfo; + +#define CV_NODE_NONE 0 +#define CV_NODE_INT 1 +#define CV_NODE_INTEGER CV_NODE_INT +#define CV_NODE_REAL 2 +#define CV_NODE_FLOAT CV_NODE_REAL +#define CV_NODE_STR 3 +#define CV_NODE_STRING CV_NODE_STR +#define CV_NODE_REF 4 /**< not used */ +#define CV_NODE_SEQ 5 +#define CV_NODE_MAP 6 +#define CV_NODE_TYPE_MASK 7 + +#define CV_NODE_TYPE(flags) ((flags) & CV_NODE_TYPE_MASK) + +/** file node flags */ +#define CV_NODE_FLOW 8 /**= CV_NODE_SEQ) +#define CV_NODE_IS_FLOW(flags) (((flags) & CV_NODE_FLOW) != 0) +#define CV_NODE_IS_EMPTY(flags) (((flags) & CV_NODE_EMPTY) != 0) +#define CV_NODE_IS_USER(flags) (((flags) & CV_NODE_USER) != 0) +#define CV_NODE_HAS_NAME(flags) (((flags) & CV_NODE_NAMED) != 0) + +#define CV_NODE_SEQ_SIMPLE 256 +#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0) + +typedef struct CvString +{ + int len; + char* ptr; +} +CvString; + +/** All the keys (names) of elements in the readed file storage + are stored in the hash to speed up the lookup operations: */ +typedef struct CvStringHashNode +{ + unsigned hashval; + CvString str; + struct CvStringHashNode* next; +} +CvStringHashNode; + +typedef struct CvGenericHash CvFileNodeHash; + +/** Basic element of the file storage - scalar or collection: */ +typedef struct CvFileNode +{ + int tag; + struct CvTypeInfo* info; /**< type information + (only for user-defined object, for others it is 0) */ + union + { + double f; /**< scalar floating-point number */ + int i; /**< scalar integer number */ + CvString str; /**< text string */ + CvSeq* seq; /**< sequence (ordered collection of file nodes) */ + CvFileNodeHash* map; /**< map (collection of named file nodes) */ + } data; +} +CvFileNode; + +#ifdef __cplusplus +extern "C" { +#endif +typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr ); +typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr ); +typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node ); +typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name, + const void* struct_ptr, CvAttrList attributes ); +typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr ); +#ifdef __cplusplus +} +#endif + +/** @brief Type information + +The structure contains information about one of the standard or user-defined types. Instances of the +type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there +is a way to find the type info structure for a given object using the cvTypeOf function. +Alternatively, type info can be found by type name using cvFindType, which is used when an object +is read from file storage. The user can register a new type with cvRegisterType that adds the type +information structure into the beginning of the type list. Thus, it is possible to create +specialized types from generic standard types and override the basic methods. + */ +typedef struct CvTypeInfo +{ + int flags; /**< not used */ + int header_size; /**< sizeof(CvTypeInfo) */ + struct CvTypeInfo* prev; /**< previous registered type in the list */ + struct CvTypeInfo* next; /**< next registered type in the list */ + const char* type_name; /**< type name, written to file storage */ + CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */ + CvReleaseFunc release; /**< releases object (memory etc.) */ + CvReadFunc read; /**< reads object from file storage */ + CvWriteFunc write; /**< writes object to file storage */ + CvCloneFunc clone; /**< creates a copy of the object */ +} +CvTypeInfo; + + +/**** System data types ******/ + +typedef struct CvPluginFuncInfo +{ + void** func_addr; + void* default_func_addr; + const char* func_names; + int search_modules; + int loaded_from; +} +CvPluginFuncInfo; + +typedef struct CvModuleInfo +{ + struct CvModuleInfo* next; + const char* name; + const char* version; + CvPluginFuncInfo* func_tab; +} +CvModuleInfo; + +/** @} */ + +#endif /*OPENCV_CORE_TYPES_H*/ + +/* End of file. */ diff --git a/thirdparty/linux/include/opencv2/core/utility.hpp b/thirdparty/linux/include/opencv2/core/utility.hpp new file mode 100644 index 0000000..e7a7c2d --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/utility.hpp @@ -0,0 +1,1171 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_UTILITY_H +#define OPENCV_CORE_UTILITY_H + +#ifndef __cplusplus +# error utility.hpp header must be compiled as C++ +#endif + +#if defined(check) +# warning Detected Apple 'check' macro definition, it can cause build conflicts. Please, include this header before any Apple headers. +#endif + +#include "opencv2/core.hpp" + +namespace cv +{ + +#ifdef CV_COLLECT_IMPL_DATA +CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays +CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays +// Get stored implementation flags and fucntions names arrays +// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which fucntion +CV_EXPORTS int getImpl(std::vector &impl, std::vector &funName); + +CV_EXPORTS bool useCollection(); // return implementation collection state +CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state + +#define CV_IMPL_PLAIN 0x01 // native CPU OpenCV implementation +#define CV_IMPL_OCL 0x02 // OpenCL implementation +#define CV_IMPL_IPP 0x04 // IPP implementation +#define CV_IMPL_MT 0x10 // multithreaded implementation + +#define CV_IMPL_ADD(impl) \ + if(cv::useCollection()) \ + { \ + cv::addImpl(impl, CV_Func); \ + } +#else +#define CV_IMPL_ADD(impl) +#endif + +//! @addtogroup core_utils +//! @{ + +/** @brief Automatically Allocated Buffer Class + + The class is used for temporary buffers in functions and methods. + If a temporary buffer is usually small (a few K's of memory), + but its size depends on the parameters, it makes sense to create a small + fixed-size array on stack and use it if it's large enough. If the required buffer size + is larger than the fixed size, another buffer of sufficient size is allocated dynamically + and released after the processing. Therefore, in typical cases, when the buffer size is small, + there is no overhead associated with malloc()/free(). + At the same time, there is no limit on the size of processed data. + + This is what AutoBuffer does. The template takes 2 parameters - type of the buffer elements and + the number of stack-allocated elements. Here is how the class is used: + + \code + void my_func(const cv::Mat& m) + { + cv::AutoBuffer buf; // create automatic buffer containing 1000 floats + + buf.allocate(m.rows); // if m.rows <= 1000, the pre-allocated buffer is used, + // otherwise the buffer of "m.rows" floats will be allocated + // dynamically and deallocated in cv::AutoBuffer destructor + ... + } + \endcode +*/ +template class AutoBuffer +{ +public: + typedef _Tp value_type; + + //! the default constructor + AutoBuffer(); + //! constructor taking the real buffer size + AutoBuffer(size_t _size); + + //! the copy constructor + AutoBuffer(const AutoBuffer<_Tp, fixed_size>& buf); + //! the assignment operator + AutoBuffer<_Tp, fixed_size>& operator = (const AutoBuffer<_Tp, fixed_size>& buf); + + //! destructor. calls deallocate() + ~AutoBuffer(); + + //! allocates the new buffer of size _size. if the _size is small enough, stack-allocated buffer is used + void allocate(size_t _size); + //! deallocates the buffer if it was dynamically allocated + void deallocate(); + //! resizes the buffer and preserves the content + void resize(size_t _size); + //! returns the current buffer size + size_t size() const; + //! returns pointer to the real buffer, stack-allocated or head-allocated + operator _Tp* (); + //! returns read-only pointer to the real buffer, stack-allocated or head-allocated + operator const _Tp* () const; + +protected: + //! pointer to the real buffer, can point to buf if the buffer is small enough + _Tp* ptr; + //! size of the real buffer + size_t sz; + //! pre-allocated buffer. At least 1 element to confirm C++ standard reqirements + _Tp buf[(fixed_size > 0) ? fixed_size : 1]; +}; + +/** @brief Sets/resets the break-on-error mode. + +When the break-on-error mode is set, the default error handler issues a hardware exception, which +can make debugging more convenient. + +\return the previous state + */ +CV_EXPORTS bool setBreakOnError(bool flag); + +extern "C" typedef int (*ErrorCallback)( int status, const char* func_name, + const char* err_msg, const char* file_name, + int line, void* userdata ); + + +/** @brief Sets the new error handler and the optional user data. + + The function sets the new error handler, called from cv::error(). + + \param errCallback the new error handler. If NULL, the default error handler is used. + \param userdata the optional user data pointer, passed to the callback. + \param prevUserdata the optional output parameter where the previous user data pointer is stored + + \return the previous error handler +*/ +CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback, void* userdata=0, void** prevUserdata=0); + +/** @brief Returns a text string formatted using the printf-like expression. + +The function acts like sprintf but forms and returns an STL string. It can be used to form an error +message in the Exception constructor. +@param fmt printf-compatible formatting specifiers. + */ +CV_EXPORTS String format( const char* fmt, ... ); +CV_EXPORTS String tempfile( const char* suffix = 0); +CV_EXPORTS void glob(String pattern, std::vector& result, bool recursive = false); + +/** @brief OpenCV will try to set the number of threads for the next parallel region. + +If threads == 0, OpenCV will disable threading optimizations and run all it's functions +sequentially. Passing threads \< 0 will reset threads number to system default. This function must +be called outside of parallel region. + +OpenCV will try to run it's functions with specified threads number, but some behaviour differs from +framework: +- `TBB` – User-defined parallel constructions will run with the same threads number, if + another does not specified. If late on user creates own scheduler, OpenCV will be use it. +- `OpenMP` – No special defined behaviour. +- `Concurrency` – If threads == 1, OpenCV will disable threading optimizations and run it's + functions sequentially. +- `GCD` – Supports only values \<= 0. +- `C=` – No special defined behaviour. +@param nthreads Number of threads used by OpenCV. +@sa getNumThreads, getThreadNum + */ +CV_EXPORTS_W void setNumThreads(int nthreads); + +/** @brief Returns the number of threads used by OpenCV for parallel regions. + +Always returns 1 if OpenCV is built without threading support. + +The exact meaning of return value depends on the threading framework used by OpenCV library: +- `TBB` – The number of threads, that OpenCV will try to use for parallel regions. If there is + any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns + default number of threads used by TBB library. +- `OpenMP` – An upper bound on the number of threads that could be used to form a new team. +- `Concurrency` – The number of threads, that OpenCV will try to use for parallel regions. +- `GCD` – Unsupported; returns the GCD thread pool limit (512) for compatibility. +- `C=` – The number of threads, that OpenCV will try to use for parallel regions, if before + called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs, + available for the process. +@sa setNumThreads, getThreadNum + */ +CV_EXPORTS_W int getNumThreads(); + +/** @brief Returns the index of the currently executed thread within the current parallel region. Always +returns 0 if called outside of parallel region. + +The exact meaning of return value depends on the threading framework used by OpenCV library: +- `TBB` – Unsupported with current 4.1 TBB release. May be will be supported in future. +- `OpenMP` – The thread number, within the current team, of the calling thread. +- `Concurrency` – An ID for the virtual processor that the current context is executing on (0 + for master thread and unique number for others, but not necessary 1,2,3,...). +- `GCD` – System calling thread's ID. Never returns 0 inside parallel region. +- `C=` – The index of the current parallel task. +@sa setNumThreads, getNumThreads + */ +CV_EXPORTS_W int getThreadNum(); + +/** @brief Returns full configuration time cmake output. + +Returned value is raw cmake output including version control system revision, compiler version, +compiler flags, enabled modules and third party libraries, etc. Output format depends on target +architecture. + */ +CV_EXPORTS_W const String& getBuildInformation(); + +/** @brief Returns the number of ticks. + +The function returns the number of ticks after the certain event (for example, when the machine was +turned on). It can be used to initialize RNG or to measure a function execution time by reading the +tick count before and after the function call. +@sa getTickFrequency, TickMeter + */ +CV_EXPORTS_W int64 getTickCount(); + +/** @brief Returns the number of ticks per second. + +The function returns the number of ticks per second. That is, the following code computes the +execution time in seconds: +@code + double t = (double)getTickCount(); + // do something ... + t = ((double)getTickCount() - t)/getTickFrequency(); +@endcode +@sa getTickCount, TickMeter + */ +CV_EXPORTS_W double getTickFrequency(); + +/** @brief a Class to measure passing time. + +The class computes passing time by counting the number of ticks per second. That is, the following code computes the +execution time in seconds: +@code +TickMeter tm; +tm.start(); +// do something ... +tm.stop(); +std::cout << tm.getTimeSec(); +@endcode +@sa getTickCount, getTickFrequency +*/ + +class CV_EXPORTS_W TickMeter +{ +public: + //! the default constructor + CV_WRAP TickMeter() + { + reset(); + } + + /** + starts counting ticks. + */ + CV_WRAP void start() + { + startTime = cv::getTickCount(); + } + + /** + stops counting ticks. + */ + CV_WRAP void stop() + { + int64 time = cv::getTickCount(); + if (startTime == 0) + return; + ++counter; + sumTime += (time - startTime); + startTime = 0; + } + + /** + returns counted ticks. + */ + CV_WRAP int64 getTimeTicks() const + { + return sumTime; + } + + /** + returns passed time in microseconds. + */ + CV_WRAP double getTimeMicro() const + { + return getTimeMilli()*1e3; + } + + /** + returns passed time in milliseconds. + */ + CV_WRAP double getTimeMilli() const + { + return getTimeSec()*1e3; + } + + /** + returns passed time in seconds. + */ + CV_WRAP double getTimeSec() const + { + return (double)getTimeTicks() / getTickFrequency(); + } + + /** + returns internal counter value. + */ + CV_WRAP int64 getCounter() const + { + return counter; + } + + /** + resets internal values. + */ + CV_WRAP void reset() + { + startTime = 0; + sumTime = 0; + counter = 0; + } + +private: + int64 counter; + int64 sumTime; + int64 startTime; +}; + +/** @brief output operator +@code +TickMeter tm; +tm.start(); +// do something ... +tm.stop(); +std::cout << tm; +@endcode +*/ + +static inline +std::ostream& operator << (std::ostream& out, const TickMeter& tm) +{ + return out << tm.getTimeSec() << "sec"; +} + +/** @brief Returns the number of CPU ticks. + +The function returns the current number of CPU ticks on some architectures (such as x86, x64, +PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for +very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU +systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU +with its own counter. So, theoretically (and practically) the subsequent calls to the function do +not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU +frequency depending on the load, the number of CPU clocks spent in some code cannot be directly +converted to time units. Therefore, getTickCount is generally a preferable solution for measuring +execution time. + */ +CV_EXPORTS_W int64 getCPUTickCount(); + +/** @brief Returns true if the specified feature is supported by the host hardware. + +The function returns true if the host hardware supports the specified feature. When user calls +setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until +setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code +in OpenCV. +@param feature The feature of interest, one of cv::CpuFeatures + */ +CV_EXPORTS_W bool checkHardwareSupport(int feature); + +/** @brief Returns the number of logical CPUs available for the process. + */ +CV_EXPORTS_W int getNumberOfCPUs(); + + +/** @brief Aligns a pointer to the specified number of bytes. + +The function returns the aligned pointer of the same type as the input pointer: +\f[\texttt{(_Tp*)(((size_t)ptr + n-1) & -n)}\f] +@param ptr Aligned pointer. +@param n Alignment size that must be a power of two. + */ +template static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp)) +{ + return (_Tp*)(((size_t)ptr + n-1) & -n); +} + +/** @brief Aligns a buffer size to the specified number of bytes. + +The function returns the minimum number that is greater or equal to sz and is divisible by n : +\f[\texttt{(sz + n-1) & -n}\f] +@param sz Buffer size to align. +@param n Alignment size that must be a power of two. + */ +static inline size_t alignSize(size_t sz, int n) +{ + CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 + return (sz + n-1) & -n; +} + +/** @brief Enables or disables the optimized code. + +The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX, +and other instructions on the platforms that support it). It sets a global flag that is further +checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only +safe to call the function on the very top level in your application where you can be sure that no +other OpenCV function is currently executed. + +By default, the optimized code is enabled unless you disable it in CMake. The current status can be +retrieved using useOptimized. +@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true) +or not (onoff=false). + */ +CV_EXPORTS_W void setUseOptimized(bool onoff); + +/** @brief Returns the status of optimized code usage. + +The function returns true if the optimized code is enabled. Otherwise, it returns false. + */ +CV_EXPORTS_W bool useOptimized(); + +static inline size_t getElemSize(int type) { return CV_ELEM_SIZE(type); } + +/////////////////////////////// Parallel Primitives ////////////////////////////////// + +/** @brief Base class for parallel data processors +*/ +class CV_EXPORTS ParallelLoopBody +{ +public: + virtual ~ParallelLoopBody(); + virtual void operator() (const Range& range) const = 0; +}; + +/** @brief Parallel data processor +*/ +CV_EXPORTS void parallel_for_(const Range& range, const ParallelLoopBody& body, double nstripes=-1.); + +/////////////////////////////// forEach method of cv::Mat //////////////////////////// +template inline +void Mat::forEach_impl(const Functor& operation) { + if (false) { + operation(*reinterpret_cast<_Tp*>(0), reinterpret_cast(0)); + // If your compiler fail in this line. + // Please check that your functor signature is + // (_Tp&, const int*) <- multidimential + // or (_Tp&, void*) <- in case of you don't need current idx. + } + + CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX); + const int LINES = static_cast(this->total() / this->size[this->dims - 1]); + + class PixelOperationWrapper :public ParallelLoopBody + { + public: + PixelOperationWrapper(Mat_<_Tp>* const frame, const Functor& _operation) + : mat(frame), op(_operation) {} + virtual ~PixelOperationWrapper(){} + // ! Overloaded virtual operator + // convert range call to row call. + virtual void operator()(const Range &range) const { + const int DIMS = mat->dims; + const int COLS = mat->size[DIMS - 1]; + if (DIMS <= 2) { + for (int row = range.start; row < range.end; ++row) { + this->rowCall2(row, COLS); + } + } else { + std::vector idx(COLS); /// idx is modified in this->rowCall + idx[DIMS - 2] = range.start - 1; + + for (int line_num = range.start; line_num < range.end; ++line_num) { + idx[DIMS - 2]++; + for (int i = DIMS - 2; i >= 0; --i) { + if (idx[i] >= mat->size[i]) { + idx[i - 1] += idx[i] / mat->size[i]; + idx[i] %= mat->size[i]; + continue; // carry-over; + } + else { + break; + } + } + this->rowCall(&idx[0], COLS, DIMS); + } + } + } + private: + Mat_<_Tp>* const mat; + const Functor op; + // ! Call operator for each elements in this row. + inline void rowCall(int* const idx, const int COLS, const int DIMS) const { + int &col = idx[DIMS - 1]; + col = 0; + _Tp* pixel = &(mat->template at<_Tp>(idx)); + + while (col < COLS) { + op(*pixel, const_cast(idx)); + pixel++; col++; + } + col = 0; + } + // ! Call operator for each elements in this row. 2d mat special version. + inline void rowCall2(const int row, const int COLS) const { + union Index{ + int body[2]; + operator const int*() const { + return reinterpret_cast(this); + } + int& operator[](const int i) { + return body[i]; + } + } idx = {{row, 0}}; + // Special union is needed to avoid + // "error: array subscript is above array bounds [-Werror=array-bounds]" + // when call the functor `op` such that access idx[3]. + + _Tp* pixel = &(mat->template at<_Tp>(idx)); + const _Tp* const pixel_end = pixel + COLS; + while(pixel < pixel_end) { + op(*pixel++, static_cast(idx)); + idx[1]++; + } + } + PixelOperationWrapper& operator=(const PixelOperationWrapper &) { + CV_Assert(false); + // We can not remove this implementation because Visual Studio warning C4822. + return *this; + } + }; + + parallel_for_(cv::Range(0, LINES), PixelOperationWrapper(reinterpret_cast*>(this), operation)); +} + +/////////////////////////// Synchronization Primitives /////////////////////////////// + +class CV_EXPORTS Mutex +{ +public: + Mutex(); + ~Mutex(); + Mutex(const Mutex& m); + Mutex& operator = (const Mutex& m); + + void lock(); + bool trylock(); + void unlock(); + + struct Impl; +protected: + Impl* impl; +}; + +class CV_EXPORTS AutoLock +{ +public: + AutoLock(Mutex& m) : mutex(&m) { mutex->lock(); } + ~AutoLock() { mutex->unlock(); } +protected: + Mutex* mutex; +private: + AutoLock(const AutoLock&); + AutoLock& operator = (const AutoLock&); +}; + +// TLS interface +class CV_EXPORTS TLSDataContainer +{ +protected: + TLSDataContainer(); + virtual ~TLSDataContainer(); + + void gatherData(std::vector &data) const; +#if OPENCV_ABI_COMPATIBILITY > 300 + void* getData() const; + void release(); + +private: +#else + void release(); + +public: + void* getData() const; +#endif + virtual void* createDataInstance() const = 0; + virtual void deleteDataInstance(void* pData) const = 0; + + int key_; +}; + +// Main TLS data class +template +class TLSData : protected TLSDataContainer +{ +public: + inline TLSData() {} + inline ~TLSData() { release(); } // Release key and delete associated data + inline T* get() const { return (T*)getData(); } // Get data assosiated with key + + // Get data from all threads + inline void gather(std::vector &data) const + { + std::vector &dataVoid = reinterpret_cast&>(data); + gatherData(dataVoid); + } + +private: + virtual void* createDataInstance() const {return new T;} // Wrapper to allocate data by template + virtual void deleteDataInstance(void* pData) const {delete (T*)pData;} // Wrapper to release data by template + + // Disable TLS copy operations + TLSData(TLSData &) {} + TLSData& operator =(const TLSData &) {return *this;} +}; + +/** @brief Designed for command line parsing + +The sample below demonstrates how to use CommandLineParser: +@code + CommandLineParser parser(argc, argv, keys); + parser.about("Application name v1.0.0"); + + if (parser.has("help")) + { + parser.printMessage(); + return 0; + } + + int N = parser.get("N"); + double fps = parser.get("fps"); + String path = parser.get("path"); + + use_time_stamp = parser.has("timestamp"); + + String img1 = parser.get(0); + String img2 = parser.get(1); + + int repeat = parser.get(2); + + if (!parser.check()) + { + parser.printErrors(); + return 0; + } +@endcode + +### Keys syntax + +The keys parameter is a string containing several blocks, each one is enclosed in curley braces and +describes one argument. Each argument contains three parts separated by the `|` symbol: + +-# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol) +-# default value will be used if the argument was not provided (can be empty) +-# help message (can be empty) + +For example: + +@code{.cpp} + const String keys = + "{help h usage ? | | print this message }" + "{@image1 | | image1 for compare }" + "{@image2 || image2 for compare }" + "{@repeat |1 | number }" + "{path |. | path to file }" + "{fps | -1.0 | fps for output video }" + "{N count |100 | count of objects }" + "{ts timestamp | | use time stamp }" + ; +} +@endcode + +Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method. +Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their +actual value instead. + +String keys like `get("@image1")` return the empty string `""` by default - even with an empty default value. +Use the special `` default value to enforce that the returned string must not be empty. (like in `get("@image2")`) + +### Usage + +For the described keys: + +@code{.sh} + # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true) + $ ./app -N=200 1.png 2.jpg 19 -ts + + # Bad call + $ ./app -fps=aaa + ERRORS: + Parameter 'fps': can not convert: [aaa] to [double] +@endcode + */ +class CV_EXPORTS CommandLineParser +{ +public: + + /** @brief Constructor + + Initializes command line parser object + + @param argc number of command line arguments (from main()) + @param argv array of command line arguments (from main()) + @param keys string describing acceptable command line parameters (see class description for syntax) + */ + CommandLineParser(int argc, const char* const argv[], const String& keys); + + /** @brief Copy constructor */ + CommandLineParser(const CommandLineParser& parser); + + /** @brief Assignment operator */ + CommandLineParser& operator = (const CommandLineParser& parser); + + /** @brief Destructor */ + ~CommandLineParser(); + + /** @brief Returns application path + + This method returns the path to the executable from the command line (`argv[0]`). + + For example, if the application has been started with such command: + @code{.sh} + $ ./bin/my-executable + @endcode + this method will return `./bin`. + */ + String getPathToApplication() const; + + /** @brief Access arguments by name + + Returns argument converted to selected type. If the argument is not known or can not be + converted to selected type, the error flag is set (can be checked with @ref check). + + For example, define: + @code{.cpp} + String keys = "{N count||}"; + @endcode + + Call: + @code{.sh} + $ ./my-app -N=20 + # or + $ ./my-app --count=20 + @endcode + + Access: + @code{.cpp} + int N = parser.get("N"); + @endcode + + @param name name of the argument + @param space_delete remove spaces from the left and right of the string + @tparam T the argument will be converted to this type if possible + + @note You can access positional arguments by their `@`-prefixed name: + @code{.cpp} + parser.get("@image"); + @endcode + */ + template + T get(const String& name, bool space_delete = true) const + { + T val = T(); + getByName(name, space_delete, ParamType::type, (void*)&val); + return val; + } + + /** @brief Access positional arguments by index + + Returns argument converted to selected type. Indexes are counted from zero. + + For example, define: + @code{.cpp} + String keys = "{@arg1||}{@arg2||}" + @endcode + + Call: + @code{.sh} + ./my-app abc qwe + @endcode + + Access arguments: + @code{.cpp} + String val_1 = parser.get(0); // returns "abc", arg1 + String val_2 = parser.get(1); // returns "qwe", arg2 + @endcode + + @param index index of the argument + @param space_delete remove spaces from the left and right of the string + @tparam T the argument will be converted to this type if possible + */ + template + T get(int index, bool space_delete = true) const + { + T val = T(); + getByIndex(index, space_delete, ParamType::type, (void*)&val); + return val; + } + + /** @brief Check if field was provided in the command line + + @param name argument name to check + */ + bool has(const String& name) const; + + /** @brief Check for parsing errors + + Returns true if error occured while accessing the parameters (bad conversion, missing arguments, + etc.). Call @ref printErrors to print error messages list. + */ + bool check() const; + + /** @brief Set the about message + + The about message will be shown when @ref printMessage is called, right before arguments table. + */ + void about(const String& message); + + /** @brief Print help message + + This method will print standard help message containing the about message and arguments description. + + @sa about + */ + void printMessage() const; + + /** @brief Print list of errors occured + + @sa check + */ + void printErrors() const; + +protected: + void getByName(const String& name, bool space_delete, int type, void* dst) const; + void getByIndex(int index, bool space_delete, int type, void* dst) const; + + struct Impl; + Impl* impl; +}; + +//! @} core_utils + +//! @cond IGNORED + +/////////////////////////////// AutoBuffer implementation //////////////////////////////////////// + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer() +{ + ptr = buf; + sz = fixed_size; +} + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer(size_t _size) +{ + ptr = buf; + sz = fixed_size; + allocate(_size); +} + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer(const AutoBuffer<_Tp, fixed_size>& abuf ) +{ + ptr = buf; + sz = fixed_size; + allocate(abuf.size()); + for( size_t i = 0; i < sz; i++ ) + ptr[i] = abuf.ptr[i]; +} + +template inline AutoBuffer<_Tp, fixed_size>& +AutoBuffer<_Tp, fixed_size>::operator = (const AutoBuffer<_Tp, fixed_size>& abuf) +{ + if( this != &abuf ) + { + deallocate(); + allocate(abuf.size()); + for( size_t i = 0; i < sz; i++ ) + ptr[i] = abuf.ptr[i]; + } + return *this; +} + +template inline +AutoBuffer<_Tp, fixed_size>::~AutoBuffer() +{ deallocate(); } + +template inline void +AutoBuffer<_Tp, fixed_size>::allocate(size_t _size) +{ + if(_size <= sz) + { + sz = _size; + return; + } + deallocate(); + sz = _size; + if(_size > fixed_size) + { + ptr = new _Tp[_size]; + } +} + +template inline void +AutoBuffer<_Tp, fixed_size>::deallocate() +{ + if( ptr != buf ) + { + delete[] ptr; + ptr = buf; + sz = fixed_size; + } +} + +template inline void +AutoBuffer<_Tp, fixed_size>::resize(size_t _size) +{ + if(_size <= sz) + { + sz = _size; + return; + } + size_t i, prevsize = sz, minsize = MIN(prevsize, _size); + _Tp* prevptr = ptr; + + ptr = _size > fixed_size ? new _Tp[_size] : buf; + sz = _size; + + if( ptr != prevptr ) + for( i = 0; i < minsize; i++ ) + ptr[i] = prevptr[i]; + for( i = prevsize; i < _size; i++ ) + ptr[i] = _Tp(); + + if( prevptr != buf ) + delete[] prevptr; +} + +template inline size_t +AutoBuffer<_Tp, fixed_size>::size() const +{ return sz; } + +template inline +AutoBuffer<_Tp, fixed_size>::operator _Tp* () +{ return ptr; } + +template inline +AutoBuffer<_Tp, fixed_size>::operator const _Tp* () const +{ return ptr; } + +#ifndef OPENCV_NOSTL +template<> inline std::string CommandLineParser::get(int index, bool space_delete) const +{ + return get(index, space_delete); +} +template<> inline std::string CommandLineParser::get(const String& name, bool space_delete) const +{ + return get(name, space_delete); +} +#endif // OPENCV_NOSTL + +//! @endcond + + +// Basic Node class for tree building +template +class CV_EXPORTS Node +{ +public: + Node() + { + m_pParent = 0; + } + Node(OBJECT& payload) : m_payload(payload) + { + m_pParent = 0; + } + ~Node() + { + removeChilds(); + if (m_pParent) + { + int idx = m_pParent->findChild(this); + if (idx >= 0) + m_pParent->m_childs.erase(m_pParent->m_childs.begin() + idx); + } + } + + Node* findChild(OBJECT& payload) const + { + for(size_t i = 0; i < this->m_childs.size(); i++) + { + if(this->m_childs[i]->m_payload == payload) + return this->m_childs[i]; + } + return NULL; + } + + int findChild(Node *pNode) const + { + for (size_t i = 0; i < this->m_childs.size(); i++) + { + if(this->m_childs[i] == pNode) + return (int)i; + } + return -1; + } + + void addChild(Node *pNode) + { + if(!pNode) + return; + + CV_Assert(pNode->m_pParent == 0); + pNode->m_pParent = this; + this->m_childs.push_back(pNode); + } + + void removeChilds() + { + for(size_t i = 0; i < m_childs.size(); i++) + { + m_childs[i]->m_pParent = 0; // avoid excessive parent vector trimming + delete m_childs[i]; + } + m_childs.clear(); + } + + int getDepth() + { + int count = 0; + Node *pParent = m_pParent; + while(pParent) count++, pParent = pParent->m_pParent; + return count; + } + +public: + OBJECT m_payload; + Node* m_pParent; + std::vector*> m_childs; +}; + +// Instrumentation external interface +namespace instr +{ + +#if !defined OPENCV_ABI_CHECK + +enum TYPE +{ + TYPE_GENERAL = 0, // OpenCV API function, e.g. exported function + TYPE_MARKER, // Information marker + TYPE_WRAPPER, // Wrapper function for implementation + TYPE_FUN, // Simple function call +}; + +enum IMPL +{ + IMPL_PLAIN = 0, + IMPL_IPP, + IMPL_OPENCL, +}; + +struct NodeDataTls +{ + NodeDataTls() + { + m_ticksTotal = 0; + } + uint64 m_ticksTotal; +}; + +class CV_EXPORTS NodeData +{ +public: + NodeData(const char* funName = 0, const char* fileName = NULL, int lineNum = 0, void* retAddress = NULL, bool alwaysExpand = false, cv::instr::TYPE instrType = TYPE_GENERAL, cv::instr::IMPL implType = IMPL_PLAIN); + NodeData(NodeData &ref); + ~NodeData(); + NodeData& operator=(const NodeData&); + + cv::String m_funName; + cv::instr::TYPE m_instrType; + cv::instr::IMPL m_implType; + const char* m_fileName; + int m_lineNum; + void* m_retAddress; + bool m_alwaysExpand; + bool m_funError; + + volatile int m_counter; + volatile uint64 m_ticksTotal; + TLSData m_tls; + int m_threads; + + // No synchronization + double getTotalMs() const { return ((double)m_ticksTotal / cv::getTickFrequency()) * 1000; } + double getMeanMs() const { return (((double)m_ticksTotal/m_counter) / cv::getTickFrequency()) * 1000; } +}; +bool operator==(const NodeData& lhs, const NodeData& rhs); + +typedef Node InstrNode; + +CV_EXPORTS InstrNode* getTrace(); + +#endif // !defined OPENCV_ABI_CHECK + + +CV_EXPORTS bool useInstrumentation(); +CV_EXPORTS void setUseInstrumentation(bool flag); +CV_EXPORTS void resetTrace(); + +enum FLAGS +{ + FLAGS_NONE = 0, + FLAGS_MAPPING = 0x01, + FLAGS_EXPAND_SAME_NAMES = 0x02, +}; + +CV_EXPORTS void setFlags(FLAGS modeFlags); +static inline void setFlags(int modeFlags) { setFlags((FLAGS)modeFlags); } +CV_EXPORTS FLAGS getFlags(); +} + +} //namespace cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/core/core_c.h" +#endif + +#endif //OPENCV_CORE_UTILITY_H diff --git a/thirdparty/linux/include/opencv2/core/va_intel.hpp b/thirdparty/linux/include/opencv2/core/va_intel.hpp new file mode 100644 index 0000000..3325848 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/va_intel.hpp @@ -0,0 +1,77 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2015, Itseez, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#ifndef OPENCV_CORE_VA_INTEL_HPP +#define OPENCV_CORE_VA_INTEL_HPP + +#ifndef __cplusplus +# error va_intel.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "ocl.hpp" + +#if defined(HAVE_VA) +# include "va/va.h" +#else // HAVE_VA +# if !defined(_VA_H_) + typedef void* VADisplay; + typedef unsigned int VASurfaceID; +# endif // !_VA_H_ +#endif // HAVE_VA + +namespace cv { namespace va_intel { + +/** @addtogroup core_va_intel +This section describes Intel VA-API/OpenCL (CL-VA) interoperability. + +To enable CL-VA interoperability support, configure OpenCV using CMake with WITH_VA_INTEL=ON . Currently VA-API is +supported on Linux only. You should also install Intel Media Server Studio (MSS) to use this feature. You may +have to specify the path(s) to MSS components for cmake in environment variables: VA_INTEL_MSDK_ROOT for Media SDK +(default is "/opt/intel/mediasdk"), and VA_INTEL_IOCL_ROOT for Intel OpenCL (default is "/opt/intel/opencl"). + +To use CL-VA interoperability you should first create VADisplay (libva), and then call initializeContextFromVA() +function to create OpenCL context and set up interoperability. +*/ +//! @{ + +/////////////////// CL-VA Interoperability Functions /////////////////// + +namespace ocl { +using namespace cv::ocl; + +// TODO static functions in the Context class +/** @brief Creates OpenCL context from VA. +@param display - VADisplay for which CL interop should be established. +@param tryInterop - try to set up for interoperability, if true; set up for use slow copy if false. +@return Returns reference to OpenCL Context + */ +CV_EXPORTS Context& initializeContextFromVA(VADisplay display, bool tryInterop = true); + +} // namespace cv::va_intel::ocl + +/** @brief Converts InputArray to VASurfaceID object. +@param display - VADisplay object. +@param src - source InputArray. +@param surface - destination VASurfaceID object. +@param size - size of image represented by VASurfaceID object. + */ +CV_EXPORTS void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size); + +/** @brief Converts VASurfaceID object to OutputArray. +@param display - VADisplay object. +@param surface - source VASurfaceID object. +@param size - size of image represented by VASurfaceID object. +@param dst - destination OutputArray. + */ +CV_EXPORTS void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, OutputArray dst); + +//! @} + +}} // namespace cv::va_intel + +#endif /* OPENCV_CORE_VA_INTEL_HPP */ diff --git a/thirdparty/linux/include/opencv2/core/version.hpp b/thirdparty/linux/include/opencv2/core/version.hpp new file mode 100644 index 0000000..85c12d8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/version.hpp @@ -0,0 +1,71 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright( C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2011-2013, NVIDIA Corporation, all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +//(including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort(including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +/* + definition of the current version of OpenCV + Usefull to test in user programs +*/ + +#ifndef OPENCV_VERSION_HPP +#define OPENCV_VERSION_HPP + +#define CV_VERSION_MAJOR 3 +#define CV_VERSION_MINOR 2 +#define CV_VERSION_REVISION 0 +#define CV_VERSION_STATUS "-dev" + +#define CVAUX_STR_EXP(__A) #__A +#define CVAUX_STR(__A) CVAUX_STR_EXP(__A) + +#define CVAUX_STRW_EXP(__A) L ## #__A +#define CVAUX_STRW(__A) CVAUX_STRW_EXP(__A) + +#define CV_VERSION CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION) CV_VERSION_STATUS + +/* old style version constants*/ +#define CV_MAJOR_VERSION CV_VERSION_MAJOR +#define CV_MINOR_VERSION CV_VERSION_MINOR +#define CV_SUBMINOR_VERSION CV_VERSION_REVISION + +#endif diff --git a/thirdparty/linux/include/opencv2/core/wimage.hpp b/thirdparty/linux/include/opencv2/core/wimage.hpp new file mode 100644 index 0000000..b246c89 --- /dev/null +++ b/thirdparty/linux/include/opencv2/core/wimage.hpp @@ -0,0 +1,603 @@ +/*M////////////////////////////////////////////////////////////////////////////// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to +// this license. If you do not agree to this license, do not download, +// install, copy or use the software. +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2008, Google, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation or contributors may not be used to endorse +// or promote products derived from this software without specific +// prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" +// and any express or implied warranties, including, but not limited to, the +// implied warranties of merchantability and fitness for a particular purpose +// are disclaimed. In no event shall the Intel Corporation or contributors be +// liable for any direct, indirect, incidental, special, exemplary, or +// consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +///////////////////////////////////////////////////////////////////////////////// +//M*/ + +#ifndef OPENCV_CORE_WIMAGE_HPP +#define OPENCV_CORE_WIMAGE_HPP + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus + +namespace cv { + +//! @addtogroup core +//! @{ + +template class WImage; +template class WImageBuffer; +template class WImageView; + +template class WImageC; +template class WImageBufferC; +template class WImageViewC; + +// Commonly used typedefs. +typedef WImage WImage_b; +typedef WImageView WImageView_b; +typedef WImageBuffer WImageBuffer_b; + +typedef WImageC WImage1_b; +typedef WImageViewC WImageView1_b; +typedef WImageBufferC WImageBuffer1_b; + +typedef WImageC WImage3_b; +typedef WImageViewC WImageView3_b; +typedef WImageBufferC WImageBuffer3_b; + +typedef WImage WImage_f; +typedef WImageView WImageView_f; +typedef WImageBuffer WImageBuffer_f; + +typedef WImageC WImage1_f; +typedef WImageViewC WImageView1_f; +typedef WImageBufferC WImageBuffer1_f; + +typedef WImageC WImage3_f; +typedef WImageViewC WImageView3_f; +typedef WImageBufferC WImageBuffer3_f; + +// There isn't a standard for signed and unsigned short so be more +// explicit in the typename for these cases. +typedef WImage WImage_16s; +typedef WImageView WImageView_16s; +typedef WImageBuffer WImageBuffer_16s; + +typedef WImageC WImage1_16s; +typedef WImageViewC WImageView1_16s; +typedef WImageBufferC WImageBuffer1_16s; + +typedef WImageC WImage3_16s; +typedef WImageViewC WImageView3_16s; +typedef WImageBufferC WImageBuffer3_16s; + +typedef WImage WImage_16u; +typedef WImageView WImageView_16u; +typedef WImageBuffer WImageBuffer_16u; + +typedef WImageC WImage1_16u; +typedef WImageViewC WImageView1_16u; +typedef WImageBufferC WImageBuffer1_16u; + +typedef WImageC WImage3_16u; +typedef WImageViewC WImageView3_16u; +typedef WImageBufferC WImageBuffer3_16u; + +/** @brief Image class which provides a thin layer around an IplImage. + +The goals of the class design are: + + -# All the data has explicit ownership to avoid memory leaks + -# No hidden allocations or copies for performance. + -# Easy access to OpenCV methods (which will access IPP if available) + -# Can easily treat external data as an image + -# Easy to create images which are subsets of other images + -# Fast pixel access which can take advantage of number of channels if known at compile time. + +The WImage class is the image class which provides the data accessors. The 'W' comes from the fact +that it is also a wrapper around the popular but inconvenient IplImage class. A WImage can be +constructed either using a WImageBuffer class which allocates and frees the data, or using a +WImageView class which constructs a subimage or a view into external data. The view class does no +memory management. Each class actually has two versions, one when the number of channels is known +at compile time and one when it isn't. Using the one with the number of channels specified can +provide some compile time optimizations by using the fact that the number of channels is a +constant. + +We use the convention (c,r) to refer to column c and row r with (0,0) being the upper left corner. +This is similar to standard Euclidean coordinates with the first coordinate varying in the +horizontal direction and the second coordinate varying in the vertical direction. Thus (c,r) is +usually in the domain [0, width) X [0, height) + +Example usage: +@code +WImageBuffer3_b im(5,7); // Make a 5X7 3 channel image of type uchar +WImageView3_b sub_im(im, 2,2, 3,3); // 3X3 submatrix +vector vec(10, 3.0f); +WImageView1_f user_im(&vec[0], 2, 5); // 2X5 image w/ supplied data + +im.SetZero(); // same as cvSetZero(im.Ipl()) +*im(2, 3) = 15; // Modify the element at column 2, row 3 +MySetRand(&sub_im); + +// Copy the second row into the first. This can be done with no memory +// allocation and will use SSE if IPP is available. +int w = im.Width(); +im.View(0,0, w,1).CopyFrom(im.View(0,1, w,1)); + +// Doesn't care about source of data since using WImage +void MySetRand(WImage_b* im) { // Works with any number of channels +for (int r = 0; r < im->Height(); ++r) { + float* row = im->Row(r); + for (int c = 0; c < im->Width(); ++c) { + for (int ch = 0; ch < im->Channels(); ++ch, ++row) { + *row = uchar(rand() & 255); + } + } +} +} +@endcode + +Functions that are not part of the basic image allocation, viewing, and access should come from +OpenCV, except some useful functions that are not part of OpenCV can be found in wimage_util.h +*/ +template +class WImage +{ +public: + typedef T BaseType; + + // WImage is an abstract class with no other virtual methods so make the + // destructor virtual. + virtual ~WImage() = 0; + + // Accessors + IplImage* Ipl() {return image_; } + const IplImage* Ipl() const {return image_; } + T* ImageData() { return reinterpret_cast(image_->imageData); } + const T* ImageData() const { + return reinterpret_cast(image_->imageData); + } + + int Width() const {return image_->width; } + int Height() const {return image_->height; } + + // WidthStep is the number of bytes to go to the pixel with the next y coord + int WidthStep() const {return image_->widthStep; } + + int Channels() const {return image_->nChannels; } + int ChannelSize() const {return sizeof(T); } // number of bytes per channel + + // Number of bytes per pixel + int PixelSize() const {return Channels() * ChannelSize(); } + + // Return depth type (e.g. IPL_DEPTH_8U, IPL_DEPTH_32F) which is the number + // of bits per channel and with the signed bit set. + // This is known at compile time using specializations. + int Depth() const; + + inline const T* Row(int r) const { + return reinterpret_cast(image_->imageData + r*image_->widthStep); + } + + inline T* Row(int r) { + return reinterpret_cast(image_->imageData + r*image_->widthStep); + } + + // Pixel accessors which returns a pointer to the start of the channel + inline T* operator() (int c, int r) { + return reinterpret_cast(image_->imageData + r*image_->widthStep) + + c*Channels(); + } + + inline const T* operator() (int c, int r) const { + return reinterpret_cast(image_->imageData + r*image_->widthStep) + + c*Channels(); + } + + // Copy the contents from another image which is just a convenience to cvCopy + void CopyFrom(const WImage& src) { cvCopy(src.Ipl(), image_); } + + // Set contents to zero which is just a convenient to cvSetZero + void SetZero() { cvSetZero(image_); } + + // Construct a view into a region of this image + WImageView View(int c, int r, int width, int height); + +protected: + // Disallow copy and assignment + WImage(const WImage&); + void operator=(const WImage&); + + explicit WImage(IplImage* img) : image_(img) { + assert(!img || img->depth == Depth()); + } + + void SetIpl(IplImage* image) { + assert(!image || image->depth == Depth()); + image_ = image; + } + + IplImage* image_; +}; + + +/** Image class when both the pixel type and number of channels +are known at compile time. This wrapper will speed up some of the operations +like accessing individual pixels using the () operator. +*/ +template +class WImageC : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + explicit WImageC(IplImage* img) : WImage(img) { + assert(!img || img->nChannels == Channels()); + } + + // Construct a view into a region of this image + WImageViewC View(int c, int r, int width, int height); + + // Copy the contents from another image which is just a convenience to cvCopy + void CopyFrom(const WImageC& src) { + cvCopy(src.Ipl(), WImage::image_); + } + + // WImageC is an abstract class with no other virtual methods so make the + // destructor virtual. + virtual ~WImageC() = 0; + + int Channels() const {return C; } + +protected: + // Disallow copy and assignment + WImageC(const WImageC&); + void operator=(const WImageC&); + + void SetIpl(IplImage* image) { + assert(!image || image->depth == WImage::Depth()); + WImage::SetIpl(image); + } +}; + +/** Image class which owns the data, so it can be allocated and is always +freed. It cannot be copied but can be explicity cloned. +*/ +template +class WImageBuffer : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + + // Default constructor which creates an object that can be + WImageBuffer() : WImage(0) {} + + WImageBuffer(int width, int height, int nchannels) : WImage(0) { + Allocate(width, height, nchannels); + } + + // Constructor which takes ownership of a given IplImage so releases + // the image on destruction. + explicit WImageBuffer(IplImage* img) : WImage(img) {} + + // Allocate an image. Does nothing if current size is the same as + // the new size. + void Allocate(int width, int height, int nchannels); + + // Set the data to point to an image, releasing the old data + void SetIpl(IplImage* img) { + ReleaseImage(); + WImage::SetIpl(img); + } + + // Clone an image which reallocates the image if of a different dimension. + void CloneFrom(const WImage& src) { + Allocate(src.Width(), src.Height(), src.Channels()); + CopyFrom(src); + } + + ~WImageBuffer() { + ReleaseImage(); + } + + // Release the image if it isn't null. + void ReleaseImage() { + if (WImage::image_) { + IplImage* image = WImage::image_; + cvReleaseImage(&image); + WImage::SetIpl(0); + } + } + + bool IsNull() const {return WImage::image_ == NULL; } + +private: + // Disallow copy and assignment + WImageBuffer(const WImageBuffer&); + void operator=(const WImageBuffer&); +}; + +/** Like a WImageBuffer class but when the number of channels is known at compile time. +*/ +template +class WImageBufferC : public WImageC +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + // Default constructor which creates an object that can be + WImageBufferC() : WImageC(0) {} + + WImageBufferC(int width, int height) : WImageC(0) { + Allocate(width, height); + } + + // Constructor which takes ownership of a given IplImage so releases + // the image on destruction. + explicit WImageBufferC(IplImage* img) : WImageC(img) {} + + // Allocate an image. Does nothing if current size is the same as + // the new size. + void Allocate(int width, int height); + + // Set the data to point to an image, releasing the old data + void SetIpl(IplImage* img) { + ReleaseImage(); + WImageC::SetIpl(img); + } + + // Clone an image which reallocates the image if of a different dimension. + void CloneFrom(const WImageC& src) { + Allocate(src.Width(), src.Height()); + CopyFrom(src); + } + + ~WImageBufferC() { + ReleaseImage(); + } + + // Release the image if it isn't null. + void ReleaseImage() { + if (WImage::image_) { + IplImage* image = WImage::image_; + cvReleaseImage(&image); + WImageC::SetIpl(0); + } + } + + bool IsNull() const {return WImage::image_ == NULL; } + +private: + // Disallow copy and assignment + WImageBufferC(const WImageBufferC&); + void operator=(const WImageBufferC&); +}; + +/** View into an image class which allows treating a subimage as an image or treating external data +as an image +*/ +template class WImageView : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + + // Construct a subimage. No checks are done that the subimage lies + // completely inside the original image. + WImageView(WImage* img, int c, int r, int width, int height); + + // Refer to external data. + // If not given width_step assumed to be same as width. + WImageView(T* data, int width, int height, int channels, int width_step = -1); + + // Refer to external data. This does NOT take ownership + // of the supplied IplImage. + WImageView(IplImage* img) : WImage(img) {} + + // Copy constructor + WImageView(const WImage& img) : WImage(0) { + header_ = *(img.Ipl()); + WImage::SetIpl(&header_); + } + + WImageView& operator=(const WImage& img) { + header_ = *(img.Ipl()); + WImage::SetIpl(&header_); + return *this; + } + +protected: + IplImage header_; +}; + + +template +class WImageViewC : public WImageC +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + // Default constructor needed for vectors of views. + WImageViewC(); + + virtual ~WImageViewC() {} + + // Construct a subimage. No checks are done that the subimage lies + // completely inside the original image. + WImageViewC(WImageC* img, + int c, int r, int width, int height); + + // Refer to external data + WImageViewC(T* data, int width, int height, int width_step = -1); + + // Refer to external data. This does NOT take ownership + // of the supplied IplImage. + WImageViewC(IplImage* img) : WImageC(img) {} + + // Copy constructor which does a shallow copy to allow multiple views + // of same data. gcc-4.1.1 gets confused if both versions of + // the constructor and assignment operator are not provided. + WImageViewC(const WImageC& img) : WImageC(0) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + } + WImageViewC(const WImageViewC& img) : WImageC(0) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + } + + WImageViewC& operator=(const WImageC& img) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + return *this; + } + WImageViewC& operator=(const WImageViewC& img) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + return *this; + } + +protected: + IplImage header_; +}; + + +// Specializations for depth +template<> +inline int WImage::Depth() const {return IPL_DEPTH_8U; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_8S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_16S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_16U; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_32S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_32F; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_64F; } + +template inline WImage::~WImage() {} +template inline WImageC::~WImageC() {} + +template +inline void WImageBuffer::Allocate(int width, int height, int nchannels) +{ + if (IsNull() || WImage::Width() != width || + WImage::Height() != height || WImage::Channels() != nchannels) { + ReleaseImage(); + WImage::image_ = cvCreateImage(cvSize(width, height), + WImage::Depth(), nchannels); + } +} + +template +inline void WImageBufferC::Allocate(int width, int height) +{ + if (IsNull() || WImage::Width() != width || WImage::Height() != height) { + ReleaseImage(); + WImageC::SetIpl(cvCreateImage(cvSize(width, height),WImage::Depth(), C)); + } +} + +template +WImageView::WImageView(WImage* img, int c, int r, int width, int height) + : WImage(0) +{ + header_ = *(img->Ipl()); + header_.imageData = reinterpret_cast((*img)(c, r)); + header_.width = width; + header_.height = height; + WImage::SetIpl(&header_); +} + +template +WImageView::WImageView(T* data, int width, int height, int nchannels, int width_step) + : WImage(0) +{ + cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), nchannels); + header_.imageData = reinterpret_cast(data); + if (width_step > 0) { + header_.widthStep = width_step; + } + WImage::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC(WImageC* img, int c, int r, int width, int height) + : WImageC(0) +{ + header_ = *(img->Ipl()); + header_.imageData = reinterpret_cast((*img)(c, r)); + header_.width = width; + header_.height = height; + WImageC::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC() : WImageC(0) { + cvInitImageHeader(&header_, cvSize(0, 0), WImage::Depth(), C); + header_.imageData = reinterpret_cast(0); + WImageC::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC(T* data, int width, int height, int width_step) + : WImageC(0) +{ + cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), C); + header_.imageData = reinterpret_cast(data); + if (width_step > 0) { + header_.widthStep = width_step; + } + WImageC::SetIpl(&header_); +} + +// Construct a view into a region of an image +template +WImageView WImage::View(int c, int r, int width, int height) { + return WImageView(this, c, r, width, height); +} + +template +WImageViewC WImageC::View(int c, int r, int width, int height) { + return WImageViewC(this, c, r, width, height); +} + +//! @} core + +} // end of namespace + +#endif // __cplusplus + +#endif diff --git a/thirdparty/linux/include/opencv2/cvconfig.h b/thirdparty/linux/include/opencv2/cvconfig.h new file mode 100644 index 0000000..1b55f2a --- /dev/null +++ b/thirdparty/linux/include/opencv2/cvconfig.h @@ -0,0 +1,208 @@ +/* OpenCV compiled as static or dynamic libs */ +#define BUILD_SHARED_LIBS + +/* Compile for 'real' NVIDIA GPU architectures */ +#define CUDA_ARCH_BIN "" + +/* Create PTX or BIN for 1.0 compute capability */ +/* #undef CUDA_ARCH_BIN_OR_PTX_10 */ + +/* NVIDIA GPU features are used */ +#define CUDA_ARCH_FEATURES "" + +/* Compile for 'virtual' NVIDIA PTX architectures */ +#define CUDA_ARCH_PTX "" + +/* AVFoundation video libraries */ +/* #undef HAVE_AVFOUNDATION */ + +/* V4L capturing support */ +/* #undef HAVE_CAMV4L */ + +/* V4L2 capturing support */ +#define HAVE_CAMV4L2 + +/* Carbon windowing environment */ +/* #undef HAVE_CARBON */ + +/* AMD's Basic Linear Algebra Subprograms Library*/ +/* #undef HAVE_CLAMDBLAS */ + +/* AMD's OpenCL Fast Fourier Transform Library*/ +/* #undef HAVE_CLAMDFFT */ + +/* Clp support */ +/* #undef HAVE_CLP */ + +/* Cocoa API */ +/* #undef HAVE_COCOA */ + +/* C= */ +/* #undef HAVE_CSTRIPES */ + +/* NVidia Cuda Basic Linear Algebra Subprograms (BLAS) API*/ +/* #undef HAVE_CUBLAS */ + +/* NVidia Cuda Runtime API*/ +/* #undef HAVE_CUDA */ + +/* NVidia Cuda Fast Fourier Transform (FFT) API*/ +/* #undef HAVE_CUFFT */ + +/* IEEE1394 capturing support */ +/* #undef HAVE_DC1394 */ + +/* IEEE1394 capturing support - libdc1394 v2.x */ +/* #undef HAVE_DC1394_2 */ + +/* DirectX */ +/* #undef HAVE_DIRECTX */ +/* #undef HAVE_DIRECTX_NV12 */ +/* #undef HAVE_D3D11 */ +/* #undef HAVE_D3D10 */ +/* #undef HAVE_D3D9 */ + +/* DirectShow Video Capture library */ +/* #undef HAVE_DSHOW */ + +/* Eigen Matrix & Linear Algebra Library */ +/* #undef HAVE_EIGEN */ + +/* FFMpeg video library */ +#define HAVE_FFMPEG + +/* Geospatial Data Abstraction Library */ +/* #undef HAVE_GDAL */ + +/* GStreamer multimedia framework */ +#define HAVE_GSTREAMER + +/* GTK+ 2.0 Thread support */ +/* #undef HAVE_GTHREAD */ + +/* GTK+ 2.x toolkit */ +/* #undef HAVE_GTK */ + +/* Define to 1 if you have the header file. */ +/* #undef HAVE_INTTYPES_H */ + +/* Intel Perceptual Computing SDK library */ +/* #undef HAVE_INTELPERC */ + +/* Intel Integrated Performance Primitives */ +/* #undef HAVE_IPP */ +/* #undef HAVE_IPP_ICV_ONLY */ + +/* Intel IPP Async */ +/* #undef HAVE_IPP_A */ + +/* JPEG-2000 codec */ +#define HAVE_JASPER + +/* IJG JPEG codec */ +#define HAVE_JPEG + +/* libpng/png.h needs to be included */ +#define HAVE_LIBPNG_PNG_H + +/* GDCM DICOM codec */ +/* #undef HAVE_GDCM */ + +/* V4L/V4L2 capturing support via libv4l */ +/* #undef HAVE_LIBV4L */ + +/* Microsoft Media Foundation Capture library */ +/* #undef HAVE_MSMF */ + +/* NVidia Video Decoding API*/ +/* #undef HAVE_NVCUVID */ + +/* NVidia Video Encoding API*/ +/* #undef HAVE_NVCUVENC */ + +/* OpenCL Support */ +#define HAVE_OPENCL +/* #undef HAVE_OPENCL_STATIC */ +/* #undef HAVE_OPENCL_SVM */ + +/* OpenEXR codec */ +#define HAVE_OPENEXR + +/* OpenGL support*/ +/* #undef HAVE_OPENGL */ + +/* OpenNI library */ +/* #undef HAVE_OPENNI */ + +/* OpenNI library */ +/* #undef HAVE_OPENNI2 */ + +/* PNG codec */ +#define HAVE_PNG + +/* Posix threads (pthreads) */ +#define HAVE_PTHREADS + +/* parallel_for with pthreads */ +#define HAVE_PTHREADS_PF + +/* Qt support */ +/* #undef HAVE_QT */ + +/* Qt OpenGL support */ +/* #undef HAVE_QT_OPENGL */ + +/* QuickTime video libraries */ +/* #undef HAVE_QUICKTIME */ + +/* QTKit video libraries */ +/* #undef HAVE_QTKIT */ + +/* Intel Threading Building Blocks */ +/* #undef HAVE_TBB */ + +/* TIFF codec */ +#define HAVE_TIFF + +/* Unicap video capture library */ +/* #undef HAVE_UNICAP */ + +/* Video for Windows support */ +/* #undef HAVE_VFW */ + +/* V4L2 capturing support in videoio.h */ +/* #undef HAVE_VIDEOIO */ + +/* Win32 UI */ +/* #undef HAVE_WIN32UI */ + +/* XIMEA camera support */ +/* #undef HAVE_XIMEA */ + +/* Xine video library */ +/* #undef HAVE_XINE */ + +/* Define if your processor stores words with the most significant byte + first (like Motorola and SPARC, unlike Intel and VAX). */ +/* #undef WORDS_BIGENDIAN */ + +/* gPhoto2 library */ +/* #undef HAVE_GPHOTO2 */ + +/* VA library (libva) */ +/* #undef HAVE_VA */ + +/* Intel VA-API/OpenCL */ +/* #undef HAVE_VA_INTEL */ + +/* Lapack */ +/* #undef HAVE_LAPACK */ + +/* FP16 */ +/* #undef HAVE_FP16 */ + +/* Library was compiled with functions instrumentation */ +/* #undef ENABLE_INSTRUMENTATION */ + +/* OpenVX */ +/* #undef HAVE_OPENVX */ diff --git a/thirdparty/linux/include/opencv2/datasets/ar_hmdb.hpp b/thirdparty/linux/include/opencv2/datasets/ar_hmdb.hpp new file mode 100644 index 0000000..8941583 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/ar_hmdb.hpp @@ -0,0 +1,80 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_AR_HMDB_HPP +#define OPENCV_DATASETS_AR_HMDB_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_ar +//! @{ + +struct AR_hmdbObj : public Object +{ + int id; + std::string name; + std::string videoName; +}; + +class CV_EXPORTS AR_hmdb : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/ar_sports.hpp b/thirdparty/linux/include/opencv2/datasets/ar_sports.hpp new file mode 100644 index 0000000..7f51405 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/ar_sports.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_AR_SPORTS_HPP +#define OPENCV_DATASETS_AR_SPORTS_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_ar +//! @{ + +struct AR_sportsObj : public Object +{ + std::string videoUrl; + std::vector labels; +}; + +class CV_EXPORTS AR_sports : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/dataset.hpp b/thirdparty/linux/include/opencv2/datasets/dataset.hpp new file mode 100644 index 0000000..ccf2b66 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/dataset.hpp @@ -0,0 +1,545 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_DATASET_HPP +#define OPENCV_DATASETS_DATASET_HPP + +#include +#include + +#include + +/** @defgroup datasets Framework for working with different datasets + +The datasets module includes classes for working with different datasets: load data, evaluate +different algorithms on them, contains benchmarks, etc. + +It is planned to have: + +- basic: loading code for all datasets to help start work with them. +- next stage: quick benchmarks for all datasets to show how to solve them using OpenCV and +implement evaluation code. +- finally: implement on OpenCV state-of-the-art algorithms, which solve these tasks. + +@{ +@defgroup datasets_ar Action Recognition + +### HMDB: A Large Human Motion Database + +Implements loading dataset: + +"HMDB: A Large Human Motion Database": + +Usage: +-# From link above download dataset files: `hmdb51_org.rar` & `test_train_splits.rar`. +-# Unpack them. Unpack all archives from directory: `hmdb51_org/` and remove them. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_ar_hmdb -p=/home/user/path_to_unpacked_folders/ +~~~ + +#### Benchmark + +For this dataset was implemented benchmark with accuracy: 0.107407 (using precomputed HOG/HOF +"STIP" features from site, averaging for 3 splits) + +To run this benchmark execute: +~~~ +./opencv/build/bin/example_datasets_ar_hmdb_benchmark -p=/home/user/path_to_unpacked_folders/ +~~~ + +@note +Precomputed features should be unpacked in the same folder: `/home/user/path_to_unpacked_folders/hmdb51_org_stips/`. +Also unpack all archives from directory: `hmdb51_org_stips/` and remove them. + +### Sports-1M %Dataset + +Implements loading dataset: + +"Sports-1M Dataset": + +Usage: +-# From link above download dataset files (`git clone https://code.google.com/p/sports-1m-dataset/`). +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_ar_sports -p=/home/user/path_to_downloaded_folders/ +~~~ + +@defgroup datasets_fr Face Recognition + +### Adience + +Implements loading dataset: + +"Adience": + +Usage: +-# From link above download any dataset file: `faces.tar.gz\aligned.tar.gz` and files with splits: +`fold_0_data.txt-fold_4_data.txt`, `fold_frontal_0_data.txt-fold_frontal_4_data.txt`. (For +face recognition task another splits should be created) +-# Unpack dataset file to some folder and place split files into the same folder. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_fr_adience -p=/home/user/path_to_created_folder/ +~~~ + +### Labeled Faces in the Wild + +Implements loading dataset: + +"Labeled Faces in the Wild": + +Usage: +-# From link above download any dataset file: +`lfw.tgz\lfwa.tar.gz\lfw-deepfunneled.tgz\lfw-funneled.tgz` and files with pairs: 10 test +splits: `pairs.txt` and developer train split: `pairsDevTrain.txt`. +-# Unpack dataset file and place `pairs.txt` and `pairsDevTrain.txt` in created folder. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_fr_lfw -p=/home/user/path_to_unpacked_folder/lfw2/ +~~~ + +#### Benchmark + +For this dataset was implemented benchmark with accuracy: 0.623833 +- 0.005223 (train split: +`pairsDevTrain.txt`, dataset: lfwa) + +To run this benchmark execute: +~~~ +./opencv/build/bin/example_datasets_fr_lfw_benchmark -p=/home/user/path_to_unpacked_folder/lfw2/ +~~~ + +@defgroup datasets_gr Gesture Recognition + +### ChaLearn Looking at People + +Implements loading dataset: + +"ChaLearn Looking at People": + +Usage +-# Follow instruction from site above, download files for dataset "Track 3: Gesture Recognition": +`Train1.zip`-`Train5.zip`, `Validation1.zip`-`Validation3.zip` (Register on site: www.codalab.org and +accept the terms and conditions of competition: + There are three mirrors for +downloading dataset files. When I downloaded data only mirror: "Universitat Oberta de Catalunya" +works). +-# Unpack train archives `Train1.zip`-`Train5.zip` to folder `Train/`, validation archives +`Validation1.zip`-`Validation3.zip` to folder `Validation/` +-# Unpack all archives in `Train/` & `Validation/` in the folders with the same names, for example: +`Sample0001.zip` to `Sample0001/` +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_gr_chalearn -p=/home/user/path_to_unpacked_folders/ +~~~ + +### Sheffield Kinect Gesture Dataset + +Implements loading dataset: + +"Sheffield Kinect Gesture Dataset": + +Usage: +-# From link above download dataset files: `subject1_dep.7z`-`subject6_dep.7z`, `subject1_rgb.7z`-`subject6_rgb.7z`. +-# Unpack them. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_gr_skig -p=/home/user/path_to_unpacked_folders/ +~~~ + +@defgroup datasets_hpe Human Pose Estimation + +### HumanEva Dataset + +Implements loading dataset: + +"HumanEva Dataset": + +Usage: +-# From link above download dataset files for `HumanEva-I` (tar) & `HumanEva-II`. +-# Unpack them to `HumanEva_1` & `HumanEva_2` accordingly. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_hpe_humaneva -p=/home/user/path_to_unpacked_folders/ +~~~ + +### PARSE Dataset + +Implements loading dataset: + +"PARSE Dataset": + +Usage: +-# From link above download dataset file: `people.zip`. +-# Unpack it. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_hpe_parse -p=/home/user/path_to_unpacked_folder/people_all/ +~~~ + +@defgroup datasets_ir Image Registration + +### Affine Covariant Regions Datasets + +Implements loading dataset: + +"Affine Covariant Regions Datasets": + +Usage: +-# From link above download dataset files: +`bark\bikes\boat\graf\leuven\trees\ubc\wall.tar.gz`. +-# Unpack them. +-# To load data, for example, for "bark", run: +``` +./opencv/build/bin/example_datasets_ir_affine -p=/home/user/path_to_unpacked_folder/bark/ +``` + +### Robot Data Set + +Implements loading dataset: + +"Robot Data Set, Point Feature Data Set – 2010": + +Usage: +-# From link above download dataset files: `SET001_6.tar.gz`-`SET055_60.tar.gz` +-# Unpack them to one folder. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_ir_robot -p=/home/user/path_to_unpacked_folder/ +~~~ + +@defgroup datasets_is Image Segmentation + +### The Berkeley Segmentation Dataset and Benchmark + +Implements loading dataset: + +"The Berkeley Segmentation Dataset and Benchmark": + +Usage: +-# From link above download dataset files: `BSDS300-human.tgz` & `BSDS300-images.tgz`. +-# Unpack them. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_is_bsds -p=/home/user/path_to_unpacked_folder/BSDS300/ +~~~ + +### Weizmann Segmentation Evaluation Database + +Implements loading dataset: + +"Weizmann Segmentation Evaluation Database": + +Usage: +-# From link above download dataset files: `Weizmann_Seg_DB_1obj.ZIP` & `Weizmann_Seg_DB_2obj.ZIP`. +-# Unpack them. +-# To load data, for example, for `1 object` dataset, run: +~~~ +./opencv/build/bin/example_datasets_is_weizmann -p=/home/user/path_to_unpacked_folder/1obj/ +~~~ + +@defgroup datasets_msm Multiview Stereo Matching + +### EPFL Multi-View Stereo + +Implements loading dataset: + +"EPFL Multi-View Stereo": + +Usage: +-# From link above download dataset files: +`castle_dense\castle_dense_large\castle_entry\fountain\herzjesu_dense\herzjesu_dense_large_bounding\cameras\images\p.tar.gz`. +-# Unpack them in separate folder for each object. For example, for "fountain", in folder `fountain/` : +`fountain_dense_bounding.tar.gz -> bounding/`, +`fountain_dense_cameras.tar.gz -> camera/`, +`fountain_dense_images.tar.gz -> png/`, +`fountain_dense_p.tar.gz -> P/` +-# To load data, for example, for "fountain", run: +~~~ +./opencv/build/bin/example_datasets_msm_epfl -p=/home/user/path_to_unpacked_folder/fountain/ +~~~ + +### Stereo – Middlebury Computer Vision + +Implements loading dataset: + +"Stereo – Middlebury Computer Vision": + +Usage: +-# From link above download dataset files: +`dino\dinoRing\dinoSparseRing\temple\templeRing\templeSparseRing.zip` +-# Unpack them. +-# To load data, for example "temple" dataset, run: +~~~ +./opencv/build/bin/example_datasets_msm_middlebury -p=/home/user/path_to_unpacked_folder/temple/ +~~~ + +@defgroup datasets_or Object Recognition + +### ImageNet + +Implements loading dataset: "ImageNet": + +Usage: +-# From link above download dataset files: +`ILSVRC2010_images_train.tar\ILSVRC2010_images_test.tar\ILSVRC2010_images_val.tar` & devkit: +`ILSVRC2010_devkit-1.0.tar.gz` (Implemented loading of 2010 dataset as only this dataset has ground +truth for test data, but structure for ILSVRC2014 is similar) +-# Unpack them to: `some_folder/train/`, `some_folder/test/`, `some_folder/val` & +`some_folder/ILSVRC2010_validation_ground_truth.txt`, +`some_folder/ILSVRC2010_test_ground_truth.txt`. +-# Create file with labels: `some_folder/labels.txt`, for example, using python script below (each +file's row format: `synset,labelID,description`. For example: "n07751451,18,plum"). +-# Unpack all tar files in train. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_or_imagenet -p=/home/user/some_folder/ +~~~ + +Python script to parse `meta.mat`: +~~~{py} + import scipy.io + meta_mat = scipy.io.loadmat("devkit-1.0/data/meta.mat") + + labels_dic = dict((m[0][1][0], m[0][0][0][0]-1) for m in meta_mat['synsets'] + label_names_dic = dict((m[0][1][0], m[0][2][0]) for m in meta_mat['synsets'] + + for label in labels_dic.keys(): + print "{0},{1},{2}".format(label, labels_dic[label], label_names_dic[label]) +~~~ + +### MNIST + +Implements loading dataset: + +"MNIST": + +Usage: +-# From link above download dataset files: +`t10k-images-idx3-ubyte.gz`, `t10k-labels-idx1-ubyte.gz`, `train-images-idx3-ubyte.gz`, `train-labels-idx1-ubyte.gz`. +-# Unpack them. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_or_mnist -p=/home/user/path_to_unpacked_files/ +~~~ + +### SUN Database + +Implements loading dataset: + +"SUN Database, Scene Recognition Benchmark. SUN397": + +Usage: +-# From link above download dataset file: `SUN397.tar` & file with splits: `Partitions.zip` +-# Unpack `SUN397.tar` into folder: `SUN397/` & `Partitions.zip` into folder: `SUN397/Partitions/` +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_or_sun -p=/home/user/path_to_unpacked_files/SUN397/ +~~~ + +@defgroup datasets_pd Pedestrian Detection + +### Caltech Pedestrian Detection Benchmark + +Implements loading dataset: + +"Caltech Pedestrian Detection Benchmark": + +@note First version of Caltech Pedestrian dataset loading. Code to unpack all frames from seq files +commented as their number is huge! So currently load only meta information without data. Also +ground truth isn't processed, as need to convert it from mat files first. + +Usage: +-# From link above download dataset files: `set00.tar`-`set10.tar`. +-# Unpack them to separate folder. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_pd_caltech -p=/home/user/path_to_unpacked_folders/ +~~~ + +@defgroup datasets_slam SLAM + +### KITTI Vision Benchmark + +Implements loading dataset: + +"KITTI Vision Benchmark": + +Usage: +-# From link above download "Odometry" dataset files: +`data_odometry_gray\data_odometry_color\data_odometry_velodyne\data_odometry_poses\data_odometry_calib.zip`. +-# Unpack `data_odometry_poses.zip`, it creates folder `dataset/poses/`. After that unpack +`data_odometry_gray.zip`, `data_odometry_color.zip`, `data_odometry_velodyne.zip`. Folder +`dataset/sequences/` will be created with folders `00/..21/`. Each of these folders will contain: +`image_0/`, `image_1/`, `image_2/`, `image_3/`, `velodyne/` and files `calib.txt` & `times.txt`. +These two last files will be replaced after unpacking `data_odometry_calib.zip` at the end. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_slam_kitti -p=/home/user/path_to_unpacked_folder/dataset/ +~~~ + +### TUMindoor Dataset + +Implements loading dataset: + +"TUMindoor Dataset": + +Usage: +-# From link above download dataset files: `dslr\info\ladybug\pointcloud.tar.bz2` for each dataset: +`11-11-28 (1st floor)\11-12-13 (1st floor N1)\11-12-17a (4th floor)\11-12-17b (3rd floor)\11-12-17c (Ground I)\11-12-18a (Ground II)\11-12-18b (2nd floor)` +-# Unpack them in separate folder for each dataset. +`dslr.tar.bz2 -> dslr/`, +`info.tar.bz2 -> info/`, +`ladybug.tar.bz2 -> ladybug/`, +`pointcloud.tar.bz2 -> pointcloud/`. +-# To load each dataset run: +~~~ +./opencv/build/bin/example_datasets_slam_tumindoor -p=/home/user/path_to_unpacked_folders/ +~~~ + +@defgroup datasets_tr Text Recognition + +### The Chars74K Dataset + +Implements loading dataset: + +"The Chars74K Dataset": + +Usage: +-# From link above download dataset files: +`EnglishFnt\EnglishHnd\EnglishImg\KannadaHnd\KannadaImg.tgz`, `ListsTXT.tgz`. +-# Unpack them. +-# Move `.m` files from folder `ListsTXT/` to appropriate folder. For example, +`English/list_English_Img.m` for `EnglishImg.tgz`. +-# To load data, for example "EnglishImg", run: +~~~ +./opencv/build/bin/example_datasets_tr_chars -p=/home/user/path_to_unpacked_folder/English/ +~~~ + +### The Street View Text Dataset + +Implements loading dataset: + +"The Street View Text Dataset": + +Usage: +-# From link above download dataset file: `svt.zip`. +-# Unpack it. +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_tr_svt -p=/home/user/path_to_unpacked_folder/svt/svt1/ +~~~ + +#### Benchmark + +For this dataset was implemented benchmark with accuracy (mean f1): 0.217 + +To run benchmark execute: +~~~ +./opencv/build/bin/example_datasets_tr_svt_benchmark -p=/home/user/path_to_unpacked_folders/svt/svt1/ +~~~ + +@defgroup datasets_track Tracking + +### VOT 2015 Database + +Implements loading dataset: + +"VOT 2015 dataset comprises 60 short sequences showing various objects in challenging backgrounds. +The sequences were chosen from a large pool of sequences including the ALOV dataset, OTB2 dataset, +non-tracking datasets, Computer Vision Online, Professor Bob Fisher’s Image Database, Videezy, +Center for Research in Computer Vision, University of Central Florida, USA, NYU Center for Genomics +and Systems Biology, Data Wrangling, Open Access Directory and Learning and Recognition in Vision +Group, INRIA, France. The VOT sequence selection protocol was applied to obtain a representative +set of challenging sequences.": + +Usage: +-# From link above download dataset file: `vot2015.zip` +-# Unpack `vot2015.zip` into folder: `VOT2015/` +-# To load data run: +~~~ +./opencv/build/bin/example_datasets_track_vot -p=/home/user/path_to_unpacked_files/VOT2015/ +~~~ +@} + +*/ + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets +//! @{ + +struct Object +{ +}; + +class CV_EXPORTS Dataset +{ +public: + Dataset() {} + virtual ~Dataset() {} + + virtual void load(const std::string &path) = 0; + + std::vector< Ptr >& getTrain(int splitNum = 0); + std::vector< Ptr >& getTest(int splitNum = 0); + std::vector< Ptr >& getValidation(int splitNum = 0); + + int getNumSplits() const; + +protected: + std::vector< std::vector< Ptr > > train; + std::vector< std::vector< Ptr > > test; + std::vector< std::vector< Ptr > > validation; + +private: + std::vector< Ptr > empty; +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/fr_adience.hpp b/thirdparty/linux/include/opencv2/datasets/fr_adience.hpp new file mode 100644 index 0000000..c84bce1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/fr_adience.hpp @@ -0,0 +1,98 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_FR_ADIENCE_HPP +#define OPENCV_DATASETS_FR_ADIENCE_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_fr +//! @{ + +enum genderType +{ + male = 0, + female, + none +}; + +struct FR_adienceObj : public Object +{ + std::string user_id; + std::string original_image; + int face_id; + std::string age; + genderType gender; + int x; + int y; + int dx; + int dy; + int tilt_ang; + int fiducial_yaw_angle; + int fiducial_score; +}; + +class CV_EXPORTS FR_adience : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); + + std::vector paths; +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/fr_lfw.hpp b/thirdparty/linux/include/opencv2/datasets/fr_lfw.hpp new file mode 100644 index 0000000..7065da7 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/fr_lfw.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_FR_LFW_HPP +#define OPENCV_DATASETS_FR_LFW_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_fr +//! @{ + +struct FR_lfwObj : public Object +{ + std::string image1, image2; + bool same; +}; + +class CV_EXPORTS FR_lfw : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/gr_chalearn.hpp b/thirdparty/linux/include/opencv2/datasets/gr_chalearn.hpp new file mode 100644 index 0000000..a8eaa6c --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/gr_chalearn.hpp @@ -0,0 +1,96 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_GR_CHALEARN_HPP +#define OPENCV_DATASETS_GR_CHALEARN_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_gr +//! @{ + +struct groundTruth +{ + int gestureID, initialFrame, lastFrame; +}; + +struct join +{ + double Wx, Wy, Wz, Rx, Ry, Rz, Rw, Px, Py; +}; + +struct skeleton +{ + join s[20]; +}; + +struct GR_chalearnObj : public Object +{ + std::string name, nameColor, nameDepth, nameUser; + int numFrames, fps, depth; + std::vector groundTruths; + std::vector skeletons; +}; + +class CV_EXPORTS GR_chalearn : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/gr_skig.hpp b/thirdparty/linux/include/opencv2/datasets/gr_skig.hpp new file mode 100644 index 0000000..9c86224 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/gr_skig.hpp @@ -0,0 +1,118 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_GR_SKIG_HPP +#define OPENCV_DATASETS_GR_SKIG_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_gr +//! @{ + +enum actionType +{ + circle = 1, + triangle, + updown, + rightleft, + wave, + z, + cross, + comehere, + turnaround, + pat +}; + +enum poseType +{ + fist = 1, + index, + flat +}; + +enum illuminationType +{ + light = 1, + dark +}; + +enum backgroundType +{ + woodenBoard = 1, + whitePaper, + paperWithCharacters +}; + +struct GR_skigObj : public Object +{ + std::string rgb; + std::string dep; + char person; // 1..6 + backgroundType background; + illuminationType illumination; + poseType pose; + actionType type; +}; + +class CV_EXPORTS GR_skig : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/hpe_humaneva.hpp b/thirdparty/linux/include/opencv2/datasets/hpe_humaneva.hpp new file mode 100644 index 0000000..5366e0d --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/hpe_humaneva.hpp @@ -0,0 +1,90 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_HPE_HUMANEVA_HPP +#define OPENCV_DATASETS_HPE_HUMANEVA_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_hpe +//! @{ + +struct HPE_humanevaObj : public Object +{ + char person; // 1..4 + std::string action; + int type1; + std::string type2; + Matx13d ofs; + std::string fileName; + std::vector imageNames; // for HumanEva_II +}; + +enum datasetType +{ + humaneva_1 = 1, + humaneva_2 +}; + +class CV_EXPORTS HPE_humaneva : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(int num=humaneva_1); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/hpe_parse.hpp b/thirdparty/linux/include/opencv2/datasets/hpe_parse.hpp new file mode 100644 index 0000000..7629e2c --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/hpe_parse.hpp @@ -0,0 +1,78 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_HPE_PARSE_HPP +#define OPENCV_DATASETS_HPE_PARSE_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_hpe +//! @{ + +struct HPE_parseObj : public Object +{ + std::string name; +}; + +class CV_EXPORTS HPE_parse : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/ir_affine.hpp b/thirdparty/linux/include/opencv2/datasets/ir_affine.hpp new file mode 100644 index 0000000..3b04a4b --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/ir_affine.hpp @@ -0,0 +1,80 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_IR_AFFINE_HPP +#define OPENCV_DATASETS_IR_AFFINE_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_ir +//! @{ + +struct IR_affineObj : public Object +{ + std::string imageName; + Matx33d mat; +}; + +class CV_EXPORTS IR_affine : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/ir_robot.hpp b/thirdparty/linux/include/opencv2/datasets/ir_robot.hpp new file mode 100644 index 0000000..0acfe0a --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/ir_robot.hpp @@ -0,0 +1,89 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_IR_ROBOT_HPP +#define OPENCV_DATASETS_IR_ROBOT_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_ir +//! @{ + +// calibration matrix from calibrationFile.mat +// 2.8290e+03 0.0000e+00 8.0279e+02 +// 0.0000e+00 2.8285e+03 6.1618e+02 +// 0.0000e+00 0.0000e+00 1.0000e+00 + +struct cameraPos +{ + std::vector images; +}; + +struct IR_robotObj : public Object +{ + std::string name; + std::vector pos; +}; + +class CV_EXPORTS IR_robot : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/is_bsds.hpp b/thirdparty/linux/include/opencv2/datasets/is_bsds.hpp new file mode 100644 index 0000000..7357a67 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/is_bsds.hpp @@ -0,0 +1,78 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_IS_BSDS_HPP +#define OPENCV_DATASETS_IS_BSDS_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_is +//! @{ + +struct IS_bsdsObj : public Object +{ + std::string name; +}; + +class CV_EXPORTS IS_bsds : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/is_weizmann.hpp b/thirdparty/linux/include/opencv2/datasets/is_weizmann.hpp new file mode 100644 index 0000000..5daa420 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/is_weizmann.hpp @@ -0,0 +1,81 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_IS_WEIZMANN_HPP +#define OPENCV_DATASETS_IS_WEIZMANN_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_is +//! @{ + +struct IS_weizmannObj : public Object +{ + std::string imageName; + std::string srcBw; + std::string srcColor; + std::string humanSeg; // TODO: read human segmented +}; + +class CV_EXPORTS IS_weizmann : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/msm_epfl.hpp b/thirdparty/linux/include/opencv2/datasets/msm_epfl.hpp new file mode 100644 index 0000000..a08fc4b --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/msm_epfl.hpp @@ -0,0 +1,90 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_MSM_EPFL_HPP +#define OPENCV_DATASETS_MSM_EPFL_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_msm +//! @{ + +struct cameraParam +{ + Matx33d mat1; + double mat2[3]; + Matx33d mat3; + double mat4[3]; + int imageWidth, imageHeight; +}; + +struct MSM_epflObj : public Object +{ + std::string imageName; + Matx23d bounding; + Matx34d p; + cameraParam camera; +}; + +class CV_EXPORTS MSM_epfl : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/msm_middlebury.hpp b/thirdparty/linux/include/opencv2/datasets/msm_middlebury.hpp new file mode 100644 index 0000000..2fd67bf --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/msm_middlebury.hpp @@ -0,0 +1,81 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_MSM_MIDDLEBURY_HPP +#define OPENCV_DATASETS_MSM_MIDDLEBURY_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_msm +//! @{ + +struct MSM_middleburyObj : public Object +{ + std::string imageName; + Matx33d k; + Matx33d r; + double t[3]; +}; + +class CV_EXPORTS MSM_middlebury : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/or_imagenet.hpp b/thirdparty/linux/include/opencv2/datasets/or_imagenet.hpp new file mode 100644 index 0000000..26a8f63 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/or_imagenet.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_OR_IMAGENET_HPP +#define OPENCV_DATASETS_OR_IMAGENET_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_or +//! @{ + +struct OR_imagenetObj : public Object +{ + int id; + std::string image; +}; + +class CV_EXPORTS OR_imagenet : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/or_mnist.hpp b/thirdparty/linux/include/opencv2/datasets/or_mnist.hpp new file mode 100644 index 0000000..ff6bd60 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/or_mnist.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_OR_MNIST_HPP +#define OPENCV_DATASETS_OR_MNIST_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_or +//! @{ + +struct OR_mnistObj : public Object +{ + char label; // 0..9 + Mat image; // [28][28] +}; + +class CV_EXPORTS OR_mnist : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/or_pascal.hpp b/thirdparty/linux/include/opencv2/datasets/or_pascal.hpp new file mode 100644 index 0000000..bca8e62 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/or_pascal.hpp @@ -0,0 +1,102 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_VOC_PASCAL_HPP +#define OPENCV_DATASETS_VOC_PASCAL_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_or +//! @{ +struct PascalPart: public Object +{ + std::string name; + int xmin; + int ymin; + int xmax; + int ymax; +}; + +struct PascalObj: public PascalPart +{ + std::string pose; + bool truncated; + bool difficult; + bool occluded; + + std::vector parts; +}; + +struct OR_pascalObj : public Object +{ + std::string filename; + + int width; + int height; + int depth; + + std::vector objects; +}; + +class CV_EXPORTS OR_pascal : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +}// namespace dataset +}// namespace cv + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/or_sun.hpp b/thirdparty/linux/include/opencv2/datasets/or_sun.hpp new file mode 100644 index 0000000..059c0d4 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/or_sun.hpp @@ -0,0 +1,81 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_OR_SUN_HPP +#define OPENCV_DATASETS_OR_SUN_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_or +//! @{ + +struct OR_sunObj : public Object +{ + int label; + std::string name; +}; + +class CV_EXPORTS OR_sun : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); + + std::vector paths; +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/pd_caltech.hpp b/thirdparty/linux/include/opencv2/datasets/pd_caltech.hpp new file mode 100644 index 0000000..9ff7278 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/pd_caltech.hpp @@ -0,0 +1,89 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_PD_CALTECH_HPP +#define OPENCV_DATASETS_PD_CALTECH_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_pd +//! @{ + +struct PD_caltechObj : public Object +{ + //double groundTrue[][]; + //Mat image; + std::string name; + std::vector< std::string > imageNames; +}; + +// +// first version of Caltech Pedestrian dataset loading +// code to unpack all frames from seq files commented as their number is huge +// so currently load only meta information without data +// +// also ground truth isn't processed, as need to convert it from mat files first +// + +class CV_EXPORTS PD_caltech : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/pd_inria.hpp b/thirdparty/linux/include/opencv2/datasets/pd_inria.hpp new file mode 100644 index 0000000..7586578 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/pd_inria.hpp @@ -0,0 +1,96 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_PD_INRIA_HPP +#define OPENCV_DATASETS_PD_INRIA_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_pd +//! @{ + +enum sampleType +{ + POS = 0, + NEG = 1 +}; + +struct PD_inriaObj : public Object +{ + // image file name + std::string filename; + + // positive or negative + sampleType sType; + + // image size + int width; + int height; + int depth; + + // bounding boxes + std::vector< Rect > bndboxes; +}; + +class CV_EXPORTS PD_inria : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/slam_kitti.hpp b/thirdparty/linux/include/opencv2/datasets/slam_kitti.hpp new file mode 100644 index 0000000..1b7c408 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/slam_kitti.hpp @@ -0,0 +1,87 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_SLAM_KITTI_HPP +#define OPENCV_DATASETS_SLAM_KITTI_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_slam +//! @{ + +struct pose +{ + double elem[12]; +}; + +struct SLAM_kittiObj : public Object +{ + std::string name; + std::vector images[4]; + std::vector velodyne; + std::vector times, p[4]; + std::vector posesArray; +}; + +class CV_EXPORTS SLAM_kitti : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/slam_tumindoor.hpp b/thirdparty/linux/include/opencv2/datasets/slam_tumindoor.hpp new file mode 100644 index 0000000..758dd13 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/slam_tumindoor.hpp @@ -0,0 +1,87 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_SLAM_TUMINDOOR_HPP +#define OPENCV_DATASETS_SLAM_TUMINDOOR_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_slam +//! @{ + +enum imageType +{ + LEFT = 0, + RIGHT, + LADYBUG +}; + +struct SLAM_tumindoorObj : public Object +{ + std::string name; + Matx44d transformMat; + imageType type; +}; + +class CV_EXPORTS SLAM_tumindoor : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/tr_chars.hpp b/thirdparty/linux/include/opencv2/datasets/tr_chars.hpp new file mode 100644 index 0000000..c213bff --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/tr_chars.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_TR_CHARS_HPP +#define OPENCV_DATASETS_TR_CHARS_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_tr +//! @{ + +struct TR_charsObj : public Object +{ + std::string imgName; + int label; +}; + +class CV_EXPORTS TR_chars : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/tr_icdar.hpp b/thirdparty/linux/include/opencv2/datasets/tr_icdar.hpp new file mode 100644 index 0000000..abfd7db --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/tr_icdar.hpp @@ -0,0 +1,87 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_TR_ICDAR_HPP +#define OPENCV_DATASETS_TR_ICDAR_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_tr +//! @{ + +struct word +{ + std::string value; + int height, width, x, y; +}; + +struct TR_icdarObj : public Object +{ + std::string fileName; + std::vector lex100; + std::vector lexFull; + std::vector words; +}; + +class CV_EXPORTS TR_icdar : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/tr_svt.hpp b/thirdparty/linux/include/opencv2/datasets/tr_svt.hpp new file mode 100644 index 0000000..6c2d533 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/tr_svt.hpp @@ -0,0 +1,86 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_TR_SVT_HPP +#define OPENCV_DATASETS_TR_SVT_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_tr +//! @{ + +struct tag +{ + std::string value; + int height, width, x, y; +}; + +struct TR_svtObj : public Object +{ + std::string fileName; + std::vector lex; + std::vector tags; +}; + +class CV_EXPORTS TR_svt : public Dataset +{ +public: + virtual void load(const std::string &path) = 0; + + static Ptr create(); +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/track_alov.hpp b/thirdparty/linux/include/opencv2/datasets/track_alov.hpp new file mode 100644 index 0000000..276e2f1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/track_alov.hpp @@ -0,0 +1,107 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_TRACK_ALOV_HPP +#define OPENCV_DATASETS_TRACK_ALOV_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" +#include "opencv2/datasets/util.hpp" + +using namespace std; + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_track +//! @{ + +struct TRACK_alovObj : public Object +{ + int id; + std::string imagePath; + vector gtbb; +}; + +const string sectionNames[] = { "01-Light", "02-SurfaceCover", "03-Specularity", "04-Transparency", "05-Shape", "06-MotionSmoothness", "07-MotionCoherence", +"08-Clutter", "09-Confusion", "10-LowContrast", "11-Occlusion", "12-MovingCamera", "13-ZoomingCamera", "14-LongDuration" }; + +const int sectionSizes[] = { 33, 15, 18, 20, 24, 22, 12, 15, 37, 23, 34, 22, 29, 10 }; + +class CV_EXPORTS TRACK_alov : public Dataset +{ +public: + static Ptr create(); + + virtual void load(const std::string &path) = 0; + + //Load only frames with annotations (~every 5-th frame) + virtual void loadAnnotatedOnly(const std::string &path) = 0; + + virtual int getDatasetsNum() = 0; + + virtual int getDatasetLength(int id) = 0; + + virtual bool initDataset(int id) = 0; + + virtual bool getNextFrame(Mat &frame) = 0; + virtual vector getNextGT() = 0; + + //Get frame/GT by datasetID (1..N) frameID (1..K) + virtual bool getFrame(Mat &frame, int datasetID, int frameID) = 0; + virtual vector getGT(int datasetID, int frameID) = 0; + +protected: + vector > > data; + int activeDatasetID; + int frameCounter; +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/track_vot.hpp b/thirdparty/linux/include/opencv2/datasets/track_vot.hpp new file mode 100644 index 0000000..6249f02 --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/track_vot.hpp @@ -0,0 +1,96 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_TRACK_VOT_HPP +#define OPENCV_DATASETS_TRACK_VOT_HPP + +#include +#include + +#include "opencv2/datasets/dataset.hpp" +#include "opencv2/datasets/util.hpp" + +using namespace std; + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets_track +//! @{ + +struct TRACK_votObj : public Object +{ + int id; + std::string imagePath; + vector gtbb; +}; + +class CV_EXPORTS TRACK_vot : public Dataset +{ +public: + static Ptr create(); + + virtual void load(const std::string &path) = 0; + + virtual int getDatasetsNum() = 0; + + virtual int getDatasetLength(int id) = 0; + + virtual bool initDataset(int id) = 0; + + virtual bool getNextFrame(Mat &frame) = 0; + + virtual vector getGT() = 0; + +protected: + vector > > data; + int activeDatasetID; + int frameCounter; +}; + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/datasets/util.hpp b/thirdparty/linux/include/opencv2/datasets/util.hpp new file mode 100644 index 0000000..316de3a --- /dev/null +++ b/thirdparty/linux/include/opencv2/datasets/util.hpp @@ -0,0 +1,74 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DATASETS_UTIL_HPP +#define OPENCV_DATASETS_UTIL_HPP + +#include +#include + +#include +#include // atoi, atof + +#include + +#include + +namespace cv +{ +namespace datasets +{ + +//! @addtogroup datasets +//! @{ + +void CV_EXPORTS split(const std::string &s, std::vector &elems, char delim); + +void CV_EXPORTS createDirectory(const std::string &path); + +void CV_EXPORTS getDirList(const std::string &dirName, std::vector &fileNames); + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/dnn.hpp b/thirdparty/linux/include/opencv2/dnn.hpp new file mode 100644 index 0000000..37be989 --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn.hpp @@ -0,0 +1,64 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_HPP__ +#define __OPENCV_DNN_HPP__ + +// This is an umbrealla header to include into you project. +// We are free to change headers layout in dnn subfolder, so please include +// this header for future compartibility + + +/** @defgroup dnn Deep Neural Network module + @{ + This module contains: + - API for new layers creation, layers are building bricks of neural networks; + - set of built-in most-useful Layers; + - API to constuct and modify comprehensive neural networks from layers; + - functionality for loading serialized networks models from differnet frameworks. + + Functionality of this module is designed only for forward pass computations (i. e. network testing). + A network training is in principle not supported. + @} +*/ +#include + +#endif /* __OPENCV_DNN_HPP__ */ diff --git a/thirdparty/linux/include/opencv2/dnn/all_layers.hpp b/thirdparty/linux/include/opencv2/dnn/all_layers.hpp new file mode 100644 index 0000000..9d26b35 --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/all_layers.hpp @@ -0,0 +1,423 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_ALL_LAYERS_HPP__ +#define __OPENCV_DNN_DNN_ALL_LAYERS_HPP__ +#include + +namespace cv +{ +namespace dnn +{ +//! @addtogroup dnn +//! @{ + +/** @defgroup dnnLayerList Partial List of Implemented Layers + @{ + This subsection of dnn module contains information about bult-in layers and their descriptions. + + Classes listed here, in fact, provides C++ API for creating intances of bult-in layers. + In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones. + You can use both API, but factory API is less convinient for native C++ programming and basically designed for use inside importers (see @ref Importer, @ref createCaffeImporter(), @ref createTorchImporter()). + + Bult-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers. + In partuclar, the following layers and Caffe @ref Importer were tested to reproduce Caffe functionality: + - Convolution + - Deconvolution + - Pooling + - InnerProduct + - TanH, ReLU, Sigmoid, BNLL, Power, AbsVal + - Softmax + - Reshape, Flatten, Slice, Split + - LRN + - MVN + - Dropout (since it does nothing on forward pass -)) +*/ + + //! LSTM recurrent layer + class CV_EXPORTS_W LSTMLayer : public Layer + { + public: + /** Creates instance of LSTM layer */ + static CV_WRAP Ptr create(); + + /** Set trained weights for LSTM layer. + LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights. + + Let @f$x_t@f$ be current input, @f$h_t@f$ be current output, @f$c_t@f$ be current state. + Than current output and current cell state is computed as follows: + @f{eqnarray*}{ + h_t &= o_t \odot tanh(c_t), \\ + c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\ + @f} + where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned wights. + + Gates are computed as follows: + @f{eqnarray*}{ + i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\ + f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\ + o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\ + g_t &= tanh &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\ + @f} + where @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices: + @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$. + + For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$ + (i.e. @f$W_x@f$ is vertical contacentaion of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$. + The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$ + and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$. + + @param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_h @f$) + @param Wx is matrix defining how current input is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_x @f$) + @param b is bias vector (i.e. according to abovemtioned notation is @f$ b @f$) + */ + CV_WRAP virtual void setWeights(const Blob &Wh, const Blob &Wx, const Blob &b) = 0; + + /** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape. + * @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used, + * where `Wh` is parameter from setWeights(). + */ + CV_WRAP virtual void setOutShape(const BlobShape &outTailShape = BlobShape::empty()) = 0; + + /** @brief Set @f$ h_{t-1} @f$ value that will be used in next forward() calls. + * @details By-default @f$ h_{t-1} @f$ is inited by zeros and updated after each forward() call. + */ + CV_WRAP virtual void setH(const Blob &H) = 0; + /** @brief Returns current @f$ h_{t-1} @f$ value (deep copy). */ + CV_WRAP virtual Blob getH() const = 0; + + /** @brief Set @f$ c_{t-1} @f$ value that will be used in next forward() calls. + * @details By-default @f$ c_{t-1} @f$ is inited by zeros and updated after each forward() call. + */ + CV_WRAP virtual void setC(const Blob &C) = 0; + /** @brief Returns current @f$ c_{t-1} @f$ value (deep copy). */ + CV_WRAP virtual Blob getC() const = 0; + + /** @brief Specifies either interpet first dimension of input blob as timestamp dimenion either as sample. + * + * If flag is set to true then shape of input blob will be interpeted as [`T`, `N`, `[data dims]`] where `T` specifies number of timpestamps, `N` is number of independent streams. + * In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times. + * + * If flag is set to false then shape of input blob will be interpeted as [`N`, `[data dims]`]. + * In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`]. + */ + CV_WRAP virtual void setUseTimstampsDim(bool use = true) = 0; + + /** @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output. + * @details Shape of the second output is the same as first output. + */ + CV_WRAP virtual void setProduceCellOutput(bool produce = false) = 0; + + /** In common case it use single input with @f$x_t@f$ values to compute output(s) @f$h_t@f$ (and @f$c_t@f$). + * @param input should contain packed values @f$x_t@f$ + * @param output contains computed outputs: @f$h_t@f$ (and @f$c_t@f$ if setProduceCellOutput() flag was set to true). + * + * If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`], + * where `T` specifies number of timpestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]). + * + * If setUseTimstampsDim() is set to fase then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension. + * (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]). + */ + void forward(std::vector &input, std::vector &output); + + int inputNameToIndex(String inputName); + + int outputNameToIndex(String outputName); + }; + + //! Classical recurrent layer + class CV_EXPORTS_W RNNLayer : public Layer + { + public: + /** Creates instance of RNNLayer */ + static CV_WRAP Ptr create(); + + /** Setups learned weights. + + Recurrent-layer behavior on each step is defined by current input @f$ x_t @f$, previous state @f$ h_t @f$ and learned weights as follows: + @f{eqnarray*}{ + h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h), \\ + o_t &= tanh&(W_{ho} h_t + b_o), + @f} + + @param Wxh is @f$ W_{xh} @f$ matrix + @param bh is @f$ b_{h} @f$ vector + @param Whh is @f$ W_{hh} @f$ matrix + @param Who is @f$ W_{xo} @f$ matrix + @param bo is @f$ b_{o} @f$ vector + */ + CV_WRAP virtual void setWeights(const Blob &Wxh, const Blob &bh, const Blob &Whh, const Blob &Who, const Blob &bo) = 0; + + /** @brief If this flag is set to true then layer will produce @f$ h_t @f$ as second output. + * @details Shape of the second output is the same as first output. + */ + CV_WRAP virtual void setProduceHiddenOutput(bool produce = false) = 0; + + /** Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$. + + @param input should contain packed input @f$x_t@f$. + @param output should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true). + + @p input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively. + + @p output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix. + + If setProduceHiddenOutput() is set to true then @p output[1] will contain a Blob with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix. + */ + void forward(std::vector &input, std::vector &output); + }; + + class CV_EXPORTS_W BaseConvolutionLayer : public Layer + { + public: + + CV_PROP_RW Size kernel, stride, pad, dilation, adjustPad; + CV_PROP_RW String padMode; + }; + + class CV_EXPORTS_W ConvolutionLayer : public BaseConvolutionLayer + { + public: + + static CV_WRAP Ptr create(Size kernel = Size(3, 3), Size stride = Size(1, 1), Size pad = Size(0, 0), Size dilation = Size(1, 1)); + }; + + class CV_EXPORTS_W DeconvolutionLayer : public BaseConvolutionLayer + { + public: + + static CV_WRAP Ptr create(Size kernel = Size(3, 3), Size stride = Size(1, 1), Size pad = Size(0, 0), Size dilation = Size(1, 1), Size adjustPad = Size()); + }; + + class CV_EXPORTS_W LRNLayer : public Layer + { + public: + + enum Type + { + CHANNEL_NRM, + SPATIAL_NRM + }; + CV_PROP_RW int type; + + CV_PROP_RW int size; + CV_PROP_RW double alpha, beta, bias; + CV_PROP_RW bool normBySize; + + static CV_WRAP Ptr create(int type = LRNLayer::CHANNEL_NRM, int size = 5, + double alpha = 1, double beta = 0.75, double bias = 1, + bool normBySize = true); + }; + + class CV_EXPORTS_W PoolingLayer : public Layer + { + public: + + enum Type + { + MAX, + AVE, + STOCHASTIC + }; + + CV_PROP_RW int type; + CV_PROP_RW Size kernel, stride, pad; + CV_PROP_RW bool globalPooling; + CV_PROP_RW String padMode; + + static CV_WRAP Ptr create(int type = PoolingLayer::MAX, Size kernel = Size(2, 2), + Size stride = Size(1, 1), Size pad = Size(0, 0), + const cv::String& padMode = ""); + static CV_WRAP Ptr createGlobal(int type = PoolingLayer::MAX); + }; + + class CV_EXPORTS_W SoftmaxLayer : public Layer + { + public: + + static CV_WRAP Ptr create(int axis = 1); + }; + + class CV_EXPORTS_W InnerProductLayer : public Layer + { + public: + CV_PROP_RW int axis; + + static CV_WRAP Ptr create(int axis = 1); + }; + + class CV_EXPORTS_W MVNLayer : public Layer + { + public: + CV_PROP_RW double eps; + CV_PROP_RW bool normVariance, acrossChannels; + + static CV_WRAP Ptr create(bool normVariance = true, bool acrossChannels = false, double eps = 1e-9); + }; + + /* Reshaping */ + + class CV_EXPORTS_W ReshapeLayer : public Layer + { + public: + CV_PROP_RW BlobShape newShapeDesc; + CV_PROP_RW Range newShapeRange; + + static CV_WRAP Ptr create(const BlobShape &newShape, Range applyingRange = Range::all(), + bool enableReordering = false); + }; + + class CV_EXPORTS_W ConcatLayer : public Layer + { + public: + int axis; + + static CV_WRAP Ptr create(int axis = 1); + }; + + class CV_EXPORTS_W SplitLayer : public Layer + { + public: + int outputsCount; //!< Number of copies that will be produced (is ignored when negative). + + static CV_WRAP Ptr create(int outputsCount = -1); + }; + + class CV_EXPORTS_W SliceLayer : public Layer + { + public: + CV_PROP_RW int axis; + CV_PROP std::vector sliceIndices; + + static CV_WRAP Ptr create(int axis); + static CV_WRAP Ptr create(int axis, const std::vector &sliceIndices); + }; + + /* Activations */ + + class CV_EXPORTS_W ReLULayer : public Layer + { + public: + CV_PROP_RW double negativeSlope; + + static CV_WRAP Ptr create(double negativeSlope = 0); + }; + + class CV_EXPORTS_W ChannelsPReLULayer : public Layer + { + public: + static CV_WRAP Ptr create(); + }; + + class CV_EXPORTS_W TanHLayer : public Layer + { + public: + static CV_WRAP Ptr create(); + }; + + class CV_EXPORTS_W SigmoidLayer : public Layer + { + public: + static CV_WRAP Ptr create(); + }; + + class CV_EXPORTS_W BNLLLayer : public Layer + { + public: + static CV_WRAP Ptr create(); + }; + + class CV_EXPORTS_W AbsLayer : public Layer + { + public: + static CV_WRAP Ptr create(); + }; + + class CV_EXPORTS_W PowerLayer : public Layer + { + public: + CV_PROP_RW double power, scale, shift; + + static CV_WRAP Ptr create(double power = 1, double scale = 1, double shift = 0); + }; + + /* Layers using in semantic segmentation */ + + class CV_EXPORTS_W CropLayer : public Layer + { + public: + CV_PROP int startAxis; + CV_PROP std::vector offset; + + static Ptr create(int start_axis, const std::vector &offset); + }; + + class CV_EXPORTS_W EltwiseLayer : public Layer + { + public: + enum EltwiseOp + { + PROD = 0, + SUM = 1, + MAX = 2, + }; + + static Ptr create(EltwiseOp op, const std::vector &coeffs); + }; + + class CV_EXPORTS_W BatchNormLayer : public Layer + { + public: + static CV_WRAP Ptr create(float eps, bool has_weights, bool has_bias); + }; + + class CV_EXPORTS_W MaxUnpoolLayer : public Layer + { + public: + static CV_WRAP Ptr create(Size unpoolSize); + }; + +//! @} +//! @} + +} +} +#endif diff --git a/thirdparty/linux/include/opencv2/dnn/blob.hpp b/thirdparty/linux/include/opencv2/dnn/blob.hpp new file mode 100644 index 0000000..71e929d --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/blob.hpp @@ -0,0 +1,341 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_BLOB_HPP__ +#define __OPENCV_DNN_DNN_BLOB_HPP__ +#include +#include +#include +#include + +namespace cv +{ +namespace dnn +{ +//! @addtogroup dnn +//! @{ + + /** @brief Lightweight class for storing and processing a shape of blob (or anything else). */ + struct CV_EXPORTS_W BlobShape + { + BlobShape(); //!< Creates [1, 1, 1, 1] shape @todo Make more clearer behavior. + explicit BlobShape(int s0); //!< Creates 1-dim shape [@p s0] + BlobShape(int s0, int s1); //!< @overload + BlobShape(int s0, int s1, int s2); //!< @overload + BlobShape(int num, int cn, int rows, int cols); //!< Creates 4-dim shape [@p num, @p cn, @p rows, @p cols] + + //! Creates n-dim shape from the @p sizes array; if @p sizes is NULL then shape will contain unspecified data + BlobShape(int ndims, const int *sizes); + BlobShape(const std::vector &sizes); //!< Creates n-dim shape from the @p sizes vector + template + BlobShape(const Vec &shape); //!< Creates n-dim shape from @ref cv::Vec + + //! Creates n-dim shape and fill its by @p fill + static BlobShape all(int ndims, int fill = 1); + + /** @brief Returns number of dimensions. */ + int dims() const; + + /** @brief Returns reference to the size of the specified @p axis. + * + * Negative @p axis is supported, in this case a counting starts from the last axis, + * i. e. -1 corresponds to last axis. + * If non-existing axis was passed then an error will be generated. + */ + int &size(int axis); + + /** @brief Returns the size of the specified @p axis. + * @see size() + */ + int size(int axis) const; + + int operator[](int axis) const; //!< Does the same thing as size(axis). + int &operator[](int axis); //!< Does the same thing as size(int) const. + + /** @brief Returns the size of the specified @p axis. + * + * Does the same thing as size(int) const, but if non-existing axis will be passed then 1 will be returned, + * therefore this function always finishes successfully. + */ + int xsize(int axis) const; + + /** @brief Converts @p axis index to canonical format (where 0 <= @p axis < dims()). */ + int canonicalAxis(int axis) const; + + /** @brief Returns the product of all sizes of axes. */ + ptrdiff_t total() const; + + /** @brief Computes the product of sizes of axes among the specified axes range [@p startAxis; @p endAxis). + * @details Negative axis indexing can be used. @sa Blob::total(int,int) + */ + ptrdiff_t total(int startAxis, int endAxis = INT_MAX) const; + + /** @brief Constructs new shape from axes in range [@p startAxis; @p endAxis). + * @details Negative axis indexing can be used. @sa Blob::total(int,int) + */ + BlobShape slice(int startAxis, int endAxis = INT_MAX) const; + + /** @brief Returns pointer to the first element of continuous size array. */ + const int *ptr() const; + /** @overload */ + int *ptr(); + + bool equal(const BlobShape &other) const; //!< Checks equality of two shapes. + bool operator== (const BlobShape &r) const; //!< @sa equal() + + BlobShape operator+ (const BlobShape &r) const; //!< Contacenates two shapes. + + static BlobShape like(const Mat &m); //!< Returns shape of passed Mat. + static BlobShape like(const UMat &m); //!< Returns shape of passed UMat. + + static BlobShape empty(); //!< Returns empty shape []. + bool isEmpty() const; //!< Returns true if shape is empty (i.e []). + +#ifdef CV_CXX_MOVE_SEMANTICS + //TBD +#endif + + private: + cv::AutoBuffer sz; + }; + + + /** @brief This class provides methods for continuous n-dimensional CPU and GPU array processing. + * + * The class is realized as a wrapper over @ref cv::Mat and @ref cv::UMat. + * It will support methods for switching and logical synchronization between CPU and GPU. + */ + class CV_EXPORTS_W Blob + { + public: + Blob(); + + /** @brief Constructs blob with specified @p shape and @p type. */ + explicit Blob(const BlobShape &shape, int type = CV_32F, int allocFlags = ALLOC_MAT); + + /** @brief Constructs Blob from existing Mat or UMat. */ + Blob(InputArray data); + + /** @brief Constructs 4-dimensional blob (so-called batch) from image or array of images. + * @param image 2-dimensional multi-channel or 3-dimensional single-channel image (or array of such images) + * @param dstCn specifies size of second axis of ouptut blob + */ + static Blob fromImages(InputArray image, int dstCn = -1); + + /** @brief Works like Blob::fromImages() but in-place. */ + void batchFromImages(InputArray image, int dstCn = -1); + + /** @brief Creates blob with specified @p shape and @p type. */ + void create(const BlobShape &shape, int type = CV_32F, int allocFlags = ALLOC_MAT); + + /** @brief Creates blob from Mat or UMat without copying the data. + * @details If in is Mat then Mat data is populated, otherwise - UMat. + */ + void fill(InputArray in); + + /** @brief Creates blob from user data. + * @details If @p deepCopy is false then CPU data will not be allocated. + */ + void fill(const BlobShape &shape, int type, void *data, bool deepCopy = true); + + /** @brief Sets @p value to the last used data (if @p allocFlags = -1). + * @details If @p allocFlags != -1 then destination data (Mat or UMat) is determined by flags from AllocFlag enum like in create(). + */ + void setTo(InputArray value, int allocFlags = -1); + + Mat& matRef(bool writeOnly = true); //!< Returns reference to cv::Mat, containing blob data. + const Mat& matRefConst() const; //!< Returns reference to cv::Mat, containing blob data, for read-only purposes. + UMat &umatRef(bool writeOnly = true); //!< Returns reference to cv::UMat, containing blob data. + const UMat &umatRefConst() const; //!< Returns reference to cv::UMat, containing blob data, for read-only purposes. + + template + XMat &getRef(bool writeOnly = true); + template + const XMat &getRefConst() const; + + void updateMat(bool syncData = true) const; //!< Actualizes data stored inside Mat of Blob; if @p syncData is false then only shape will be actualized. + void updateUMat(bool syncData = true) const; //!< Actualizes data stored inside Mat of Blob; if @p syncData is false then only shape will be actualized. + void sync() const; //!< Updates Mat and UMat of Blob. + + /** @brief Returns number of blob dimensions. */ + int dims() const; + + /** @brief Returns the size of the specified @p axis. + * + * Negative @p axis is supported, in this case a counting starts from the last axis, + * i. e. -1 corresponds to last axis. + * If non-existing axis was passed then an error will be generated. + */ + int size(int axis) const; + + /** @brief Returns the size of the specified @p axis. + * + * Does the same thing as size(int) const, but if non-existing axis will be passed then 1 will be returned, + * therefore this function always finishes successfully. + */ + int xsize(int axis) const; + + /** @brief Computes the product of sizes of axes among the specified axes range [@p startAxis; @p endAxis). + * @param startAxis the first axis to include in the range. + * @param endAxis the first axis to exclude from the range. + * @details Negative axis indexing can be used. + */ + size_t total(int startAxis = 0, int endAxis = INT_MAX) const; + + /** @brief Converts @p axis index to canonical format (where 0 <= @p axis < dims()). */ + int canonicalAxis(int axis) const; + + /** @brief Returns shape of the blob. */ + BlobShape shape() const; + + /** @brief Checks equality of two blobs shapes. */ + bool equalShape(const Blob &other) const; + + /** @brief Returns slice of first two dimensions. + * @details The behaviour is similar to the following numpy code: blob[n, cn, ...] + */ + Mat getPlane(int n, int cn); + + /** @brief Returns slice of first dimension. + * @details The behaviour is similar to getPlane(), but returns all + * channels * rows * cols values, corresponding to the n-th value + * of the first dimension. + */ + Mat getPlanes(int n); + + /* Shape getters of 4-dimensional blobs. */ + int cols() const; //!< Returns size of the fourth axis blob. + int rows() const; //!< Returns size of the thrid axis blob. + int channels() const; //!< Returns size of the second axis blob. + int num() const; //!< Returns size of the first axis blob. + Size size2() const; //!< Returns cv::Size(cols(), rows()) + Vec4i shape4() const; //!< Returns shape of first four blob axes. + + /** @brief Returns linear index of the element with specified coordinates in the blob. + * + * If @p n < dims() then unspecified coordinates will be filled by zeros. + * If @p n > dims() then extra coordinates will be ignored. + */ + template + size_t offset(const Vec &pos) const; + /** @overload */ + size_t offset(int n = 0, int cn = 0, int row = 0, int col = 0) const; + + /* CPU pointer getters */ + /** @brief Returns pointer to the blob element with the specified position, stored in CPU memory. + * + * @p n correspond to the first axis, @p cn - to the second, etc. + * If dims() > 4 then unspecified coordinates will be filled by zeros. + * If dims() < 4 then extra coordinates will be ignored. + */ + uchar *ptr(int n = 0, int cn = 0, int row = 0, int col = 0); + /** @overload */ + template + Type *ptr(int n = 0, int cn = 0, int row = 0, int col = 0); + /** @overload ptr() */ + float *ptrf(int n = 0, int cn = 0, int row = 0, int col = 0); + //TODO: add const ptr methods + + /** @brief Shares data from other @p blob. + * @returns *this + */ + Blob &shareFrom(const Blob &blob); + + /** @brief Changes shape of the blob without copying the data. + * @returns *this + */ + Blob &reshape(const BlobShape &shape); + + /** @brief Changes shape of the blob without copying the data. + * @returns shallow copy of original blob with new shape. + */ + Blob reshaped(const BlobShape &newShape) const; + + int type() const; //!< Returns type of the blob. + int elemSize() const; //!< Returns size of single element in bytes. + int getState() const; //!< Returns current state of the blob, @see DataState. + + private: + const int *sizes() const; + +# define CV_DNN_UMAT //DBG +#ifdef HAVE_OPENCL +# define CV_DNN_UMAT +#endif + +#ifdef CV_DNN_UMAT +# define CV_DNN_UMAT_ONLY(expr) (expr) +#else +# define CV_DNN_UMAT_ONLY(expr) +#endif + +#ifndef CV_DNN_UMAT + Mat m; +#else + mutable Mat m; + mutable UMat um; + mutable uchar state; +#endif + +public: + enum DataState + { + UNINITIALIZED = 0, + HEAD_AT_MAT = 1 << 0, + HEAD_AT_UMAT = 1 << 1, + SYNCED = HEAD_AT_MAT | HEAD_AT_UMAT + }; + + enum AllocFlag + { + ALLOC_MAT = HEAD_AT_MAT, + ALLOC_UMAT = HEAD_AT_UMAT, + ALLOC_BOTH = SYNCED + }; + }; + +//! @} +} +} + +#include "blob.inl.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv2/dnn/blob.inl.hpp b/thirdparty/linux/include/opencv2/dnn/blob.inl.hpp new file mode 100644 index 0000000..b7f741e --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/blob.inl.hpp @@ -0,0 +1,533 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_BLOB_INL_HPP__ +#define __OPENCV_DNN_DNN_BLOB_INL_HPP__ +#include "blob.hpp" + +namespace cv +{ +namespace dnn +{ + +inline BlobShape::BlobShape() +{ + sz.allocate(4); + for (size_t i = 0; i < sz.size(); i++) + sz[i] = 1; +} + +inline BlobShape BlobShape::all(int ndims, int fill) +{ + CV_Assert(ndims >= 0); + BlobShape res; + res.sz.allocate(ndims); + for (int i = 0; i < ndims; i++) + res.sz[i] = fill; + return res; +} + +inline BlobShape::BlobShape(int ndims, const int *sizes) : sz( (size_t)std::max(ndims, 0) ) +{ + CV_Assert(ndims >= 0); + if (!sizes) + return; + for (int i = 0; i < ndims; i++) + sz[i] = sizes[i]; +} + +inline BlobShape::BlobShape(int s0) : sz(1) +{ + sz[0] = s0; +} + +inline BlobShape::BlobShape(int s0, int s1) : sz(2) +{ + sz[0] = s0; + sz[1] = s1; +} + +inline BlobShape::BlobShape(int s0, int s1, int s2) : sz(3) +{ + sz[0] = s0; + sz[1] = s1; + sz[2] = s2; +} + +inline BlobShape::BlobShape(int num, int cn, int rows, int cols) : sz(4) +{ + sz[0] = num; + sz[1] = cn; + sz[2] = rows; + sz[3] = cols; +} + +inline BlobShape::BlobShape(const std::vector &sizes) : sz( sizes.size() ) +{ + for (int i = 0; i < (int)sizes.size(); i++) + sz[i] = sizes[i]; +} + +template +inline BlobShape::BlobShape(const Vec &shape) : sz(n) +{ + for (int i = 0; i < n; i++) + sz[i] = shape[i]; +} + +inline int BlobShape::dims() const +{ + return (int)sz.size(); +} + +inline int BlobShape::xsize(int axis) const +{ + if (axis < -dims() || axis >= dims()) + return 1; + + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int BlobShape::size(int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int &BlobShape::size(int axis) +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int BlobShape::operator[] (int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int &BlobShape::operator[] (int axis) +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sz[(axis < 0) ? axis + dims() : axis]; +} + +inline int BlobShape::canonicalAxis(int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return (axis < 0) ? axis + dims() : axis; +} + +inline ptrdiff_t BlobShape::total() const +{ + if (dims() == 0) + return 0; + + ptrdiff_t res = 1; + for (int i = 0; i < dims(); i++) + res *= sz[i]; + return res; +} + +inline ptrdiff_t BlobShape::total(int startAxis, int endAxis) const +{ + if (isEmpty()) + return 0; + + if (endAxis == INT_MAX) + endAxis = dims(); + else if (endAxis < 0) + endAxis += dims(); + startAxis = (startAxis < 0) ? startAxis + dims() : startAxis; + CV_Assert(0 <= startAxis && startAxis <= endAxis && endAxis <= dims()); + + ptrdiff_t res = 1; + for (int i = startAxis; i < endAxis; i++) + res *= sz[i]; + return res; +} + +inline BlobShape BlobShape::slice(int startAxis, int endAxis) const +{ + if (isEmpty()) + return BlobShape::empty(); + + if (endAxis == INT_MAX) + endAxis = dims(); + else if (endAxis < 0) + endAxis += dims(); + startAxis = (startAxis < 0) ? startAxis + dims() : startAxis; + CV_Assert(0 <= startAxis && startAxis <= endAxis && endAxis <= dims()); + + BlobShape res(endAxis - startAxis, (const int*)NULL); + for (int i = startAxis; i < endAxis; i++) + res[i - startAxis] = sz[i]; + return res; +} + +inline const int *BlobShape::ptr() const +{ + return sz; +} + +inline int *BlobShape::ptr() +{ + return sz; +} + +inline bool BlobShape::equal(const BlobShape &other) const +{ + if (this->dims() != other.dims()) + return false; + + for (int i = 0; i < other.dims(); i++) + { + if (sz[i] != other.sz[i]) + return false; + } + + return true; +} + +inline bool BlobShape::operator==(const BlobShape &r) const +{ + return this->equal(r); +} + +inline BlobShape BlobShape::like(const Mat &m) +{ + return BlobShape(m.dims, (const int*)m.size); +} + +inline BlobShape BlobShape::like(const UMat &m) +{ + return BlobShape(m.dims, (const int*)m.size); +} + +inline BlobShape BlobShape::empty() +{ + return BlobShape(0, (const int*)NULL); +} + +inline bool BlobShape::isEmpty() const +{ + return dims() == 0; +} + +inline BlobShape BlobShape::operator+(const BlobShape &r) const +{ + BlobShape newShape(this->dims() + r.dims(), (int*)NULL); + for (int i = 0; i < this->dims(); i++) + newShape[i] = (*this)[i]; + for (int i = 0; i < r.dims(); i++) + newShape[this->dims() + i] = r[i]; + return newShape; +} + +CV_EXPORTS std::ostream &operator<< (std::ostream &stream, const BlobShape &shape); + +///////////////////////////////////////////////////////////////////// + +#ifndef CV_DNN_UMAT +# define CV_DNN_SWITCH_MU(cpu_expr, gpu_expr) (cpu_expr) +#else +# define CV_DNN_SWITCH_MU(cpu_expr, gpu_expr) ((state == HEAD_AT_UMAT) ? (gpu_expr) : (cpu_expr)) +#endif + + +inline int Blob::dims() const +{ + return CV_DNN_SWITCH_MU(m.dims, um.dims); +} + +inline const int * Blob::sizes() const +{ + return CV_DNN_SWITCH_MU((const int*)m.size, (const int*)um.size); +} + +inline int Blob::type() const +{ + return CV_DNN_SWITCH_MU(m.type(), um.type()); +} + +template +inline size_t Blob::offset(const Vec &pos) const +{ + const MatStep &step = CV_DNN_SWITCH_MU(m.step, um.step); + size_t ofs = 0; + int i; + for (i = 0; i < std::min(n, dims()); i++) + { + CV_DbgAssert(pos[i] >= 0 && pos[i] < size(i)); + ofs += step[i] * pos[i]; + } + for (; i < dims(); i++) + CV_DbgAssert(pos[i] == 0); + CV_DbgAssert(ofs % elemSize() == 0); + return ofs / elemSize(); +} + +inline int Blob::canonicalAxis(int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return (axis < 0) ? axis + dims() : axis; +} + +inline int Blob::xsize(int axis) const +{ + if (axis < -dims() || axis >= dims()) + return 1; + + return sizes()[(axis < 0) ? axis + dims() : axis]; +} + +inline int Blob::size(int axis) const +{ + CV_Assert(-dims() <= axis && axis < dims()); + return sizes()[(axis < 0) ? axis + dims() : axis]; +} + +inline size_t Blob::total(int startAxis, int endAxis) const +{ + if (startAxis < 0) + startAxis += dims(); + + if (endAxis == INT_MAX) + endAxis = dims(); + else if (endAxis < 0) + endAxis += dims(); + + CV_Assert(0 <= startAxis && startAxis <= endAxis && endAxis <= dims()); + + size_t cnt = 1; //fix: assume that slice isn't empty + for (int i = startAxis; i < endAxis; i++) + cnt *= (size_t)sizes()[i]; + + return cnt; +} + +inline size_t Blob::offset(int n, int cn, int row, int col) const +{ + return offset(Vec4i(n, cn, row, col)); +} + +inline float *Blob::ptrf(int n, int cn, int row, int col) +{ + return matRef(false).ptr() + offset(n, cn, row, col); +} + +inline uchar *Blob::ptr(int n, int cn, int row, int col) +{ + Mat &mat = matRef(false); + return mat.ptr() + mat.elemSize() * offset(n, cn, row, col); +} + +template +inline Dtype* Blob::ptr(int n, int cn, int row, int col) +{ + CV_Assert(type() == cv::DataDepth::value); + return (Dtype*) ptr(n, cn, row, col); +} + +inline BlobShape Blob::shape() const +{ + return BlobShape(dims(), sizes()); +} + +inline bool Blob::equalShape(const Blob &other) const +{ + if (this->dims() != other.dims()) + return false; + + for (int i = 0; i < dims(); i++) + { + if (this->sizes()[i] != other.sizes()[i]) + return false; + } + return true; +} + +inline Mat& Blob::matRef(bool writeOnly) +{ +#ifdef CV_DNN_UMAT + updateMat(!writeOnly); + state = HEAD_AT_MAT; +#else + (void)writeOnly; +#endif + return m; +} + +inline const Mat& Blob::matRefConst() const +{ + CV_DNN_UMAT_ONLY( updateMat() ); + return m; +} + +inline UMat &Blob::umatRef(bool writeOnly) +{ +#ifndef CV_DNN_UMAT + CV_Error(Error::GpuNotSupported, ""); + (void)writeOnly; + return *(new UMat()); +#else + updateUMat(!writeOnly); + state = HEAD_AT_UMAT; + return um; +#endif +} + +inline const UMat &Blob::umatRefConst() const +{ +#ifndef CV_DNN_UMAT + CV_Error(Error::GpuNotSupported, ""); + return *(new UMat()); +#else + updateUMat(); + return um; +#endif +} + +template<> +inline Mat &Blob::getRef(bool writeOnly) +{ + return matRef(writeOnly); +} + +template<> +inline UMat &Blob::getRef(bool writeOnly) +{ + return umatRef(writeOnly); +} + +template<> +inline const Mat &Blob::getRefConst() const +{ + return matRefConst(); +} + +template<> +inline const UMat &Blob::getRefConst() const +{ + return umatRefConst(); +} + +inline Mat Blob::getPlane(int n, int cn) +{ + CV_Assert(dims() > 2); + return Mat(dims() - 2, sizes() + 2, type(), ptr(n, cn)); +} + +inline Mat Blob::getPlanes(int n) +{ + CV_Assert(dims() > 3); + return Mat(dims() - 1, sizes() + 1, type(), ptr(n)); +} + +inline int Blob::cols() const +{ + return xsize(3); +} + +inline int Blob::rows() const +{ + return xsize(2); +} + +inline int Blob::channels() const +{ + return xsize(1); +} + +inline int Blob::num() const +{ + return xsize(0); +} + +inline Size Blob::size2() const +{ + return Size(cols(), rows()); +} + +inline Blob &Blob::shareFrom(const Blob &blob) +{ + this->m = blob.m; +#ifdef CV_DNN_UMAT + this->um = blob.um; + this->state = blob.state; +#endif + return *this; +} + +inline Blob &Blob::reshape(const BlobShape &newShape) +{ + if (!m.empty()) m = m.reshape(1, newShape.dims(), newShape.ptr()); +#ifdef CV_DNN_UMAT + if (!um.empty()) um = um.reshape(1, newShape.dims(), newShape.ptr()); +#endif + return *this; +} + +inline Blob Blob::reshaped(const BlobShape &newShape) const +{ + Blob res(*this); //also, res.shareFrom(*this) could be used + res.reshape(newShape); + return res; +} + +inline int Blob::elemSize() const +{ + return CV_ELEM_SIZE(type()); +} + +inline int Blob::getState() const +{ +#ifdef CV_DNN_UMAT + return this->state; +#else + return m.empty() ? UNINITIALIZED : HEAD_AT_MAT; +#endif +} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/dnn/dict.hpp b/thirdparty/linux/include/opencv2/dnn/dict.hpp new file mode 100644 index 0000000..f7cd0f2 --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/dict.hpp @@ -0,0 +1,143 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_DICT_HPP__ +#define __OPENCV_DNN_DNN_DICT_HPP__ + +#include +#include +#include + +namespace cv +{ +namespace dnn +{ +//! @addtogroup dnn +//! @{ + +/** @brief This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. + * @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers. + */ +struct DictValue +{ + DictValue(const DictValue &r); + DictValue(int64 i = 0) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i; } //!< Constructs integer scalar + DictValue(int i) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i; } //!< Constructs integer scalar + DictValue(unsigned p) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = p; } //!< Constructs integer scalar + DictValue(double p) : type(Param::REAL), pd(new AutoBuffer) { (*pd)[0] = p; } //!< Constructs floating point scalar + DictValue(const String &s) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = s; } //!< Constructs string scalar + DictValue(const char *s) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = s; } //!< @overload + + template + static DictValue arrayInt(TypeIter begin, int size); //!< Constructs integer array + template + static DictValue arrayReal(TypeIter begin, int size); //!< Constructs floating point array + template + static DictValue arrayString(TypeIter begin, int size); //!< Constructs array of strings + + template + T get(int idx = -1) const; //!< Tries to convert array element with specified index to requested type and returns its. + + int size() const; + + bool isInt() const; + bool isString() const; + bool isReal() const; + + DictValue &operator=(const DictValue &r); + + friend std::ostream &operator<<(std::ostream &stream, const DictValue &dictv); + + ~DictValue(); + +private: + + int type; + + union + { + AutoBuffer *pi; + AutoBuffer *pd; + AutoBuffer *ps; + void *pv; + }; + + DictValue(int _type, void *_p) : type(_type), pv(_p) {} + void release(); +}; + +/** @brief This class implements name-value dictionary, values are instances of DictValue. */ +class CV_EXPORTS Dict +{ + typedef std::map _Dict; + _Dict dict; + +public: + + //! Checks a presence of the @p key in the dictionary. + bool has(const String &key) const; + + //! If the @p key in the dictionary then returns pointer to its value, else returns NULL. + DictValue *ptr(const String &key); + + //! If the @p key in the dictionary then returns its value, else an error will be generated. + const DictValue &get(const String &key) const; + + /** @overload */ + template + T get(const String &key) const; + + //! If the @p key in the dictionary then returns its value, else returns @p defaultValue. + template + T get(const String &key, const T &defaultValue) const; + + //! Sets new @p value for the @p key, or adds new key-value pair into the dictionary. + template + const T &set(const String &key, const T &value); + + friend std::ostream &operator<<(std::ostream &stream, const Dict &dict); +}; + +//! @} +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/dnn/dnn.hpp b/thirdparty/linux/include/opencv2/dnn/dnn.hpp new file mode 100644 index 0000000..41d975b --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/dnn.hpp @@ -0,0 +1,350 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_HPP__ +#define __OPENCV_DNN_DNN_HPP__ + +#include +#include +#include +#include + +namespace cv +{ +namespace dnn //! This namespace is used for dnn module functionlaity. +{ +//! @addtogroup dnn +//! @{ + + /** @brief Initialize dnn module and built-in layers. + * + * This function automatically called on most of OpenCV builds, + * but you need to call it manually on some specific configurations (iOS for example). + */ + CV_EXPORTS_W void initModule(); + + /** @brief This class provides all data needed to initialize layer. + * + * It includes dictionary with scalar params (which can be readed by using Dict interface), + * blob params #blobs and optional meta information: #name and #type of layer instance. + */ + class CV_EXPORTS LayerParams : public Dict + { + public: + //TODO: Add ability to name blob params + std::vector blobs; //!< List of learned parameters stored as blobs. + + String name; //!< Name of the layer instance (optional, can be used internal purposes). + String type; //!< Type name which was used for creating layer by layer factory (optional). + }; + + /** @brief This interface class allows to build new Layers - are building blocks of networks. + * + * Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs. + * Also before using the new layer into networks you must register your layer by using one of @ref dnnLayerFactory "LayerFactory" macros. + */ + class CV_EXPORTS_W Layer + { + public: + + //! List of learned parameters must be stored here to allow read them by using Net::getParam(). + CV_PROP_RW std::vector blobs; + + /** @brief Allocates internal buffers and output blobs with respect to the shape of inputs. + * @param[in] input vector of already allocated input blobs + * @param[out] output vector of output blobs, which must be allocated + * + * This method must create each produced blob according to shape of @p input blobs and internal layer params. + * If this method is called first time then @p output vector consists from empty blobs and its size determined by number of output connections. + * This method can be called multiple times if size of any @p input blob was changed. + */ + virtual void allocate(const std::vector &input, std::vector &output) = 0; + + /** @brief Given the @p input blobs, computes the output @p blobs. + * @param[in] input the input blobs. + * @param[out] output allocated output blobs, which will store results of the computation. + */ + virtual void forward(std::vector &input, std::vector &output) = 0; + + /** @brief @overload */ + CV_WRAP void allocate(const std::vector &inputs, CV_OUT std::vector &outputs); + + /** @brief @overload */ + CV_WRAP std::vector allocate(const std::vector &inputs); + + /** @brief @overload */ + CV_WRAP void forward(const std::vector &inputs, CV_IN_OUT std::vector &outputs); + + /** @brief Allocates layer and computes output. */ + CV_WRAP void run(const std::vector &inputs, CV_OUT std::vector &outputs); + + /** @brief Returns index of input blob into the input array. + * @param inputName label of input blob + * + * Each layer input and output can be labeled to easily identify them using "%[.output_name]" notation. + * This method maps label of input blob to its index into input vector. + */ + virtual int inputNameToIndex(String inputName); + /** @brief Returns index of output blob in output array. + * @see inputNameToIndex() + */ + virtual int outputNameToIndex(String outputName); + + CV_PROP String name; //!< Name of the layer instance, can be used for logging or other internal purposes. + CV_PROP String type; //!< Type name which was used for creating layer by layer factory. + + Layer(); + explicit Layer(const LayerParams ¶ms); //!< Initializes only #name, #type and #blobs fields. + void setParamsFrom(const LayerParams ¶ms); //!< Initializes only #name, #type and #blobs fields. + virtual ~Layer(); + }; + + /** @brief This class allows to create and manipulate comprehensive artificial neural networks. + * + * Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances, + * and edges specify relationships between layers inputs and outputs. + * + * Each network layer has unique integer id and unique string name inside its network. + * LayerId can store either layer name or layer id. + * + * This class supports reference counting of its instances, i. e. copies point to the same instance. + */ + class CV_EXPORTS_W_SIMPLE Net + { + public: + + CV_WRAP Net(); //!< Default constructor. + CV_WRAP ~Net(); //!< Destructor frees the net only if there aren't references to the net anymore. + + /** Returns true if there are no layers in the network. */ + CV_WRAP bool empty() const; + + /** @brief Adds new layer to the net. + * @param name unique name of the adding layer. + * @param type typename of the adding layer (type must be registered in LayerRegister). + * @param params parameters which will be used to initialize the creating layer. + * @returns unique identifier of created layer, or -1 if a failure will happen. + */ + int addLayer(const String &name, const String &type, LayerParams ¶ms); + /** @brief Adds new layer and connects its first input to the first output of previously added layer. + * @see addLayer() + */ + int addLayerToPrev(const String &name, const String &type, LayerParams ¶ms); + + /** @brief Converts string name of the layer to the integer identifier. + * @returns id of the layer, or -1 if the layer wasn't found. + */ + CV_WRAP int getLayerId(const String &layer); + + CV_WRAP std::vector getLayerNames() const; + + /** @brief Container for strings and integers. */ + typedef DictValue LayerId; + + /** @brief Returns pointer to layer with specified name which the network use. */ + CV_WRAP Ptr getLayer(LayerId layerId); + + /** @brief Delete layer for the network (not implemented yet) */ + CV_WRAP void deleteLayer(LayerId layer); + + /** @brief Connects output of the first layer to input of the second layer. + * @param outPin descriptor of the first layer output. + * @param inpPin descriptor of the second layer input. + * + * Descriptors have the following template <layer_name>[.input_number]: + * - the first part of the template layer_name is sting name of the added layer. + * If this part is empty then the network input pseudo layer will be used; + * - the second optional part of the template input_number + * is either number of the layer input, either label one. + * If this part is omitted then the first layer input will be used. + * + * @see setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex() + */ + CV_WRAP void connect(String outPin, String inpPin); + + /** @brief Connects #@p outNum output of the first layer to #@p inNum input of the second layer. + * @param outLayerId identifier of the first layer + * @param inpLayerId identifier of the second layer + * @param outNum number of the first layer output + * @param inpNum number of the second layer input + */ + void connect(int outLayerId, int outNum, int inpLayerId, int inpNum); + + /** @brief Sets outputs names of the network input pseudo layer. + * + * Each net always has special own the network input pseudo layer with id=0. + * This layer stores the user blobs only and don't make any computations. + * In fact, this layer provides the only way to pass user data into the network. + * As any other layer, this layer can label its outputs and this function provides an easy way to do this. + */ + CV_WRAP void setNetInputs(const std::vector &inputBlobNames); + + /** @brief Initializes and allocates all layers. */ + CV_WRAP void allocate(); + + /** @brief Runs forward pass to compute output of layer @p toLayer. + * @details By default runs forward pass for the whole network. + */ + CV_WRAP void forward(LayerId toLayer = String()); + /** @brief Runs forward pass to compute output of layer @p toLayer, but computations start from @p startLayer */ + void forward(LayerId startLayer, LayerId toLayer); + /** @overload */ + void forward(const std::vector &startLayers, const std::vector &toLayers); + + //TODO: + /** @brief Optimized forward. + * @warning Not implemented yet. + * @details Makes forward only those layers which weren't changed after previous forward(). + */ + void forwardOpt(LayerId toLayer); + /** @overload */ + void forwardOpt(const std::vector &toLayers); + + /** @brief Sets the new value for the layer output blob + * @param outputName descriptor of the updating layer output blob. + * @param blob new blob. + * @see connect(String, String) to know format of the descriptor. + * @note If updating blob is not empty then @p blob must have the same shape, + * because network reshaping is not implemented yet. + */ + CV_WRAP void setBlob(String outputName, const Blob &blob); + + /** @brief Returns the layer output blob. + * @param outputName the descriptor of the returning layer output blob. + * @see connect(String, String) + */ + CV_WRAP Blob getBlob(String outputName); + + /** @brief Sets the new value for the learned param of the layer. + * @param layer name or id of the layer. + * @param numParam index of the layer parameter in the Layer::blobs array. + * @param blob the new value. + * @see Layer::blobs + * @note If shape of the new blob differs from the previous shape, + * then the following forward pass may fail. + */ + CV_WRAP void setParam(LayerId layer, int numParam, const Blob &blob); + + /** @brief Returns parameter blob of the layer. + * @param layer name or id of the layer. + * @param numParam index of the layer parameter in the Layer::blobs array. + * @see Layer::blobs + */ + CV_WRAP Blob getParam(LayerId layer, int numParam = 0); + + /** @brief Returns indexes of layers with unconnected outputs. + */ + CV_WRAP std::vector getUnconnectedOutLayers() const; + private: + + struct Impl; + Ptr impl; + }; + + /** @brief Small interface class for loading trained serialized models of different dnn-frameworks. */ + class CV_EXPORTS_W Importer + { + public: + + /** @brief Adds loaded layers into the @p net and sets connections between them. */ + CV_WRAP virtual void populateNet(Net net) = 0; + + virtual ~Importer(); + }; + + /** @brief Creates the importer of Caffe framework network. + * @param prototxt path to the .prototxt file with text description of the network architecture. + * @param caffeModel path to the .caffemodel file with learned network. + * @returns Pointer to the created importer, NULL in failure cases. + */ + CV_EXPORTS_W Ptr createCaffeImporter(const String &prototxt, const String &caffeModel = String()); + + /** @brief Reads a network model stored in Caffe model files. + * @details This is shortcut consisting from createCaffeImporter and Net::populateNet calls. + */ + CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String()); + + /** @brief Creates the importer of TensorFlow framework network. + * @param model path to the .pb file with binary protobuf description of the network architecture. + * @returns Pointer to the created importer, NULL in failure cases. + */ + CV_EXPORTS Ptr createTensorflowImporter(const String &model); + + /** @brief Creates the importer of Torch7 framework network. + * @param filename path to the file, dumped from Torch by using torch.save() function. + * @param isBinary specifies whether the network was serialized in ascii mode or binary. + * @returns Pointer to the created importer, NULL in failure cases. + * + * @warning Torch7 importer is experimental now, you need explicitly set CMake `opencv_dnn_BUILD_TORCH_IMPORTER` flag to compile its. + * + * @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language, + * which has various bit-length on different systems. + * + * The loading file must contain serialized nn.Module object + * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors. + * + * List of supported layers (i.e. object instances derived from Torch nn.Module class): + * - nn.Sequential + * - nn.Parallel + * - nn.Concat + * - nn.Linear + * - nn.SpatialConvolution + * - nn.SpatialMaxPooling, nn.SpatialAveragePooling + * - nn.ReLU, nn.TanH, nn.Sigmoid + * - nn.Reshape + * + * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported. + */ + CV_EXPORTS_W Ptr createTorchImporter(const String &filename, bool isBinary = true); + + /** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework. + * @warning This function has the same limitations as createTorchImporter(). + */ + CV_EXPORTS_W Blob readTorchBlob(const String &filename, bool isBinary = true); + +//! @} +} +} + +#include +#include + +#endif /* __OPENCV_DNN_DNN_HPP__ */ diff --git a/thirdparty/linux/include/opencv2/dnn/dnn.inl.hpp b/thirdparty/linux/include/opencv2/dnn/dnn.inl.hpp new file mode 100644 index 0000000..a272044 --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/dnn.inl.hpp @@ -0,0 +1,351 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_INL_HPP__ +#define __OPENCV_DNN_DNN_INL_HPP__ + +#include + +namespace cv +{ +namespace dnn +{ + +template +DictValue DictValue::arrayInt(TypeIter begin, int size) +{ + DictValue res(Param::INT, new AutoBuffer(size)); + for (int j = 0; j < size; begin++, j++) + (*res.pi)[j] = *begin; + return res; +} + +template +DictValue DictValue::arrayReal(TypeIter begin, int size) +{ + DictValue res(Param::REAL, new AutoBuffer(size)); + for (int j = 0; j < size; begin++, j++) + (*res.pd)[j] = *begin; + return res; +} + +template +DictValue DictValue::arrayString(TypeIter begin, int size) +{ + DictValue res(Param::STRING, new AutoBuffer(size)); + for (int j = 0; j < size; begin++, j++) + (*res.ps)[j] = *begin; + return res; +} + +template<> +inline DictValue DictValue::get(int idx) const +{ + CV_Assert(idx == -1); + return *this; +} + +template<> +inline int64 DictValue::get(int idx) const +{ + CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size())); + idx = (idx == -1) ? 0 : idx; + + if (type == Param::INT) + { + return (*pi)[idx]; + } + else if (type == Param::REAL) + { + double doubleValue = (*pd)[idx]; + + double fracpart, intpart; + fracpart = std::modf(doubleValue, &intpart); + CV_Assert(fracpart == 0.0); + + return (int64)doubleValue; + } + else + { + CV_Assert(isInt() || isReal()); + return 0; + } +} + +template<> +inline int DictValue::get(int idx) const +{ + return (int)get(idx); +} + +template<> +inline unsigned DictValue::get(int idx) const +{ + return (unsigned)get(idx); +} + +template<> +inline bool DictValue::get(int idx) const +{ + return (get(idx) != 0); +} + +template<> +inline double DictValue::get(int idx) const +{ + CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size())); + idx = (idx == -1) ? 0 : idx; + + if (type == Param::REAL) + { + return (*pd)[idx]; + } + else if (type == Param::INT) + { + return (double)(*pi)[idx]; + } + else + { + CV_Assert(isReal() || isInt()); + return 0; + } +} + +template<> +inline float DictValue::get(int idx) const +{ + return (float)get(idx); +} + +template<> +inline String DictValue::get(int idx) const +{ + CV_Assert(isString()); + CV_Assert((idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size())); + return (*ps)[(idx == -1) ? 0 : idx]; +} + +inline void DictValue::release() +{ + switch (type) + { + case Param::INT: + delete pi; + break; + case Param::STRING: + delete ps; + break; + case Param::REAL: + delete pd; + break; + } +} + +inline DictValue::~DictValue() +{ + release(); +} + +inline DictValue & DictValue::operator=(const DictValue &r) +{ + if (&r == this) + return *this; + + if (r.type == Param::INT) + { + AutoBuffer *tmp = new AutoBuffer(*r.pi); + release(); + pi = tmp; + } + else if (r.type == Param::STRING) + { + AutoBuffer *tmp = new AutoBuffer(*r.ps); + release(); + ps = tmp; + } + else if (r.type == Param::REAL) + { + AutoBuffer *tmp = new AutoBuffer(*r.pd); + release(); + pd = tmp; + } + + type = r.type; + + return *this; +} + +inline DictValue::DictValue(const DictValue &r) +{ + type = r.type; + + if (r.type == Param::INT) + pi = new AutoBuffer(*r.pi); + else if (r.type == Param::STRING) + ps = new AutoBuffer(*r.ps); + else if (r.type == Param::REAL) + pd = new AutoBuffer(*r.pd); +} + +inline bool DictValue::isString() const +{ + return (type == Param::STRING); +} + +inline bool DictValue::isInt() const +{ + return (type == Param::INT); +} + +inline bool DictValue::isReal() const +{ + return (type == Param::REAL || type == Param::INT); +} + +inline int DictValue::size() const +{ + switch (type) + { + case Param::INT: + return (int)pi->size(); + break; + case Param::STRING: + return (int)ps->size(); + break; + case Param::REAL: + return (int)pd->size(); + break; + default: + CV_Error(Error::StsInternal, ""); + return -1; + } +} + +inline std::ostream &operator<<(std::ostream &stream, const DictValue &dictv) +{ + int i; + + if (dictv.isInt()) + { + for (i = 0; i < dictv.size() - 1; i++) + stream << dictv.get(i) << ", "; + stream << dictv.get(i); + } + else if (dictv.isReal()) + { + for (i = 0; i < dictv.size() - 1; i++) + stream << dictv.get(i) << ", "; + stream << dictv.get(i); + } + else if (dictv.isString()) + { + for (i = 0; i < dictv.size() - 1; i++) + stream << "\"" << dictv.get(i) << "\", "; + stream << dictv.get(i); + } + + return stream; +} + +///////////////////////////////////////////////////////////////// + +inline bool Dict::has(const String &key) const +{ + return dict.count(key) != 0; +} + +inline DictValue *Dict::ptr(const String &key) +{ + _Dict::iterator i = dict.find(key); + return (i == dict.end()) ? NULL : &i->second; +} + +inline const DictValue &Dict::get(const String &key) const +{ + _Dict::const_iterator i = dict.find(key); + if (i == dict.end()) + CV_Error(Error::StsObjectNotFound, "Required argument \"" + key + "\" not found into dictionary"); + return i->second; +} + +template +inline T Dict::get(const String &key) const +{ + return this->get(key).get(); +} + +template +inline T Dict::get(const String &key, const T &defaultValue) const +{ + _Dict::const_iterator i = dict.find(key); + + if (i != dict.end()) + return i->second.get(); + else + return defaultValue; +} + +template +inline const T &Dict::set(const String &key, const T &value) +{ + _Dict::iterator i = dict.find(key); + + if (i != dict.end()) + i->second = DictValue(value); + else + dict.insert(std::make_pair(key, DictValue(value))); + + return value; +} + +inline std::ostream &operator<<(std::ostream &stream, const Dict &dict) +{ + Dict::_Dict::const_iterator it; + for (it = dict.dict.begin(); it != dict.dict.end(); it++) + stream << it->first << " : " << it->second << "\n"; + + return stream; +} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/dnn/layer.hpp b/thirdparty/linux/include/opencv2/dnn/layer.hpp new file mode 100644 index 0000000..e051041 --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/layer.hpp @@ -0,0 +1,148 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_LAYER_HPP__ +#define __OPENCV_DNN_LAYER_HPP__ +#include + +namespace cv +{ +namespace dnn +{ +//! @addtogroup dnn +//! @{ +//! +//! @defgroup dnnLayerFactory Utilities for New Layers Registration +//! @{ + +/** @brief %Layer factory allows to create instances of registered layers. */ +class CV_EXPORTS LayerFactory +{ +public: + + //! Each Layer class must provide this function to the factory + typedef Ptr(*Constuctor)(LayerParams ¶ms); + + //! Registers the layer class with typename @p type and specified @p constructor. + static void registerLayer(const String &type, Constuctor constructor); + + //! Unregisters registered layer with specified type name. + static void unregisterLayer(const String &type); + + /** @brief Creates instance of registered layer. + * @param type type name of creating layer. + * @param params parameters which will be used for layer initialization. + */ + static Ptr createLayerInstance(const String &type, LayerParams& params); + +private: + LayerFactory(); + + struct Impl; + static Ptr impl(); +}; + +/** @brief Registers layer constructor in runtime. +* @param type string, containing type name of the layer. +* @param constuctorFunc pointer to the function of type LayerRegister::Constuctor, which creates the layer. +* @details This macros must be placed inside the function code. +*/ +#define REG_RUNTIME_LAYER_FUNC(type, constuctorFunc) \ + cv::dnn::LayerFactory::registerLayer(#type, constuctorFunc); + +/** @brief Registers layer class in runtime. + * @param type string, containing type name of the layer. + * @param class C++ class, derived from Layer. + * @details This macros must be placed inside the function code. + */ +#define REG_RUNTIME_LAYER_CLASS(type, class) \ + cv::dnn::LayerFactory::registerLayer(#type, _layerDynamicRegisterer); + +/** @brief Registers layer constructor on module load time. +* @param type string, containing type name of the layer. +* @param constuctorFunc pointer to the function of type LayerRegister::Constuctor, which creates the layer. +* @details This macros must be placed outside the function code. +*/ +#define REG_STATIC_LAYER_FUNC(type, constuctorFunc) \ +static cv::dnn::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, constuctorFunc); + +/** @brief Registers layer class on module load time. + * @param type string, containing type name of the layer. + * @param class C++ class, derived from Layer. + * @details This macros must be placed outside the function code. + */ +#define REG_STATIC_LAYER_CLASS(type, class) \ +Ptr __LayerStaticRegisterer_func_##type(LayerParams ¶ms) \ + { return Ptr(new class(params)); } \ +static _LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, __LayerStaticRegisterer_func_##type); + + +//! @} +//! @} + + +template +Ptr _layerDynamicRegisterer(LayerParams ¶ms) +{ + return Ptr(new LayerClass(params)); +} + +//allows automatically register created layer on module load time +class _LayerStaticRegisterer +{ + String type; +public: + + _LayerStaticRegisterer(const String &layerType, LayerFactory::Constuctor layerConstuctor) + { + this->type = layerType; + LayerFactory::registerLayer(layerType, layerConstuctor); + } + + ~_LayerStaticRegisterer() + { + LayerFactory::unregisterLayer(type); + } +}; + +} +} +#endif diff --git a/thirdparty/linux/include/opencv2/dnn/shape_utils.hpp b/thirdparty/linux/include/opencv2/dnn/shape_utils.hpp new file mode 100644 index 0000000..f52e5b9 --- /dev/null +++ b/thirdparty/linux/include/opencv2/dnn/shape_utils.hpp @@ -0,0 +1,137 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_SHAPE_UTILS_HPP__ +#define __OPENCV_DNN_DNN_SHAPE_UTILS_HPP__ + +#include +#include + +namespace cv { +namespace dnn { + +//Useful shortcut +typedef BlobShape Shape; + +inline std::ostream &operator<< (std::ostream &s, cv::Range &r) +{ + return s << "[" << r.start << ", " << r.end << ")"; +} + +//Reshaping +//TODO: add -1 specifier for automatic size inferring + +template +void reshape(Mat &m, const BlobShape &shape) +{ + m = m.reshape(1, shape.dims(), shape.ptr()); +} + +template +Mat reshaped(const Mat &m, const BlobShape &shape) +{ + return m.reshape(1, shape.dims(), shape.ptr()); +} + + +//Slicing + +struct _Range : public cv::Range +{ + _Range(const Range &r) : cv::Range(r) {} + _Range(int start, int size = 1) : cv::Range(start, start + size) {} +}; + +template +Mat slice(const Mat &m, const _Range &r0) +{ + //CV_Assert(m.dims >= 1); + cv::AutoBuffer ranges(m.dims); + for (int i = 1; i < m.dims; i++) + ranges[i] = Range::all(); + ranges[0] = r0; + return m(&ranges[0]); +} + +template +Mat slice(const Mat &m, const _Range &r0, const _Range &r1) +{ + CV_Assert(m.dims >= 2); + cv::AutoBuffer ranges(m.dims); + for (int i = 2; i < m.dims; i++) + ranges[i] = Range::all(); + ranges[0] = r0; + ranges[1] = r1; + return m(&ranges[0]); +} + +template +Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2) +{ + CV_Assert(m.dims <= 3); + cv::AutoBuffer ranges(m.dims); + for (int i = 3; i < m.dims; i++) + ranges[i] = Range::all(); + ranges[0] = r0; + ranges[1] = r1; + ranges[2] = r2; + return m(&ranges[0]); +} + +template +Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2, const _Range &r3) +{ + CV_Assert(m.dims <= 4); + cv::AutoBuffer ranges(m.dims); + for (int i = 4; i < m.dims; i++) + ranges[i] = Range::all(); + ranges[0] = r0; + ranges[1] = r1; + ranges[2] = r2; + ranges[3] = r3; + return m(&ranges[0]); +} + +BlobShape computeShapeByReshapeMask(const BlobShape &srcShape, const BlobShape &maskShape, Range srcRange = Range::all()); + +} +} +#endif diff --git a/thirdparty/linux/include/opencv2/dpm.hpp b/thirdparty/linux/include/opencv2/dpm.hpp new file mode 100644 index 0000000..387a311 --- /dev/null +++ b/thirdparty/linux/include/opencv2/dpm.hpp @@ -0,0 +1,148 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Itseez Inc or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +// Implementation authors: +// Jiaolong Xu - jiaolongxu@gmail.com +// Evgeniy Kozinov - evgeniy.kozinov@gmail.com +// Valentina Kustikova - valentina.kustikova@gmail.com +// Nikolai Zolotykh - Nikolai.Zolotykh@gmail.com +// Iosif Meyerov - meerov@vmk.unn.ru +// Alexey Polovinkin - polovinkin.alexey@gmail.com +// +//M*/ + +#ifndef __OPENCV_LATENTSVM_HPP__ +#define __OPENCV_LATENTSVM_HPP__ + +#include "opencv2/core.hpp" + +#include +#include +#include + +/** @defgroup dpm Deformable Part-based Models + +Discriminatively Trained Part Based Models for Object Detection +--------------------------------------------------------------- + +The object detector described below has been initially proposed by P.F. Felzenszwalb in +@cite Felzenszwalb2010a . It is based on a Dalal-Triggs detector that uses a single filter on histogram +of oriented gradients (HOG) features to represent an object category. This detector uses a sliding +window approach, where a filter is applied at all positions and scales of an image. The first +innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a +"root" filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated +deformation models. The score of one of star models at a particular position and scale within an +image is the score of the root filter at the given location plus the sum over parts of the maximum, +over placements of that part, of the part filter score on its location minus a deformation cost +easuring the deviation of the part from its ideal location relative to the root. Both root and part +filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of +a feature pyramid computed from the input image. Another improvement is a representation of the +class of models by a mixture of star models. The score of a mixture model at a particular position +and scale is the maximum over components, of the score of that component model at the given +location. + +The detector was dramatically speeded-up with cascade algorithm proposed by P.F. Felzenszwalb in +@cite Felzenszwalb2010b . The algorithm prunes partial hypotheses using thresholds on their scores.The +basic idea of the algorithm is to use a hierarchy of models defined by an ordering of the original +model's parts. For a model with (n+1) parts, including the root, a sequence of (n+1) models is +obtained. The i-th model in this sequence is defined by the first i parts from the original model. +Using this hierarchy, low scoring hypotheses can be pruned after looking at the best configuration +of a subset of the parts. Hypotheses that score high under a weak model are evaluated further using +a richer model. + +In OpenCV there is an C++ implementation of DPM cascade detector. + +*/ + +namespace cv +{ + +namespace dpm +{ + +/** @brief This is a C++ abstract class, it provides external user API to work with DPM. + */ +class CV_EXPORTS_W DPMDetector +{ +public: + + struct CV_EXPORTS_W ObjectDetection + { + ObjectDetection(); + ObjectDetection( const Rect& rect, float score, int classID=-1 ); + Rect rect; + float score; + int classID; + }; + + virtual bool isEmpty() const = 0; + + /** @brief Find rectangular regions in the given image that are likely to contain objects of loaded classes + (models) and corresponding confidence levels. + @param image An image. + @param objects The detections: rectangulars, scores and class IDs. + */ + virtual void detect(cv::Mat &image, CV_OUT std::vector &objects) = 0; + + /** @brief Return the class (model) names that were passed in constructor or method load or extracted from + models filenames in those methods. + */ + virtual std::vector const& getClassNames() const = 0; + + /** @brief Return a count of loaded models (classes). + */ + virtual size_t getClassCount() const = 0; + + /** @brief Load the trained models from given .xml files and return cv::Ptr\. + @param filenames A set of filenames storing the trained detectors (models). Each file contains one + model. See examples of such files here `/opencv_extra/testdata/cv/dpm/VOC2007_Cascade/`. + @param classNames A set of trained models names. If it's empty then the name of each model will be + constructed from the name of file containing the model. E.g. the model stored in + "/home/user/cat.xml" will get the name "cat". + */ + static cv::Ptr create(std::vector const &filenames, + std::vector const &classNames = std::vector()); + + virtual ~DPMDetector(){} +}; + +} // namespace dpm +} // namespace cv + +#endif diff --git a/thirdparty/linux/include/opencv2/face.hpp b/thirdparty/linux/include/opencv2/face.hpp new file mode 100644 index 0000000..a90a1da --- /dev/null +++ b/thirdparty/linux/include/opencv2/face.hpp @@ -0,0 +1,375 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_FACE_HPP__ +#define __OPENCV_FACE_HPP__ + +/** +@defgroup face Face Recognition + +- @ref face_changelog +- @ref tutorial_face_main + +*/ + +#include "opencv2/core.hpp" +#include "face/predict_collector.hpp" +#include + +namespace cv { namespace face { + +//! @addtogroup face +//! @{ + +/** @brief Abstract base class for all face recognition models + +All face recognition models in OpenCV are derived from the abstract base class FaceRecognizer, which +provides a unified access to all face recongition algorithms in OpenCV. + +### Description + +I'll go a bit more into detail explaining FaceRecognizer, because it doesn't look like a powerful +interface at first sight. But: Every FaceRecognizer is an Algorithm, so you can easily get/set all +model internals (if allowed by the implementation). Algorithm is a relatively new OpenCV concept, +which is available since the 2.4 release. I suggest you take a look at its description. + +Algorithm provides the following features for all derived classes: + +- So called “virtual constructorâ€. That is, each Algorithm derivative is registered at program + start and you can get the list of registered algorithms and create instance of a particular + algorithm by its name (see Algorithm::create). If you plan to add your own algorithms, it is + good practice to add a unique prefix to your algorithms to distinguish them from other + algorithms. +- Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from + OpenCV highgui module, you are probably familar with cv::cvSetCaptureProperty, +ocvcvGetCaptureProperty, VideoCapture::set and VideoCapture::get. Algorithm provides similar + method where instead of integer id's you specify the parameter names as text Strings. See + Algorithm::set and Algorithm::get for details. +- Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store + all its parameters and then read them back. There is no need to re-implement it each time. + +Moreover every FaceRecognizer supports the: + +- **Training** of a FaceRecognizer with FaceRecognizer::train on a given set of images (your face + database!). +- **Prediction** of a given sample image, that means a face. The image is given as a Mat. +- **Loading/Saving** the model state from/to a given XML or YAML. +- **Setting/Getting labels info**, that is stored as a string. String labels info is useful for + keeping names of the recognized people. + +@note When using the FaceRecognizer interface in combination with Python, please stick to Python 2. +Some underlying scripts like create_csv will not work in other versions, like Python 3. Setting the +Thresholds +++++++++++++++++++++++ + +Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common +scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is +unknown. You might wonder, why there's no public API in FaceRecognizer to set the threshold for the +prediction, but rest assured: It's supported. It just means there's no generic way in an abstract +class to provide an interface for setting/getting the thresholds of *every possible* FaceRecognizer +algorithm. The appropriate place to set the thresholds is in the constructor of the specific +FaceRecognizer and since every FaceRecognizer is a Algorithm (see above), you can get/set the +thresholds at runtime! + +Here is an example of setting a threshold for the Eigenfaces method, when creating the model: + +@code +// Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0 +int num_components = 10; +double threshold = 10.0; +// Then if you want to have a cv::FaceRecognizer with a confidence threshold, +// create the concrete implementation with the appropiate parameters: +Ptr model = createEigenFaceRecognizer(num_components, threshold); +@endcode + +Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to +Algorithm it's possible to set internal model thresholds during runtime. Let's see how we would +set/get the prediction for the Eigenface model, we've created above: + +@code +// The following line reads the threshold from the Eigenfaces model: +double current_threshold = model->getDouble("threshold"); +// And this line sets the threshold to 0.0: +model->set("threshold", 0.0); +@endcode + +If you've set the threshold to 0.0 as we did above, then: + +@code +// +Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); +// Get a prediction from the model. Note: We've set a threshold of 0.0 above, +// since the distance is almost always larger than 0.0, you'll get -1 as +// label, which indicates, this face is unknown +int predicted_label = model->predict(img); +// ... +@endcode + +is going to yield -1 as predicted label, which states this face is unknown. + +### Getting the name of a FaceRecognizer + +Since every FaceRecognizer is a Algorithm, you can use Algorithm::name to get the name of a +FaceRecognizer: + +@code +// Create a FaceRecognizer: +Ptr model = createEigenFaceRecognizer(); +// And here's how to get its name: +String name = model->name(); +@endcode + + */ +class CV_EXPORTS_W FaceRecognizer : public Algorithm +{ +public: + /** @brief Trains a FaceRecognizer with given data and associated labels. + + @param src The training images, that means the faces you want to learn. The data has to be + given as a vector\. + @param labels The labels corresponding to the images have to be given either as a vector\ + or a + + The following source code snippet shows you how to learn a Fisherfaces model on a given set of + images. The images are read with imread and pushed into a std::vector\. The labels of each + image are stored within a std::vector\ (you could also use a Mat of type CV_32SC1). Think of + the label as the subject (the person) this image belongs to, so same subjects (persons) should have + the same label. For the available FaceRecognizer you don't have to pay any attention to the order of + the labels, just make sure same persons have the same label: + + @code + // holds images and labels + vector images; + vector labels; + // images for first person + images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); + images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); + images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0); + // images for second person + images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); + images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); + images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1); + @endcode + + Now that you have read some images, we can create a new FaceRecognizer. In this example I'll create + a Fisherfaces model and decide to keep all of the possible Fisherfaces: + + @code + // Create a new Fisherfaces model and retain all available Fisherfaces, + // this is the most common usage of this specific FaceRecognizer: + // + Ptr model = createFisherFaceRecognizer(); + @endcode + + And finally train it on the given dataset (the face images and labels): + + @code + // This is the common interface to train all of the available cv::FaceRecognizer + // implementations: + // + model->train(images, labels); + @endcode + */ + CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0; + + /** @brief Updates a FaceRecognizer with given data and associated labels. + + @param src The training images, that means the faces you want to learn. The data has to be given + as a vector\. + @param labels The labels corresponding to the images have to be given either as a vector\ or + a + + This method updates a (probably trained) FaceRecognizer, but only if the algorithm supports it. The + Local Binary Patterns Histograms (LBPH) recognizer (see createLBPHFaceRecognizer) can be updated. + For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to + re-estimate the model with FaceRecognizer::train. In any case, a call to train empties the existing + model and learns a new model, while update does not delete any model data. + + @code + // Create a new LBPH model (it can be updated) and use the default parameters, + // this is the most common usage of this specific FaceRecognizer: + // + Ptr model = createLBPHFaceRecognizer(); + // This is the common interface to train all of the available cv::FaceRecognizer + // implementations: + // + model->train(images, labels); + // Some containers to hold new image: + vector newImages; + vector newLabels; + // You should add some images to the containers: + // + // ... + // + // Now updating the model is as easy as calling: + model->update(newImages,newLabels); + // This will preserve the old model data and extend the existing model + // with the new features extracted from newImages! + @endcode + + Calling update on an Eigenfaces model (see createEigenFaceRecognizer), which doesn't support + updating, will throw an error similar to: + + @code + OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305 + terminate called after throwing an instance of 'cv::Exception' + @endcode + + @note The FaceRecognizer does not store your training images, because this would be very + memory intense and it's not the responsibility of te FaceRecognizer to do so. The caller is + responsible for maintaining the dataset, he want to work with. + */ + CV_WRAP virtual void update(InputArrayOfArrays src, InputArray labels); + + /** @overload */ + CV_WRAP_AS(predict_label) int predict(InputArray src) const; + + + /** @brief Predicts a label and associated confidence (e.g. distance) for a given input image. + + @param src Sample image to get a prediction from. + @param label The predicted label for the given image. + @param confidence Associated confidence (e.g. distance) for the predicted label. + + The suffix const means that prediction does not affect the internal model state, so the method can + be safely called from within different threads. + + The following example shows how to get a prediction from a trained model: + + @code + using namespace cv; + // Do your initialization here (create the cv::FaceRecognizer model) ... + // ... + // Read in a sample image: + Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); + // And get a prediction from the cv::FaceRecognizer: + int predicted = model->predict(img); + @endcode + + Or to get a prediction and the associated confidence (e.g. distance): + + @code + using namespace cv; + // Do your initialization here (create the cv::FaceRecognizer model) ... + // ... + Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE); + // Some variables for the predicted label and associated confidence (e.g. distance): + int predicted_label = -1; + double predicted_confidence = 0.0; + // Get the prediction and associated confidence from the model + model->predict(img, predicted_label, predicted_confidence); + @endcode + */ + CV_WRAP void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const; + + + /** @brief - if implemented - send all result of prediction to collector that can be used for somehow custom result handling + @param src Sample image to get a prediction from. + @param collector User-defined collector object that accepts all results + + To implement this method u just have to do same internal cycle as in predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) but + not try to get "best@ result, just resend it to caller side with given collector + */ + CV_WRAP_AS(predict_collect) virtual void predict(InputArray src, Ptr collector) const = 0; + + /** @brief Saves a FaceRecognizer and its model state. + + Saves this model to a given filename, either as XML or YAML. + @param filename The filename to store this FaceRecognizer to (either XML/YAML). + + Every FaceRecognizer overwrites FaceRecognizer::save(FileStorage& fs) to save the internal model + state. FaceRecognizer::save(const String& filename) saves the state of a model to the given + filename. + + The suffix const means that prediction does not affect the internal model state, so the method can + be safely called from within different threads. + */ + CV_WRAP virtual void save(const String& filename) const; + + /** @brief Loads a FaceRecognizer and its model state. + + Loads a persisted model and state from a given XML or YAML file . Every FaceRecognizer has to + overwrite FaceRecognizer::load(FileStorage& fs) to enable loading the model state. + FaceRecognizer::load(FileStorage& fs) in turn gets called by + FaceRecognizer::load(const String& filename), to ease saving a model. + */ + CV_WRAP virtual void load(const String& filename); + + /** @overload + Saves this model to a given FileStorage. + @param fs The FileStorage to store this FaceRecognizer to. + */ + virtual void save(FileStorage& fs) const = 0; + + /** @overload */ + virtual void load(const FileStorage& fs) = 0; + + /** @brief Sets string info for the specified model's label. + + The string info is replaced by the provided value if it was set before for the specified label. + */ + CV_WRAP virtual void setLabelInfo(int label, const String& strInfo); + + /** @brief Gets string information by label. + + If an unknown label id is provided or there is no label information associated with the specified + label id the method returns an empty string. + */ + CV_WRAP virtual String getLabelInfo(int label) const; + + /** @brief Gets vector of labels by string. + + The function searches for the labels containing the specified sub-string in the associated string + info. + */ + CV_WRAP virtual std::vector getLabelsByString(const String& str) const; + /** @brief threshold parameter accessor - required for default BestMinDist collector */ + virtual double getThreshold() const = 0; + /** @brief Sets threshold of model */ + virtual void setThreshold(double val) = 0; +protected: + // Stored pairs "label id - string info" + std::map _labelsInfo; +}; + +//! @} + +}} + +#include "opencv2/face/facerec.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv2/face/bif.hpp b/thirdparty/linux/include/opencv2/face/bif.hpp new file mode 100644 index 0000000..c22c28c --- /dev/null +++ b/thirdparty/linux/include/opencv2/face/bif.hpp @@ -0,0 +1,83 @@ +/* +By downloading, copying, installing or using the software you agree to this license. +If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved. +Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +Copyright (C) 2015, OpenCV Foundation, all rights reserved. +Copyright (C) 2015, Itseez Inc., all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are disclaimed. +In no event shall copyright holders or contributors be liable for any direct, +indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_BIF_HPP__ +#define __OPENCV_BIF_HPP__ + +#include "opencv2/core.hpp" + +namespace cv { +namespace face { + +/** Implementation of bio-inspired features (BIF) from the paper: + * Guo, Guodong, et al. "Human age estimation using bio-inspired features." + * Computer Vision and Pattern Recognition, 2009. CVPR 2009. + */ +class CV_EXPORTS_W BIF : public Algorithm { +public: + /** @returns The number of filter bands used for computing BIF. */ + CV_WRAP virtual int getNumBands() const = 0; + + /** @returns The number of image rotations. */ + CV_WRAP virtual int getNumRotations() const = 0; + + /** Computes features sby input image. + * @param image Input image (CV_32FC1). + * @param features Feature vector (CV_32FC1). + */ + CV_WRAP virtual void compute(InputArray image, + OutputArray features) const = 0; +}; + +/** + * @param num_bands The number of filter bands (<=8) used for computing BIF. + * @param num_rotations The number of image rotations for computing BIF. + * @returns Object for computing BIF. + */ +CV_EXPORTS_W cv::Ptr createBIF(int num_bands = 8, int num_rotations = 12); + +} // namespace cv +} // namespace face + +#endif // #ifndef __OPENCV_FACEREC_HPP__ diff --git a/thirdparty/linux/include/opencv2/face/facerec.hpp b/thirdparty/linux/include/opencv2/face/facerec.hpp new file mode 100644 index 0000000..40f62f1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/face/facerec.hpp @@ -0,0 +1,166 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (c) 2011,2012. Philipp Wagner . +// Third party copyrights are property of their respective owners. + +#ifndef __OPENCV_FACEREC_HPP__ +#define __OPENCV_FACEREC_HPP__ + +#include "opencv2/face.hpp" +#include "opencv2/core.hpp" + +namespace cv { namespace face { + +//! @addtogroup face +//! @{ + +// base for two classes +class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer +{ +public: + /** @see setNumComponents */ + CV_WRAP virtual int getNumComponents() const = 0; + /** @copybrief getNumComponents @see getNumComponents */ + CV_WRAP virtual void setNumComponents(int val) = 0; + /** @see setThreshold */ + CV_WRAP virtual double getThreshold() const = 0; + /** @copybrief getThreshold @see getThreshold */ + CV_WRAP virtual void setThreshold(double val) = 0; + CV_WRAP virtual std::vector getProjections() const = 0; + CV_WRAP virtual cv::Mat getLabels() const = 0; + CV_WRAP virtual cv::Mat getEigenValues() const = 0; + CV_WRAP virtual cv::Mat getEigenVectors() const = 0; + CV_WRAP virtual cv::Mat getMean() const = 0; +}; + +/** +@param num_components The number of components (read: Eigenfaces) kept for this Principal +Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be +kept for good reconstruction capabilities. It is based on your input data, so experiment with the +number. Keeping 80 components should almost always be sufficient. +@param threshold The threshold applied in the prediction. + +### Notes: + +- Training and prediction must be done on grayscale images, use cvtColor to convert between the + color spaces. +- **THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL + SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your + input data has the correct shape, else a meaningful exception is thrown. Use resize to resize + the images. +- This model does not support updating. + +### Model internal data: + +- num_components see createEigenFaceRecognizer. +- threshold see createEigenFaceRecognizer. +- eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending). +- eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their + eigenvalue). +- mean The sample mean calculated from the training data. +- projections The projections of the training data. +- labels The threshold applied in the prediction. If the distance to the nearest neighbor is + larger than the threshold, this method returns -1. + */ +CV_EXPORTS_W Ptr createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); + +/** +@param num_components The number of components (read: Fisherfaces) kept for this Linear +Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that +means the number of your classes c (read: subjects, persons you want to recognize). If you leave +this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the +correct number (c-1) automatically. +@param threshold The threshold applied in the prediction. If the distance to the nearest neighbor +is larger than the threshold, this method returns -1. + +### Notes: + +- Training and prediction must be done on grayscale images, use cvtColor to convert between the + color spaces. +- **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL + SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your + input data has the correct shape, else a meaningful exception is thrown. Use resize to resize + the images. +- This model does not support updating. + +### Model internal data: + +- num_components see createFisherFaceRecognizer. +- threshold see createFisherFaceRecognizer. +- eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending). +- eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their + eigenvalue). +- mean The sample mean calculated from the training data. +- projections The projections of the training data. +- labels The labels corresponding to the projections. + */ +CV_EXPORTS_W Ptr createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); + +class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer +{ +public: + /** @see setGridX */ + CV_WRAP virtual int getGridX() const = 0; + /** @copybrief getGridX @see getGridX */ + CV_WRAP virtual void setGridX(int val) = 0; + /** @see setGridY */ + CV_WRAP virtual int getGridY() const = 0; + /** @copybrief getGridY @see getGridY */ + CV_WRAP virtual void setGridY(int val) = 0; + /** @see setRadius */ + CV_WRAP virtual int getRadius() const = 0; + /** @copybrief getRadius @see getRadius */ + CV_WRAP virtual void setRadius(int val) = 0; + /** @see setNeighbors */ + CV_WRAP virtual int getNeighbors() const = 0; + /** @copybrief getNeighbors @see getNeighbors */ + CV_WRAP virtual void setNeighbors(int val) = 0; + /** @see setThreshold */ + CV_WRAP virtual double getThreshold() const = 0; + /** @copybrief getThreshold @see getThreshold */ + CV_WRAP virtual void setThreshold(double val) = 0; + CV_WRAP virtual std::vector getHistograms() const = 0; + CV_WRAP virtual cv::Mat getLabels() const = 0; +}; + +/** +@param radius The radius used for building the Circular Local Binary Pattern. The greater the +radius, the +@param neighbors The number of sample points to build a Circular Local Binary Pattern from. An +appropriate value is to use `8` sample points. Keep in mind: the more sample points you include, +the higher the computational cost. +@param grid_x The number of cells in the horizontal direction, 8 is a common value used in +publications. The more cells, the finer the grid, the higher the dimensionality of the resulting +feature vector. +@param grid_y The number of cells in the vertical direction, 8 is a common value used in +publications. The more cells, the finer the grid, the higher the dimensionality of the resulting +feature vector. +@param threshold The threshold applied in the prediction. If the distance to the nearest neighbor +is larger than the threshold, this method returns -1. + +### Notes: + +- The Circular Local Binary Patterns (used in training and prediction) expect the data given as + grayscale images, use cvtColor to convert between the color spaces. +- This model supports updating. + +### Model internal data: + +- radius see createLBPHFaceRecognizer. +- neighbors see createLBPHFaceRecognizer. +- grid_x see createLBPHFaceRecognizer. +- grid_y see createLBPHFaceRecognizer. +- threshold see createLBPHFaceRecognizer. +- histograms Local Binary Patterns Histograms calculated from the given training data (empty if + none was given). +- labels Labels corresponding to the calculated Local Binary Patterns Histograms. + */ +CV_EXPORTS_W Ptr createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX); + +//! @} + +}} //namespace cv::face + +#endif //__OPENCV_FACEREC_HPP__ diff --git a/thirdparty/linux/include/opencv2/face/predict_collector.hpp b/thirdparty/linux/include/opencv2/face/predict_collector.hpp new file mode 100644 index 0000000..a9f907d --- /dev/null +++ b/thirdparty/linux/include/opencv2/face/predict_collector.hpp @@ -0,0 +1,127 @@ +/* +By downloading, copying, installing or using the software you agree to this license. +If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved. +Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +Copyright (C) 2015, OpenCV Foundation, all rights reserved. +Copyright (C) 2015, Itseez Inc., all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are disclaimed. +In no event shall copyright holders or contributors be liable for any direct, +indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_PREDICT_COLLECTOR_HPP__ +#define __OPENCV_PREDICT_COLLECTOR_HPP__ + +#include +#include +#include +#include + +#include "opencv2/core/cvstd.hpp" + +namespace cv { +namespace face { +//! @addtogroup face +//! @{ +/** @brief Abstract base class for all strategies of prediction result handling +*/ +class CV_EXPORTS_W PredictCollector +{ +public: + virtual ~PredictCollector() {} + + /** @brief Interface method called by face recognizer before results processing + @param size total size of prediction evaluation that recognizer could perform + */ + virtual void init(size_t size) { (void)size; } + + /** @brief Interface method called by face recognizer for each result + @param label current prediction label + @param dist current prediction distance (confidence) + */ + virtual bool collect(int label, double dist) = 0; +}; + +/** @brief Default predict collector + +Trace minimal distance with treshhold checking (that is default behavior for most predict logic) +*/ +class CV_EXPORTS_W StandardCollector : public PredictCollector +{ +public: + struct PredictResult + { + int label; + double distance; + PredictResult(int label_ = -1, double distance_ = DBL_MAX) : label(label_), distance(distance_) {} + }; +protected: + double threshold; + PredictResult minRes; + std::vector data; +public: + /** @brief Constructor + @param threshold_ set threshold + */ + StandardCollector(double threshold_ = DBL_MAX); + /** @brief overloaded interface method */ + void init(size_t size); + /** @brief overloaded interface method */ + bool collect(int label, double dist); + /** @brief Returns label with minimal distance */ + CV_WRAP int getMinLabel() const; + /** @brief Returns minimal distance value */ + CV_WRAP double getMinDist() const; + /** @brief Return results as vector + @param sorted If set, results will be sorted by distance + Each values is a pair of label and distance. + */ + CV_WRAP std::vector< std::pair > getResults(bool sorted = false) const; + /** @brief Return results as map + Labels are keys, values are minimal distances + */ + std::map getResultsMap() const; + /** @brief Static constructor + @param threshold set threshold + */ + CV_WRAP static Ptr create(double threshold = DBL_MAX); +}; + +//! @} +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/features2d.hpp b/thirdparty/linux/include/opencv2/features2d.hpp new file mode 100644 index 0000000..70fe409 --- /dev/null +++ b/thirdparty/linux/include/opencv2/features2d.hpp @@ -0,0 +1,1365 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_FEATURES_2D_HPP +#define OPENCV_FEATURES_2D_HPP + +#include "opencv2/core.hpp" +#include "opencv2/flann/miniflann.hpp" + +/** + @defgroup features2d 2D Features Framework + @{ + @defgroup features2d_main Feature Detection and Description + @defgroup features2d_match Descriptor Matchers + +Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to +easily switch between different algorithms solving the same problem. This section is devoted to +matching descriptors that are represented as vectors in a multidimensional space. All objects that +implement vector descriptor matchers inherit the DescriptorMatcher interface. + +@note + - An example explaining keypoint matching can be found at + opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp + - An example on descriptor matching evaluation can be found at + opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp + - An example on one to many image matching can be found at + opencv_source_code/samples/cpp/matching_to_many_images.cpp + + @defgroup features2d_draw Drawing Function of Keypoints and Matches + @defgroup features2d_category Object Categorization + +This section describes approaches based on local 2D features and used to categorize objects. + +@note + - A complete Bag-Of-Words sample can be found at + opencv_source_code/samples/cpp/bagofwords_classification.cpp + - (Python) An example using the features2D framework to perform object categorization can be + found at opencv_source_code/samples/python/find_obj.py + + @} + */ + +namespace cv +{ + +//! @addtogroup features2d +//! @{ + +// //! writes vector of keypoints to the file storage +// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector& keypoints); +// //! reads vector of keypoints from the specified file storage node +// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector& keypoints); + +/** @brief A class filters a vector of keypoints. + + Because now it is difficult to provide a convenient interface for all usage scenarios of the + keypoints filter class, it has only several needed by now static methods. + */ +class CV_EXPORTS KeyPointsFilter +{ +public: + KeyPointsFilter(){} + + /* + * Remove keypoints within borderPixels of an image edge. + */ + static void runByImageBorder( std::vector& keypoints, Size imageSize, int borderSize ); + /* + * Remove keypoints of sizes out of range. + */ + static void runByKeypointSize( std::vector& keypoints, float minSize, + float maxSize=FLT_MAX ); + /* + * Remove keypoints from some image by mask for pixels of this image. + */ + static void runByPixelsMask( std::vector& keypoints, const Mat& mask ); + /* + * Remove duplicated keypoints. + */ + static void removeDuplicated( std::vector& keypoints ); + + /* + * Retain the specified number of the best keypoints (according to the response) + */ + static void retainBest( std::vector& keypoints, int npoints ); +}; + + +/************************************ Base Classes ************************************/ + +/** @brief Abstract base class for 2D image feature detectors and descriptor extractors +*/ +class CV_EXPORTS_W Feature2D : public virtual Algorithm +{ +public: + virtual ~Feature2D(); + + /** @brief Detects keypoints in an image (first variant) or image set (second variant). + + @param image Image. + @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set + of keypoints detected in images[i] . + @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer + matrix with non-zero values in the region of interest. + */ + CV_WRAP virtual void detect( InputArray image, + CV_OUT std::vector& keypoints, + InputArray mask=noArray() ); + + /** @overload + @param images Image set. + @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set + of keypoints detected in images[i] . + @param masks Masks for each input image specifying where to look for keypoints (optional). + masks[i] is a mask for images[i]. + */ + CV_WRAP virtual void detect( InputArrayOfArrays images, + CV_OUT std::vector >& keypoints, + InputArrayOfArrays masks=noArray() ); + + /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set + (second variant). + + @param image Image. + @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be + computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint + with several dominant orientations (for each orientation). + @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are + descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the + descriptor for keypoint j-th keypoint. + */ + CV_WRAP virtual void compute( InputArray image, + CV_OUT CV_IN_OUT std::vector& keypoints, + OutputArray descriptors ); + + /** @overload + + @param images Image set. + @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be + computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint + with several dominant orientations (for each orientation). + @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are + descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the + descriptor for keypoint j-th keypoint. + */ + CV_WRAP virtual void compute( InputArrayOfArrays images, + CV_OUT CV_IN_OUT std::vector >& keypoints, + OutputArrayOfArrays descriptors ); + + /** Detects keypoints and computes the descriptors */ + CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask, + CV_OUT std::vector& keypoints, + OutputArray descriptors, + bool useProvidedKeypoints=false ); + + CV_WRAP virtual int descriptorSize() const; + CV_WRAP virtual int descriptorType() const; + CV_WRAP virtual int defaultNorm() const; + + CV_WRAP void write( const String& fileName ) const; + + CV_WRAP void read( const String& fileName ); + + virtual void write( FileStorage&) const; + + virtual void read( const FileNode&); + + //! Return true if detector object is empty + CV_WRAP virtual bool empty() const; +}; + +/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch +between different algorithms solving the same problem. All objects that implement keypoint detectors +inherit the FeatureDetector interface. */ +typedef Feature2D FeatureDetector; + +/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you +to easily switch between different algorithms solving the same problem. This section is devoted to +computing descriptors represented as vectors in a multidimensional space. All objects that implement +the vector descriptor extractors inherit the DescriptorExtractor interface. + */ +typedef Feature2D DescriptorExtractor; + +//! @addtogroup features2d_main +//! @{ + +/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 . + */ +class CV_EXPORTS_W BRISK : public Feature2D +{ +public: + /** @brief The BRISK constructor + + @param thresh AGAST detection threshold score. + @param octaves detection octaves. Use 0 to do single scale. + @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a + keypoint. + */ + CV_WRAP static Ptr create(int thresh=30, int octaves=3, float patternScale=1.0f); + + /** @brief The BRISK constructor for a custom pattern + + @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for + keypoint scale 1). + @param numberList defines the number of sampling points on the sampling circle. Must be the same + size as radiusList.. + @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint + scale 1). + @param dMin threshold for the long pairings used for orientation determination (in pixels for + keypoint scale 1). + @param indexChange index remapping of the bits. */ + CV_WRAP static Ptr create(const std::vector &radiusList, const std::vector &numberList, + float dMax=5.85f, float dMin=8.2f, const std::vector& indexChange=std::vector()); +}; + +/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor + +described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects +the strongest features using FAST or Harris response, finds their orientation using first-order +moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or +k-tuples) are rotated according to the measured orientation). + */ +class CV_EXPORTS_W ORB : public Feature2D +{ +public: + enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 }; + + /** @brief The ORB constructor + + @param nfeatures The maximum number of features to retain. + @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical + pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor + will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor + will mean that to cover certain scale range you will need more pyramid levels and so the speed + will suffer. + @param nlevels The number of pyramid levels. The smallest level will have linear size equal to + input_image_linear_size/pow(scaleFactor, nlevels). + @param edgeThreshold This is size of the border where the features are not detected. It should + roughly match the patchSize parameter. + @param firstLevel It should be 0 in the current implementation. + @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The + default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, + so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 + random points (of course, those point coordinates are random, but they are generated from the + pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel + rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such + output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, + denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each + bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). + @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features + (the score is written to KeyPoint::score and is used to retain best nfeatures features); + FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, + but it is a little faster to compute. + @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller + pyramid layers the perceived image area covered by a feature will be larger. + @param fastThreshold + */ + CV_WRAP static Ptr create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, + int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20); + + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + CV_WRAP virtual int getMaxFeatures() const = 0; + + CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0; + CV_WRAP virtual double getScaleFactor() const = 0; + + CV_WRAP virtual void setNLevels(int nlevels) = 0; + CV_WRAP virtual int getNLevels() const = 0; + + CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0; + CV_WRAP virtual int getEdgeThreshold() const = 0; + + CV_WRAP virtual void setFirstLevel(int firstLevel) = 0; + CV_WRAP virtual int getFirstLevel() const = 0; + + CV_WRAP virtual void setWTA_K(int wta_k) = 0; + CV_WRAP virtual int getWTA_K() const = 0; + + CV_WRAP virtual void setScoreType(int scoreType) = 0; + CV_WRAP virtual int getScoreType() const = 0; + + CV_WRAP virtual void setPatchSize(int patchSize) = 0; + CV_WRAP virtual int getPatchSize() const = 0; + + CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0; + CV_WRAP virtual int getFastThreshold() const = 0; +}; + +/** @brief Maximally stable extremal region extractor + +The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki +article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)). + +- there are two different implementation of %MSER: one for grey image, one for color image + +- the grey image algorithm is taken from: @cite nister2008linear ; the paper claims to be faster +than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop. + +- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower +than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source +code which is distributed under GPL. + +- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py +*/ +class CV_EXPORTS_W MSER : public Feature2D +{ +public: + /** @brief Full consturctor for %MSER detector + + @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$ + @param _min_area prune the area which smaller than minArea + @param _max_area prune the area which bigger than maxArea + @param _max_variation prune the area have simliar size to its children + @param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity + @param _max_evolution for color image, the evolution steps + @param _area_threshold for color image, the area threshold to cause re-initialize + @param _min_margin for color image, ignore too small margin + @param _edge_blur_size for color image, the aperture size for edge blur + */ + CV_WRAP static Ptr create( int _delta=5, int _min_area=60, int _max_area=14400, + double _max_variation=0.25, double _min_diversity=.2, + int _max_evolution=200, double _area_threshold=1.01, + double _min_margin=0.003, int _edge_blur_size=5 ); + + /** @brief Detect %MSER regions + + @param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3) + @param msers resulting list of point sets + @param bboxes resulting bounding boxes + */ + CV_WRAP virtual void detectRegions( InputArray image, + CV_OUT std::vector >& msers, + CV_OUT std::vector& bboxes ) = 0; + + CV_WRAP virtual void setDelta(int delta) = 0; + CV_WRAP virtual int getDelta() const = 0; + + CV_WRAP virtual void setMinArea(int minArea) = 0; + CV_WRAP virtual int getMinArea() const = 0; + + CV_WRAP virtual void setMaxArea(int maxArea) = 0; + CV_WRAP virtual int getMaxArea() const = 0; + + CV_WRAP virtual void setPass2Only(bool f) = 0; + CV_WRAP virtual bool getPass2Only() const = 0; +}; + +/** @overload */ +CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression=true ); + +/** @brief Detects corners using the FAST algorithm + +@param image grayscale image where keypoints (corners) are detected. +@param keypoints keypoints detected on the image. +@param threshold threshold on difference between intensity of the central pixel and pixels of a +circle around this pixel. +@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners +(keypoints). +@param type one of the three neighborhoods as defined in the paper: +FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, +FastFeatureDetector::TYPE_5_8 + +Detects corners using the FAST algorithm by @cite Rosten06 . + +@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8, +cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner +detection, use cv2.FAST.detect() method. + */ +CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression, int type ); + +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Wrapping class for feature detection using the FAST method. : + */ +class CV_EXPORTS_W FastFeatureDetector : public Feature2D +{ +public: + enum + { + TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2, + THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002, + }; + + CV_WRAP static Ptr create( int threshold=10, + bool nonmaxSuppression=true, + int type=FastFeatureDetector::TYPE_9_16 ); + + CV_WRAP virtual void setThreshold(int threshold) = 0; + CV_WRAP virtual int getThreshold() const = 0; + + CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; + CV_WRAP virtual bool getNonmaxSuppression() const = 0; + + CV_WRAP virtual void setType(int type) = 0; + CV_WRAP virtual int getType() const = 0; +}; + +/** @overload */ +CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression=true ); + +/** @brief Detects corners using the AGAST algorithm + +@param image grayscale image where keypoints (corners) are detected. +@param keypoints keypoints detected on the image. +@param threshold threshold on difference between intensity of the central pixel and pixels of a +circle around this pixel. +@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners +(keypoints). +@param type one of the four neighborhoods as defined in the paper: +AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d, +AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16 + +For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. +The 32-bit binary tree tables were generated automatically from original code using perl script. +The perl script and examples of tree generation are placed in features2d/doc folder. +Detects corners using the AGAST algorithm by @cite mair2010_agast . + + */ +CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression, int type ); +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Wrapping class for feature detection using the AGAST method. : + */ +class CV_EXPORTS_W AgastFeatureDetector : public Feature2D +{ +public: + enum + { + AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3, + THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001, + }; + + CV_WRAP static Ptr create( int threshold=10, + bool nonmaxSuppression=true, + int type=AgastFeatureDetector::OAST_9_16 ); + + CV_WRAP virtual void setThreshold(int threshold) = 0; + CV_WRAP virtual int getThreshold() const = 0; + + CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; + CV_WRAP virtual bool getNonmaxSuppression() const = 0; + + CV_WRAP virtual void setType(int type) = 0; + CV_WRAP virtual int getType() const = 0; +}; + +/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. : + */ +class CV_EXPORTS_W GFTTDetector : public Feature2D +{ +public: + CV_WRAP static Ptr create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, + int blockSize=3, bool useHarrisDetector=false, double k=0.04 ); + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + CV_WRAP virtual int getMaxFeatures() const = 0; + + CV_WRAP virtual void setQualityLevel(double qlevel) = 0; + CV_WRAP virtual double getQualityLevel() const = 0; + + CV_WRAP virtual void setMinDistance(double minDistance) = 0; + CV_WRAP virtual double getMinDistance() const = 0; + + CV_WRAP virtual void setBlockSize(int blockSize) = 0; + CV_WRAP virtual int getBlockSize() const = 0; + + CV_WRAP virtual void setHarrisDetector(bool val) = 0; + CV_WRAP virtual bool getHarrisDetector() const = 0; + + CV_WRAP virtual void setK(double k) = 0; + CV_WRAP virtual double getK() const = 0; +}; + +/** @brief Class for extracting blobs from an image. : + +The class implements a simple algorithm for extracting blobs from an image: + +1. Convert the source image to binary images by applying thresholding with several thresholds from + minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between + neighboring thresholds. +2. Extract connected components from every binary image by findContours and calculate their + centers. +3. Group centers from several binary images by their coordinates. Close centers form one group that + corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter. +4. From the groups, estimate final centers of blobs and their radiuses and return as locations and + sizes of keypoints. + +This class performs several filtrations of returned blobs. You should set filterBy\* to true/false +to turn on/off corresponding filtration. Available filtrations: + +- **By color**. This filter compares the intensity of a binary image at the center of a blob to +blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs +and blobColor = 255 to extract light blobs. +- **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive). +- **By circularity**. Extracted blobs have circularity +(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and +maxCircularity (exclusive). +- **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio +between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive). +- **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between +minConvexity (inclusive) and maxConvexity (exclusive). + +Default values of parameters are tuned to extract dark circular blobs. + */ +class CV_EXPORTS_W SimpleBlobDetector : public Feature2D +{ +public: + struct CV_EXPORTS_W_SIMPLE Params + { + CV_WRAP Params(); + CV_PROP_RW float thresholdStep; + CV_PROP_RW float minThreshold; + CV_PROP_RW float maxThreshold; + CV_PROP_RW size_t minRepeatability; + CV_PROP_RW float minDistBetweenBlobs; + + CV_PROP_RW bool filterByColor; + CV_PROP_RW uchar blobColor; + + CV_PROP_RW bool filterByArea; + CV_PROP_RW float minArea, maxArea; + + CV_PROP_RW bool filterByCircularity; + CV_PROP_RW float minCircularity, maxCircularity; + + CV_PROP_RW bool filterByInertia; + CV_PROP_RW float minInertiaRatio, maxInertiaRatio; + + CV_PROP_RW bool filterByConvexity; + CV_PROP_RW float minConvexity, maxConvexity; + + void read( const FileNode& fn ); + void write( FileStorage& fs ) const; + }; + + CV_WRAP static Ptr + create(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params()); +}; + +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 . + +@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo +F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision +(ECCV), Fiorenze, Italy, October 2012. +*/ +class CV_EXPORTS_W KAZE : public Feature2D +{ +public: + enum + { + DIFF_PM_G1 = 0, + DIFF_PM_G2 = 1, + DIFF_WEICKERT = 2, + DIFF_CHARBONNIER = 3 + }; + + /** @brief The KAZE constructor + + @param extended Set to enable extraction of extended (128-byte) descriptor. + @param upright Set to enable use of upright descriptors (non rotation-invariant). + @param threshold Detector response threshold to accept point + @param nOctaves Maximum octave evolution of the image + @param nOctaveLayers Default number of sublevels per scale level + @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or + DIFF_CHARBONNIER + */ + CV_WRAP static Ptr create(bool extended=false, bool upright=false, + float threshold = 0.001f, + int nOctaves = 4, int nOctaveLayers = 4, + int diffusivity = KAZE::DIFF_PM_G2); + + CV_WRAP virtual void setExtended(bool extended) = 0; + CV_WRAP virtual bool getExtended() const = 0; + + CV_WRAP virtual void setUpright(bool upright) = 0; + CV_WRAP virtual bool getUpright() const = 0; + + CV_WRAP virtual void setThreshold(double threshold) = 0; + CV_WRAP virtual double getThreshold() const = 0; + + CV_WRAP virtual void setNOctaves(int octaves) = 0; + CV_WRAP virtual int getNOctaves() const = 0; + + CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; + CV_WRAP virtual int getNOctaveLayers() const = 0; + + CV_WRAP virtual void setDiffusivity(int diff) = 0; + CV_WRAP virtual int getDiffusivity() const = 0; +}; + +/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13 . : + +@note AKAZE descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using *extract* +and *detect* instead of *operator()* due to performance reasons. .. [ANB13] Fast Explicit Diffusion +for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien +Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013. + */ +class CV_EXPORTS_W AKAZE : public Feature2D +{ +public: + // AKAZE descriptor type + enum + { + DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation + DESCRIPTOR_KAZE = 3, + DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation + DESCRIPTOR_MLDB = 5 + }; + + /** @brief The AKAZE constructor + + @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE, + DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT. + @param descriptor_size Size of the descriptor in bits. 0 -\> Full size + @param descriptor_channels Number of channels in the descriptor (1, 2, 3) + @param threshold Detector response threshold to accept point + @param nOctaves Maximum octave evolution of the image + @param nOctaveLayers Default number of sublevels per scale level + @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or + DIFF_CHARBONNIER + */ + CV_WRAP static Ptr create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB, + int descriptor_size = 0, int descriptor_channels = 3, + float threshold = 0.001f, int nOctaves = 4, + int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2); + + CV_WRAP virtual void setDescriptorType(int dtype) = 0; + CV_WRAP virtual int getDescriptorType() const = 0; + + CV_WRAP virtual void setDescriptorSize(int dsize) = 0; + CV_WRAP virtual int getDescriptorSize() const = 0; + + CV_WRAP virtual void setDescriptorChannels(int dch) = 0; + CV_WRAP virtual int getDescriptorChannels() const = 0; + + CV_WRAP virtual void setThreshold(double threshold) = 0; + CV_WRAP virtual double getThreshold() const = 0; + + CV_WRAP virtual void setNOctaves(int octaves) = 0; + CV_WRAP virtual int getNOctaves() const = 0; + + CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; + CV_WRAP virtual int getNOctaveLayers() const = 0; + + CV_WRAP virtual void setDiffusivity(int diff) = 0; + CV_WRAP virtual int getDiffusivity() const = 0; +}; + +//! @} features2d_main + +/****************************************************************************************\ +* Distance * +\****************************************************************************************/ + +template +struct CV_EXPORTS Accumulator +{ + typedef T Type; +}; + +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; + +/* + * Squared Euclidean distance functor + */ +template +struct CV_EXPORTS SL2 +{ + enum { normType = NORM_L2SQR }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return normL2Sqr(a, b, size); + } +}; + +/* + * Euclidean distance functor + */ +template +struct CV_EXPORTS L2 +{ + enum { normType = NORM_L2 }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return (ResultType)std::sqrt((double)normL2Sqr(a, b, size)); + } +}; + +/* + * Manhattan distance (city block distance) functor + */ +template +struct CV_EXPORTS L1 +{ + enum { normType = NORM_L1 }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return normL1(a, b, size); + } +}; + +/****************************************************************************************\ +* DescriptorMatcher * +\****************************************************************************************/ + +//! @addtogroup features2d_match +//! @{ + +/** @brief Abstract base class for matching keypoint descriptors. + +It has two groups of match methods: for matching descriptors of an image with another image or with +an image set. + */ +class CV_EXPORTS_W DescriptorMatcher : public Algorithm +{ +public: + enum + { + FLANNBASED = 1, + BRUTEFORCE = 2, + BRUTEFORCE_L1 = 3, + BRUTEFORCE_HAMMING = 4, + BRUTEFORCE_HAMMINGLUT = 5, + BRUTEFORCE_SL2 = 6 + }; + virtual ~DescriptorMatcher(); + + /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor + collection. + + If the collection is not empty, the new descriptors are added to existing train descriptors. + + @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same + train image. + */ + CV_WRAP virtual void add( InputArrayOfArrays descriptors ); + + /** @brief Returns a constant link to the train descriptor collection trainDescCollection . + */ + CV_WRAP const std::vector& getTrainDescriptors() const; + + /** @brief Clears the train descriptor collections. + */ + CV_WRAP virtual void clear(); + + /** @brief Returns true if there are no train descriptors in the both collections. + */ + CV_WRAP virtual bool empty() const; + + /** @brief Returns true if the descriptor matcher supports masking permissible matches. + */ + CV_WRAP virtual bool isMaskSupported() const = 0; + + /** @brief Trains a descriptor matcher + + Trains a descriptor matcher (for example, the flann index). In all methods to match, the method + train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher) + have an empty implementation of this method. Other matchers really train their inner structures (for + example, FlannBasedMatcher trains flann::Index ). + */ + CV_WRAP virtual void train(); + + /** @brief Finds the best match for each descriptor from a query set. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param matches Matches. If a query descriptor is masked out in mask , no match is added for this + descriptor. So, matches size may be smaller than the query descriptors count. + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + + In the first variant of this method, the train descriptors are passed as an input argument. In the + second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is + used. Optional mask (or masks) can be passed to specify which query and training descriptors can be + matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if + mask.at\(i,j) is non-zero. + */ + CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector& matches, InputArray mask=noArray() ) const; + + /** @brief Finds the k best matches for each descriptor from a query set. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. + @param k Count of best matches found per each query descriptor or less if a query descriptor has + less than k possible matches in total. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + + These extended variants of DescriptorMatcher::match methods find several best matches for each query + descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match + for the details about query and train descriptors. + */ + CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector >& matches, int k, + InputArray mask=noArray(), bool compactResult=false ) const; + + /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param matches Found matches. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + @param maxDistance Threshold for the distance between matched descriptors. Distance means here + metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured + in Pixels)! + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + + For each query descriptor, the methods find such training descriptors that the distance between the + query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are + returned in the distance increasing order. + */ + CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector >& matches, float maxDistance, + InputArray mask=noArray(), bool compactResult=false ) const; + + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Matches. If a query descriptor is masked out in mask , no match is added for this + descriptor. So, matches size may be smaller than the query descriptors count. + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + */ + CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector& matches, + InputArrayOfArrays masks=noArray() ); + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. + @param k Count of best matches found per each query descriptor or less if a query descriptor has + less than k possible matches in total. + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + */ + CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Found matches. + @param maxDistance Threshold for the distance between matched descriptors. Distance means here + metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured + in Pixels)! + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + */ + CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + + + CV_WRAP void write( const String& fileName ) const + { + FileStorage fs(fileName, FileStorage::WRITE); + write(fs); + } + + CV_WRAP void read( const String& fileName ) + { + FileStorage fs(fileName, FileStorage::READ); + read(fs.root()); + } + // Reads matcher object from a file node + virtual void read( const FileNode& ); + // Writes matcher object to a file storage + virtual void write( FileStorage& ) const; + + /** @brief Clones the matcher. + + @param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object, + that is, copies both parameters and train data. If emptyTrainData is true, the method creates an + object copy with the current parameters but with empty train data. + */ + CV_WRAP virtual Ptr clone( bool emptyTrainData=false ) const = 0; + + /** @brief Creates a descriptor matcher of a given type with the default parameters (using default + constructor). + + @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are + supported: + - `BruteForce` (it uses L2 ) + - `BruteForce-L1` + - `BruteForce-Hamming` + - `BruteForce-Hamming(2)` + - `FlannBased` + */ + CV_WRAP static Ptr create( const String& descriptorMatcherType ); + + CV_WRAP static Ptr create( int matcherType ); + +protected: + /** + * Class to work with descriptors from several images as with one merged matrix. + * It is used e.g. in FlannBasedMatcher. + */ + class CV_EXPORTS DescriptorCollection + { + public: + DescriptorCollection(); + DescriptorCollection( const DescriptorCollection& collection ); + virtual ~DescriptorCollection(); + + // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here. + void set( const std::vector& descriptors ); + virtual void clear(); + + const Mat& getDescriptors() const; + const Mat getDescriptor( int imgIdx, int localDescIdx ) const; + const Mat getDescriptor( int globalDescIdx ) const; + void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const; + + int size() const; + + protected: + Mat mergedDescriptors; + std::vector startIdxs; + }; + + //! In fact the matching is implemented only by the following two methods. These methods suppose + //! that the class object has been trained already. Public match methods call these methods + //! after calling train(). + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; + + static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx ); + static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx ); + + static Mat clone_op( Mat m ) { return m.clone(); } + void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const; + + //! Collection of descriptors from train images. + std::vector trainDescCollection; + std::vector utrainDescCollection; +}; + +/** @brief Brute-force descriptor matcher. + +For each descriptor in the first set, this matcher finds the closest descriptor in the second set +by trying each one. This descriptor matcher supports masking permissible matches of descriptor +sets. + */ +class CV_EXPORTS_W BFMatcher : public DescriptorMatcher +{ +public: + /** @brief Brute-force matcher constructor (obsolete). Please use BFMatcher.create() + * + * + */ + CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false ); + + virtual ~BFMatcher() {} + + virtual bool isMaskSupported() const { return true; } + + /* @brief Brute-force matcher create method. + @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are + preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and + BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor + description). + @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k + nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with + k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the + matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent + pairs. Such technique usually produces best results with minimal number of outliers when there are + enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper. + */ + CV_WRAP static Ptr create( int normType=NORM_L2, bool crossCheck=false ) ; + + virtual Ptr clone( bool emptyTrainData=false ) const; +protected: + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + + int normType; + bool crossCheck; +}; + + +/** @brief Flann-based descriptor matcher. + +This matcher trains cv::flann::Index on a train descriptor collection and calls its nearest search +methods to find the best matches. So, this matcher may be faster when matching a large train +collection than the brute force matcher. FlannBasedMatcher does not support masking permissible +matches of descriptor sets because flann::Index does not support this. : + */ +class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher +{ +public: + CV_WRAP FlannBasedMatcher( const Ptr& indexParams=makePtr(), + const Ptr& searchParams=makePtr() ); + + virtual void add( InputArrayOfArrays descriptors ); + virtual void clear(); + + // Reads matcher object from a file node + virtual void read( const FileNode& ); + // Writes matcher object to a file storage + virtual void write( FileStorage& ) const; + + virtual void train(); + virtual bool isMaskSupported() const; + + CV_WRAP static Ptr create(); + + virtual Ptr clone( bool emptyTrainData=false ) const; +protected: + static void convertToDMatches( const DescriptorCollection& descriptors, + const Mat& indices, const Mat& distances, + std::vector >& matches ); + + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + + Ptr indexParams; + Ptr searchParams; + Ptr flannIndex; + + DescriptorCollection mergedDescriptors; + int addedDescCount; +}; + +//! @} features2d_match + +/****************************************************************************************\ +* Drawing functions * +\****************************************************************************************/ + +//! @addtogroup features2d_draw +//! @{ + +struct CV_EXPORTS DrawMatchesFlags +{ + enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create), + //!< i.e. existing memory of output image may be reused. + //!< Two source image, matches and single keypoints will be drawn. + //!< For each keypoint only the center point will be drawn (without + //!< the circle around keypoint with keypoint size and orientation). + DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create). + //!< Matches will be drawn on existing content of output image. + NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn. + DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and + //!< orientation will be drawn. + }; +}; + +/** @brief Draws keypoints. + +@param image Source image. +@param keypoints Keypoints from the source image. +@param outImage Output image. Its content depends on the flags value defining what is drawn in the +output image. See possible flags bit values below. +@param color Color of keypoints. +@param flags Flags setting drawing features. Possible flags bit values are defined by +DrawMatchesFlags. See details above in drawMatches . + +@note +For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT, +cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, +cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS + */ +CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector& keypoints, InputOutputArray outImage, + const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT ); + +/** @brief Draws the found matches of keypoints from two images. + +@param img1 First source image. +@param keypoints1 Keypoints from the first source image. +@param img2 Second source image. +@param keypoints2 Keypoints from the second source image. +@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i] +has a corresponding point in keypoints2[matches[i]] . +@param outImg Output image. Its content depends on the flags value defining what is drawn in the +output image. See possible flags bit values below. +@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) +, the color is generated randomly. +@param singlePointColor Color of single keypoints (circles), which means that keypoints do not +have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. +@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are +drawn. +@param flags Flags setting drawing features. Possible flags bit values are defined by +DrawMatchesFlags. + +This function draws matches of keypoints from two images in the output image. Match is a line +connecting two keypoints (circles). See cv::DrawMatchesFlags. + */ +CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector& keypoints1, + InputArray img2, const std::vector& keypoints2, + const std::vector& matches1to2, InputOutputArray outImg, + const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), + const std::vector& matchesMask=std::vector(), int flags=DrawMatchesFlags::DEFAULT ); + +/** @overload */ +CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector& keypoints1, + InputArray img2, const std::vector& keypoints2, + const std::vector >& matches1to2, InputOutputArray outImg, + const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), + const std::vector >& matchesMask=std::vector >(), int flags=DrawMatchesFlags::DEFAULT ); + +//! @} features2d_draw + +/****************************************************************************************\ +* Functions to evaluate the feature detectors and [generic] descriptor extractors * +\****************************************************************************************/ + +CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2, + std::vector* keypoints1, std::vector* keypoints2, + float& repeatability, int& correspCount, + const Ptr& fdetector=Ptr() ); + +CV_EXPORTS void computeRecallPrecisionCurve( const std::vector >& matches1to2, + const std::vector >& correctMatches1to2Mask, + std::vector& recallPrecisionCurve ); + +CV_EXPORTS float getRecall( const std::vector& recallPrecisionCurve, float l_precision ); +CV_EXPORTS int getNearestPoint( const std::vector& recallPrecisionCurve, float l_precision ); + +/****************************************************************************************\ +* Bag of visual words * +\****************************************************************************************/ + +//! @addtogroup features2d_category +//! @{ + +/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors. + +For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka, +Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. : + */ +class CV_EXPORTS_W BOWTrainer +{ +public: + BOWTrainer(); + virtual ~BOWTrainer(); + + /** @brief Adds descriptors to a training set. + + @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a + descriptor. + + The training set is clustered using clustermethod to construct the vocabulary. + */ + CV_WRAP void add( const Mat& descriptors ); + + /** @brief Returns a training set of descriptors. + */ + CV_WRAP const std::vector& getDescriptors() const; + + /** @brief Returns the count of all descriptors stored in the training set. + */ + CV_WRAP int descriptorsCount() const; + + CV_WRAP virtual void clear(); + + /** @overload */ + CV_WRAP virtual Mat cluster() const = 0; + + /** @brief Clusters train descriptors. + + @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor. + Descriptors are not added to the inner train descriptor set. + + The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first + variant of the method, train descriptors stored in the object are clustered. In the second variant, + input descriptors are clustered. + */ + CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0; + +protected: + std::vector descriptors; + int size; +}; + +/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. : + */ +class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer +{ +public: + /** @brief The constructor. + + @see cv::kmeans + */ + CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(), + int attempts=3, int flags=KMEANS_PP_CENTERS ); + virtual ~BOWKMeansTrainer(); + + // Returns trained vocabulary (i.e. cluster centers). + CV_WRAP virtual Mat cluster() const; + CV_WRAP virtual Mat cluster( const Mat& descriptors ) const; + +protected: + + int clusterCount; + TermCriteria termcrit; + int attempts; + int flags; +}; + +/** @brief Class to compute an image descriptor using the *bag of visual words*. + +Such a computation consists of the following steps: + +1. Compute descriptors for a given image and its keypoints set. +2. Find the nearest visual words from the vocabulary for each keypoint descriptor. +3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words +encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the +vocabulary in the given image. + */ +class CV_EXPORTS_W BOWImgDescriptorExtractor +{ +public: + /** @brief The constructor. + + @param dextractor Descriptor extractor that is used to compute descriptors for an input image and + its keypoints. + @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary + for each keypoint descriptor of the image. + */ + CV_WRAP BOWImgDescriptorExtractor( const Ptr& dextractor, + const Ptr& dmatcher ); + /** @overload */ + BOWImgDescriptorExtractor( const Ptr& dmatcher ); + virtual ~BOWImgDescriptorExtractor(); + + /** @brief Sets a visual vocabulary. + + @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the + vocabulary is a visual word (cluster center). + */ + CV_WRAP void setVocabulary( const Mat& vocabulary ); + + /** @brief Returns the set vocabulary. + */ + CV_WRAP const Mat& getVocabulary() const; + + /** @brief Computes an image descriptor using the set visual vocabulary. + + @param image Image, for which the descriptor is computed. + @param keypoints Keypoints detected in the input image. + @param imgDescriptor Computed output image descriptor. + @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that + pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) + returned if it is non-zero. + @param descriptors Descriptors of the image keypoints that are returned if they are non-zero. + */ + void compute( InputArray image, std::vector& keypoints, OutputArray imgDescriptor, + std::vector >* pointIdxsOfClusters=0, Mat* descriptors=0 ); + /** @overload + @param keypointDescriptors Computed descriptors to match with vocabulary. + @param imgDescriptor Computed output image descriptor. + @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that + pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) + returned if it is non-zero. + */ + void compute( InputArray keypointDescriptors, OutputArray imgDescriptor, + std::vector >* pointIdxsOfClusters=0 ); + // compute() is not constant because DescriptorMatcher::match is not constant + + CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector& keypoints, CV_OUT Mat& imgDescriptor ) + { compute(image,keypoints,imgDescriptor); } + + /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0. + */ + CV_WRAP int descriptorSize() const; + + /** @brief Returns an image descriptor type. + */ + CV_WRAP int descriptorType() const; + +protected: + Mat vocabulary; + Ptr dextractor; + Ptr dmatcher; +}; + +//! @} features2d_category + +//! @} features2d + +} /* namespace cv */ + +#endif diff --git a/thirdparty/linux/include/opencv2/features2d/features2d.hpp b/thirdparty/linux/include/opencv2/features2d/features2d.hpp new file mode 100644 index 0000000..e81df0a --- /dev/null +++ b/thirdparty/linux/include/opencv2/features2d/features2d.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/features2d.hpp" diff --git a/thirdparty/linux/include/opencv2/flann.hpp b/thirdparty/linux/include/opencv2/flann.hpp new file mode 100644 index 0000000..19a98f1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann.hpp @@ -0,0 +1,531 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_FLANN_HPP +#define OPENCV_FLANN_HPP + +#include "opencv2/core.hpp" +#include "opencv2/flann/miniflann.hpp" +#include "opencv2/flann/flann_base.hpp" + +/** +@defgroup flann Clustering and Search in Multi-Dimensional Spaces + +This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate +Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest +neighbor search in large datasets and for high dimensional features. More information about FLANN +can be found in @cite Muja2009 . +*/ + +namespace cvflann +{ + CV_EXPORTS flann_distance_t flann_distance_type(); + FLANN_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order); +} + + +namespace cv +{ +namespace flann +{ + + +//! @addtogroup flann +//! @{ + +template struct CvType {}; +template <> struct CvType { static int type() { return CV_8U; } }; +template <> struct CvType { static int type() { return CV_8S; } }; +template <> struct CvType { static int type() { return CV_16U; } }; +template <> struct CvType { static int type() { return CV_16S; } }; +template <> struct CvType { static int type() { return CV_32S; } }; +template <> struct CvType { static int type() { return CV_32F; } }; +template <> struct CvType { static int type() { return CV_64F; } }; + + +// bring the flann parameters into this namespace +using ::cvflann::get_param; +using ::cvflann::print_params; + +// bring the flann distances into this namespace +using ::cvflann::L2_Simple; +using ::cvflann::L2; +using ::cvflann::L1; +using ::cvflann::MinkowskiDistance; +using ::cvflann::MaxDistance; +using ::cvflann::HammingLUT; +using ::cvflann::Hamming; +using ::cvflann::Hamming2; +using ::cvflann::HistIntersectionDistance; +using ::cvflann::HellingerDistance; +using ::cvflann::ChiSquareDistance; +using ::cvflann::KL_Divergence; + + +/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which +the index is built. + */ +template +class GenericIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + /** @brief Constructs a nearest neighbor search index for a given dataset. + + @param features Matrix of containing the features(points) to index. The size of the matrix is + num_features x feature_dimensionality and the data type of the elements in the matrix must + coincide with the type of the index. + @param params Structure containing the index parameters. The type of index that will be + constructed depends on the type of this parameter. See the description. + @param distance + + The method constructs a fast search structure from a set of features using the specified algorithm + with specified parameters, as defined by params. params is a reference to one of the following class + IndexParams descendants: + + - **LinearIndexParams** When passing an object of this type, the index will perform a linear, + brute-force search. : + @code + struct LinearIndexParams : public IndexParams + { + }; + @endcode + - **KDTreeIndexParams** When passing an object of this type the index constructed will consist of + a set of randomized kd-trees which will be searched in parallel. : + @code + struct KDTreeIndexParams : public IndexParams + { + KDTreeIndexParams( int trees = 4 ); + }; + @endcode + - **KMeansIndexParams** When passing an object of this type the index constructed will be a + hierarchical k-means tree. : + @code + struct KMeansIndexParams : public IndexParams + { + KMeansIndexParams( + int branching = 32, + int iterations = 11, + flann_centers_init_t centers_init = CENTERS_RANDOM, + float cb_index = 0.2 ); + }; + @endcode + - **CompositeIndexParams** When using a parameters object of this type the index created + combines the randomized kd-trees and the hierarchical k-means tree. : + @code + struct CompositeIndexParams : public IndexParams + { + CompositeIndexParams( + int trees = 4, + int branching = 32, + int iterations = 11, + flann_centers_init_t centers_init = CENTERS_RANDOM, + float cb_index = 0.2 ); + }; + @endcode + - **LshIndexParams** When using a parameters object of this type the index created uses + multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search + by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd + International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007) : + @code + struct LshIndexParams : public IndexParams + { + LshIndexParams( + unsigned int table_number, + unsigned int key_size, + unsigned int multi_probe_level ); + }; + @endcode + - **AutotunedIndexParams** When passing an object of this type the index created is + automatically tuned to offer the best performance, by choosing the optimal index type + (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. : + @code + struct AutotunedIndexParams : public IndexParams + { + AutotunedIndexParams( + float target_precision = 0.9, + float build_weight = 0.01, + float memory_weight = 0, + float sample_fraction = 0.1 ); + }; + @endcode + - **SavedIndexParams** This object type is used for loading a previously saved index from the + disk. : + @code + struct SavedIndexParams : public IndexParams + { + SavedIndexParams( String filename ); + }; + @endcode + */ + GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance()); + + ~GenericIndex(); + + /** @brief Performs a K-nearest neighbor search for a given query point using the index. + + @param query The query point + @param indices Vector that will contain the indices of the K-nearest neighbors found. It must have + at least knn size. + @param dists Vector that will contain the distances to the K-nearest neighbors found. It must have + at least knn size. + @param knn Number of nearest neighbors to search for. + @param params SearchParams + */ + void knnSearch(const std::vector& query, std::vector& indices, + std::vector& dists, int knn, const ::cvflann::SearchParams& params); + void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params); + + int radiusSearch(const std::vector& query, std::vector& indices, + std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& params); + int radiusSearch(const Mat& query, Mat& indices, Mat& dists, + DistanceType radius, const ::cvflann::SearchParams& params); + + void save(String filename) { nnIndex->save(filename); } + + int veclen() const { return nnIndex->veclen(); } + + int size() const { return nnIndex->size(); } + + ::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); } + + FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); } + +private: + ::cvflann::Index* nnIndex; +}; + +//! @cond IGNORED + +#define FLANN_DISTANCE_CHECK \ + if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \ + printf("[WARNING] You are using cv::flann::Index (or cv::flann::GenericIndex) and have also changed "\ + "the distance using cvflann::set_distance_type. This is no longer working as expected "\ + "(cv::flann::Index always uses L2). You should create the index templated on the distance, "\ + "for example for L1 distance use: GenericIndex< L1 > \n"); \ + } + + +template +GenericIndex::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Distance distance) +{ + CV_Assert(dataset.type() == CvType::type()); + CV_Assert(dataset.isContinuous()); + ::cvflann::Matrix m_dataset((ElementType*)dataset.ptr(0), dataset.rows, dataset.cols); + + nnIndex = new ::cvflann::Index(m_dataset, params, distance); + + FLANN_DISTANCE_CHECK + + nnIndex->buildIndex(); +} + +template +GenericIndex::~GenericIndex() +{ + delete nnIndex; +} + +template +void GenericIndex::knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& searchParams) +{ + ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); + ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); + ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); + + FLANN_DISTANCE_CHECK + + nnIndex->knnSearch(m_query,m_indices,m_dists,knn,searchParams); +} + + +template +void GenericIndex::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams) +{ + CV_Assert(queries.type() == CvType::type()); + CV_Assert(queries.isContinuous()); + ::cvflann::Matrix m_queries((ElementType*)queries.ptr(0), queries.rows, queries.cols); + + CV_Assert(indices.type() == CV_32S); + CV_Assert(indices.isContinuous()); + ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); + + CV_Assert(dists.type() == CvType::type()); + CV_Assert(dists.isContinuous()); + ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); + + FLANN_DISTANCE_CHECK + + nnIndex->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); +} + +template +int GenericIndex::radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) +{ + ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); + ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); + ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); + + FLANN_DISTANCE_CHECK + + return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); +} + +template +int GenericIndex::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) +{ + CV_Assert(query.type() == CvType::type()); + CV_Assert(query.isContinuous()); + ::cvflann::Matrix m_query((ElementType*)query.ptr(0), query.rows, query.cols); + + CV_Assert(indices.type() == CV_32S); + CV_Assert(indices.isContinuous()); + ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); + + CV_Assert(dists.type() == CvType::type()); + CV_Assert(dists.isContinuous()); + ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); + + FLANN_DISTANCE_CHECK + + return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); +} + +//! @endcond + +/** + * @deprecated Use GenericIndex class instead + */ +template +class Index_ +{ +public: + typedef typename L2::ElementType ElementType; + typedef typename L2::ResultType DistanceType; + + FLANN_DEPRECATED Index_(const Mat& dataset, const ::cvflann::IndexParams& params) + { + printf("[WARNING] The cv::flann::Index_ class is deperecated, use cv::flann::GenericIndex instead\n"); + + CV_Assert(dataset.type() == CvType::type()); + CV_Assert(dataset.isContinuous()); + ::cvflann::Matrix m_dataset((ElementType*)dataset.ptr(0), dataset.rows, dataset.cols); + + if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) { + nnIndex_L1 = NULL; + nnIndex_L2 = new ::cvflann::Index< L2 >(m_dataset, params); + } + else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) { + nnIndex_L1 = new ::cvflann::Index< L1 >(m_dataset, params); + nnIndex_L2 = NULL; + } + else { + printf("[ERROR] cv::flann::Index_ only provides backwards compatibility for the L1 and L2 distances. " + "For other distance types you must use cv::flann::GenericIndex\n"); + CV_Assert(0); + } + if (nnIndex_L1) nnIndex_L1->buildIndex(); + if (nnIndex_L2) nnIndex_L2->buildIndex(); + } + FLANN_DEPRECATED ~Index_() + { + if (nnIndex_L1) delete nnIndex_L1; + if (nnIndex_L2) delete nnIndex_L2; + } + + FLANN_DEPRECATED void knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& searchParams) + { + ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); + ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); + ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); + + if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams); + if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams); + } + FLANN_DEPRECATED void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams) + { + CV_Assert(queries.type() == CvType::type()); + CV_Assert(queries.isContinuous()); + ::cvflann::Matrix m_queries((ElementType*)queries.ptr(0), queries.rows, queries.cols); + + CV_Assert(indices.type() == CV_32S); + CV_Assert(indices.isContinuous()); + ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); + + CV_Assert(dists.type() == CvType::type()); + CV_Assert(dists.isContinuous()); + ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); + + if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); + if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); + } + + FLANN_DEPRECATED int radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) + { + ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); + ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); + ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); + + if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); + if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); + } + + FLANN_DEPRECATED int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) + { + CV_Assert(query.type() == CvType::type()); + CV_Assert(query.isContinuous()); + ::cvflann::Matrix m_query((ElementType*)query.ptr(0), query.rows, query.cols); + + CV_Assert(indices.type() == CV_32S); + CV_Assert(indices.isContinuous()); + ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); + + CV_Assert(dists.type() == CvType::type()); + CV_Assert(dists.isContinuous()); + ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); + + if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); + if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); + } + + FLANN_DEPRECATED void save(String filename) + { + if (nnIndex_L1) nnIndex_L1->save(filename); + if (nnIndex_L2) nnIndex_L2->save(filename); + } + + FLANN_DEPRECATED int veclen() const + { + if (nnIndex_L1) return nnIndex_L1->veclen(); + if (nnIndex_L2) return nnIndex_L2->veclen(); + } + + FLANN_DEPRECATED int size() const + { + if (nnIndex_L1) return nnIndex_L1->size(); + if (nnIndex_L2) return nnIndex_L2->size(); + } + + FLANN_DEPRECATED ::cvflann::IndexParams getParameters() + { + if (nnIndex_L1) return nnIndex_L1->getParameters(); + if (nnIndex_L2) return nnIndex_L2->getParameters(); + + } + + FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() + { + if (nnIndex_L1) return nnIndex_L1->getIndexParameters(); + if (nnIndex_L2) return nnIndex_L2->getIndexParameters(); + } + +private: + // providing backwards compatibility for L2 and L1 distances (most common) + ::cvflann::Index< L2 >* nnIndex_L2; + ::cvflann::Index< L1 >* nnIndex_L1; +}; + + +/** @brief Clusters features using hierarchical k-means algorithm. + +@param features The points to be clustered. The matrix must have elements of type +Distance::ElementType. +@param centers The centers of the clusters obtained. The matrix must have type +Distance::ResultType. The number of rows in this matrix represents the number of clusters desired, +however, because of the way the cut in the hierarchical tree is chosen, the number of clusters +computed will be the highest number of the form (branching-1)\*k+1 that's lower than the number of +clusters desired, where branching is the tree's branching factor (see description of the +KMeansIndexParams). +@param params Parameters used in the construction of the hierarchical k-means tree. +@param d Distance to be used for clustering. + +The method clusters the given feature vectors by constructing a hierarchical k-means tree and +choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters +found. + */ +template +int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params, + Distance d = Distance()) +{ + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + CV_Assert(features.type() == CvType::type()); + CV_Assert(features.isContinuous()); + ::cvflann::Matrix m_features((ElementType*)features.ptr(0), features.rows, features.cols); + + CV_Assert(centers.type() == CvType::type()); + CV_Assert(centers.isContinuous()); + ::cvflann::Matrix m_centers((DistanceType*)centers.ptr(0), centers.rows, centers.cols); + + return ::cvflann::hierarchicalClustering(m_features, m_centers, params, d); +} + +/** @deprecated +*/ +template +FLANN_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params) +{ + printf("[WARNING] cv::flann::hierarchicalClustering is deprecated, use " + "cv::flann::hierarchicalClustering instead\n"); + + if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) { + return hierarchicalClustering< L2 >(features, centers, params); + } + else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) { + return hierarchicalClustering< L1 >(features, centers, params); + } + else { + printf("[ERROR] cv::flann::hierarchicalClustering only provides backwards " + "compatibility for the L1 and L2 distances. " + "For other distance types you must use cv::flann::hierarchicalClustering\n"); + CV_Assert(0); + } +} + +//! @} flann + +} } // namespace cv::flann + +#endif diff --git a/thirdparty/linux/include/opencv2/flann/all_indices.h b/thirdparty/linux/include/opencv2/flann/all_indices.h new file mode 100644 index 0000000..ff53fd8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/all_indices.h @@ -0,0 +1,155 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_ALL_INDICES_H_ +#define OPENCV_FLANN_ALL_INDICES_H_ + +#include "general.h" + +#include "nn_index.h" +#include "kdtree_index.h" +#include "kdtree_single_index.h" +#include "kmeans_index.h" +#include "composite_index.h" +#include "linear_index.h" +#include "hierarchical_clustering_index.h" +#include "lsh_index.h" +#include "autotuned_index.h" + + +namespace cvflann +{ + +template +struct index_creator +{ + static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) + { + flann_algorithm_t index_type = get_param(params, "algorithm"); + + NNIndex* nnIndex; + switch (index_type) { + case FLANN_INDEX_LINEAR: + nnIndex = new LinearIndex(dataset, params, distance); + break; + case FLANN_INDEX_KDTREE_SINGLE: + nnIndex = new KDTreeSingleIndex(dataset, params, distance); + break; + case FLANN_INDEX_KDTREE: + nnIndex = new KDTreeIndex(dataset, params, distance); + break; + case FLANN_INDEX_KMEANS: + nnIndex = new KMeansIndex(dataset, params, distance); + break; + case FLANN_INDEX_COMPOSITE: + nnIndex = new CompositeIndex(dataset, params, distance); + break; + case FLANN_INDEX_AUTOTUNED: + nnIndex = new AutotunedIndex(dataset, params, distance); + break; + case FLANN_INDEX_HIERARCHICAL: + nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); + break; + case FLANN_INDEX_LSH: + nnIndex = new LshIndex(dataset, params, distance); + break; + default: + throw FLANNException("Unknown index type"); + } + + return nnIndex; + } +}; + +template +struct index_creator +{ + static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) + { + flann_algorithm_t index_type = get_param(params, "algorithm"); + + NNIndex* nnIndex; + switch (index_type) { + case FLANN_INDEX_LINEAR: + nnIndex = new LinearIndex(dataset, params, distance); + break; + case FLANN_INDEX_KMEANS: + nnIndex = new KMeansIndex(dataset, params, distance); + break; + case FLANN_INDEX_HIERARCHICAL: + nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); + break; + case FLANN_INDEX_LSH: + nnIndex = new LshIndex(dataset, params, distance); + break; + default: + throw FLANNException("Unknown index type"); + } + + return nnIndex; + } +}; + +template +struct index_creator +{ + static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) + { + flann_algorithm_t index_type = get_param(params, "algorithm"); + + NNIndex* nnIndex; + switch (index_type) { + case FLANN_INDEX_LINEAR: + nnIndex = new LinearIndex(dataset, params, distance); + break; + case FLANN_INDEX_HIERARCHICAL: + nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); + break; + case FLANN_INDEX_LSH: + nnIndex = new LshIndex(dataset, params, distance); + break; + default: + throw FLANNException("Unknown index type"); + } + + return nnIndex; + } +}; + +template +NNIndex* create_index_by_type(const Matrix& dataset, const IndexParams& params, const Distance& distance) +{ + return index_creator::create(dataset, params,distance); +} + +} + +#endif /* OPENCV_FLANN_ALL_INDICES_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/allocator.h b/thirdparty/linux/include/opencv2/flann/allocator.h new file mode 100644 index 0000000..26091d0 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/allocator.h @@ -0,0 +1,188 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_ALLOCATOR_H_ +#define OPENCV_FLANN_ALLOCATOR_H_ + +#include +#include + + +namespace cvflann +{ + +/** + * Allocates (using C's malloc) a generic type T. + * + * Params: + * count = number of instances to allocate. + * Returns: pointer (of type T*) to memory buffer + */ +template +T* allocate(size_t count = 1) +{ + T* mem = (T*) ::malloc(sizeof(T)*count); + return mem; +} + + +/** + * Pooled storage allocator + * + * The following routines allow for the efficient allocation of storage in + * small chunks from a specified pool. Rather than allowing each structure + * to be freed individually, an entire pool of storage is freed at once. + * This method has two advantages over just using malloc() and free(). First, + * it is far more efficient for allocating small objects, as there is + * no overhead for remembering all the information needed to free each + * object or consolidating fragmented memory. Second, the decision about + * how long to keep an object is made at the time of allocation, and there + * is no need to track down all the objects to free them. + * + */ + +const size_t WORDSIZE=16; +const size_t BLOCKSIZE=8192; + +class PooledAllocator +{ + /* We maintain memory alignment to word boundaries by requiring that all + allocations be in multiples of the machine wordsize. */ + /* Size of machine word in bytes. Must be power of 2. */ + /* Minimum number of bytes requested at a time from the system. Must be multiple of WORDSIZE. */ + + + int remaining; /* Number of bytes left in current block of storage. */ + void* base; /* Pointer to base of current block of storage. */ + void* loc; /* Current location in block to next allocate memory. */ + int blocksize; + + +public: + int usedMemory; + int wastedMemory; + + /** + Default constructor. Initializes a new pool. + */ + PooledAllocator(int blockSize = BLOCKSIZE) + { + blocksize = blockSize; + remaining = 0; + base = NULL; + + usedMemory = 0; + wastedMemory = 0; + } + + /** + * Destructor. Frees all the memory allocated in this pool. + */ + ~PooledAllocator() + { + void* prev; + + while (base != NULL) { + prev = *((void**) base); /* Get pointer to prev block. */ + ::free(base); + base = prev; + } + } + + /** + * Returns a pointer to a piece of new memory of the given size in bytes + * allocated from the pool. + */ + void* allocateMemory(int size) + { + int blockSize; + + /* Round size up to a multiple of wordsize. The following expression + only works for WORDSIZE that is a power of 2, by masking last bits of + incremented size to zero. + */ + size = (size + (WORDSIZE - 1)) & ~(WORDSIZE - 1); + + /* Check whether a new block must be allocated. Note that the first word + of a block is reserved for a pointer to the previous block. + */ + if (size > remaining) { + + wastedMemory += remaining; + + /* Allocate new storage. */ + blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ? + size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE; + + // use the standard C malloc to allocate memory + void* m = ::malloc(blockSize); + if (!m) { + fprintf(stderr,"Failed to allocate memory.\n"); + return NULL; + } + + /* Fill first word of new block with pointer to previous block. */ + ((void**) m)[0] = base; + base = m; + + int shift = 0; + //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1); + + remaining = blockSize - sizeof(void*) - shift; + loc = ((char*)m + sizeof(void*) + shift); + } + void* rloc = loc; + loc = (char*)loc + size; + remaining -= size; + + usedMemory += size; + + return rloc; + } + + /** + * Allocates (using this pool) a generic type T. + * + * Params: + * count = number of instances to allocate. + * Returns: pointer (of type T*) to memory buffer + */ + template + T* allocate(size_t count = 1) + { + T* mem = (T*) this->allocateMemory((int)(sizeof(T)*count)); + return mem; + } + +}; + +} + +#endif //OPENCV_FLANN_ALLOCATOR_H_ diff --git a/thirdparty/linux/include/opencv2/flann/any.h b/thirdparty/linux/include/opencv2/flann/any.h new file mode 100644 index 0000000..bfe06c8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/any.h @@ -0,0 +1,324 @@ +#ifndef OPENCV_FLANN_ANY_H_ +#define OPENCV_FLANN_ANY_H_ +/* + * (C) Copyright Christopher Diggins 2005-2011 + * (C) Copyright Pablo Aguilar 2005 + * (C) Copyright Kevlin Henney 2001 + * + * Distributed under the Boost Software License, Version 1.0. (See + * accompanying file LICENSE_1_0.txt or copy at + * http://www.boost.org/LICENSE_1_0.txt + * + * Adapted for FLANN by Marius Muja + */ + +#include "defines.h" +#include +#include +#include + +namespace cvflann +{ + +namespace anyimpl +{ + +struct bad_any_cast +{ +}; + +struct empty_any +{ +}; + +inline std::ostream& operator <<(std::ostream& out, const empty_any&) +{ + out << "[empty_any]"; + return out; +} + +struct base_any_policy +{ + virtual void static_delete(void** x) = 0; + virtual void copy_from_value(void const* src, void** dest) = 0; + virtual void clone(void* const* src, void** dest) = 0; + virtual void move(void* const* src, void** dest) = 0; + virtual void* get_value(void** src) = 0; + virtual const void* get_value(void* const * src) = 0; + virtual ::size_t get_size() = 0; + virtual const std::type_info& type() = 0; + virtual void print(std::ostream& out, void* const* src) = 0; + virtual ~base_any_policy() {} +}; + +template +struct typed_base_any_policy : base_any_policy +{ + virtual ::size_t get_size() { return sizeof(T); } + virtual const std::type_info& type() { return typeid(T); } + +}; + +template +struct small_any_policy : typed_base_any_policy +{ + virtual void static_delete(void**) { } + virtual void copy_from_value(void const* src, void** dest) + { + new (dest) T(* reinterpret_cast(src)); + } + virtual void clone(void* const* src, void** dest) { *dest = *src; } + virtual void move(void* const* src, void** dest) { *dest = *src; } + virtual void* get_value(void** src) { return reinterpret_cast(src); } + virtual const void* get_value(void* const * src) { return reinterpret_cast(src); } + virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast(src); } +}; + +template +struct big_any_policy : typed_base_any_policy +{ + virtual void static_delete(void** x) + { + if (* x) delete (* reinterpret_cast(x)); + *x = NULL; + } + virtual void copy_from_value(void const* src, void** dest) + { + *dest = new T(*reinterpret_cast(src)); + } + virtual void clone(void* const* src, void** dest) + { + *dest = new T(**reinterpret_cast(src)); + } + virtual void move(void* const* src, void** dest) + { + (*reinterpret_cast(dest))->~T(); + **reinterpret_cast(dest) = **reinterpret_cast(src); + } + virtual void* get_value(void** src) { return *src; } + virtual const void* get_value(void* const * src) { return *src; } + virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast(*src); } +}; + +template<> inline void big_any_policy::print(std::ostream& out, void* const* src) +{ + out << int(*reinterpret_cast(*src)); +} + +template<> inline void big_any_policy::print(std::ostream& out, void* const* src) +{ + out << int(*reinterpret_cast(*src)); +} + +template<> inline void big_any_policy::print(std::ostream& out, void* const* src) +{ + out << (*reinterpret_cast(*src)).c_str(); +} + +template +struct choose_policy +{ + typedef big_any_policy type; +}; + +template +struct choose_policy +{ + typedef small_any_policy type; +}; + +struct any; + +/// Choosing the policy for an any type is illegal, but should never happen. +/// This is designed to throw a compiler error. +template<> +struct choose_policy +{ + typedef void type; +}; + +/// Specializations for small types. +#define SMALL_POLICY(TYPE) \ + template<> \ + struct choose_policy { typedef small_any_policy type; \ + } + +SMALL_POLICY(signed char); +SMALL_POLICY(unsigned char); +SMALL_POLICY(signed short); +SMALL_POLICY(unsigned short); +SMALL_POLICY(signed int); +SMALL_POLICY(unsigned int); +SMALL_POLICY(signed long); +SMALL_POLICY(unsigned long); +SMALL_POLICY(float); +SMALL_POLICY(bool); + +#undef SMALL_POLICY + +template +class SinglePolicy +{ + SinglePolicy(); + SinglePolicy(const SinglePolicy& other); + SinglePolicy& operator=(const SinglePolicy& other); + +public: + static base_any_policy* get_policy(); + +private: + static typename choose_policy::type policy; +}; + +template +typename choose_policy::type SinglePolicy::policy; + +/// This function will return a different policy for each type. +template +inline base_any_policy* SinglePolicy::get_policy() { return &policy; } + +} // namespace anyimpl + +struct any +{ +private: + // fields + anyimpl::base_any_policy* policy; + void* object; + +public: + /// Initializing constructor. + template + any(const T& x) + : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) + { + assign(x); + } + + /// Empty constructor. + any() + : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) + { } + + /// Special initializing constructor for string literals. + any(const char* x) + : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) + { + assign(x); + } + + /// Copy constructor. + any(const any& x) + : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) + { + assign(x); + } + + /// Destructor. + ~any() + { + policy->static_delete(&object); + } + + /// Assignment function from another any. + any& assign(const any& x) + { + reset(); + policy = x.policy; + policy->clone(&x.object, &object); + return *this; + } + + /// Assignment function. + template + any& assign(const T& x) + { + reset(); + policy = anyimpl::SinglePolicy::get_policy(); + policy->copy_from_value(&x, &object); + return *this; + } + + /// Assignment operator. + template + any& operator=(const T& x) + { + return assign(x); + } + + /// Assignment operator, specialed for literal strings. + /// They have types like const char [6] which don't work as expected. + any& operator=(const char* x) + { + return assign(x); + } + + /// Utility functions + any& swap(any& x) + { + std::swap(policy, x.policy); + std::swap(object, x.object); + return *this; + } + + /// Cast operator. You can only cast to the original type. + template + T& cast() + { + if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast(); + T* r = reinterpret_cast(policy->get_value(&object)); + return *r; + } + + /// Cast operator. You can only cast to the original type. + template + const T& cast() const + { + if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast(); + const T* r = reinterpret_cast(policy->get_value(&object)); + return *r; + } + + /// Returns true if the any contains no value. + bool empty() const + { + return policy->type() == typeid(anyimpl::empty_any); + } + + /// Frees any allocated memory, and sets the value to NULL. + void reset() + { + policy->static_delete(&object); + policy = anyimpl::SinglePolicy::get_policy(); + } + + /// Returns true if the two types are the same. + bool compatible(const any& x) const + { + return policy->type() == x.policy->type(); + } + + /// Returns if the type is compatible with the policy + template + bool has_type() + { + return policy->type() == typeid(T); + } + + const std::type_info& type() const + { + return policy->type(); + } + + friend std::ostream& operator <<(std::ostream& out, const any& any_val); +}; + +inline std::ostream& operator <<(std::ostream& out, const any& any_val) +{ + any_val.policy->print(out,&any_val.object); + return out; +} + +} + +#endif // OPENCV_FLANN_ANY_H_ diff --git a/thirdparty/linux/include/opencv2/flann/autotuned_index.h b/thirdparty/linux/include/opencv2/flann/autotuned_index.h new file mode 100644 index 0000000..6ffb929 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/autotuned_index.h @@ -0,0 +1,588 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ +#ifndef OPENCV_FLANN_AUTOTUNED_INDEX_H_ +#define OPENCV_FLANN_AUTOTUNED_INDEX_H_ + +#include "general.h" +#include "nn_index.h" +#include "ground_truth.h" +#include "index_testing.h" +#include "sampling.h" +#include "kdtree_index.h" +#include "kdtree_single_index.h" +#include "kmeans_index.h" +#include "composite_index.h" +#include "linear_index.h" +#include "logger.h" + +namespace cvflann +{ + +template +NNIndex* create_index_by_type(const Matrix& dataset, const IndexParams& params, const Distance& distance); + + +struct AutotunedIndexParams : public IndexParams +{ + AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1) + { + (*this)["algorithm"] = FLANN_INDEX_AUTOTUNED; + // precision desired (used for autotuning, -1 otherwise) + (*this)["target_precision"] = target_precision; + // build tree time weighting factor + (*this)["build_weight"] = build_weight; + // index memory weighting factor + (*this)["memory_weight"] = memory_weight; + // what fraction of the dataset to use for autotuning + (*this)["sample_fraction"] = sample_fraction; + } +}; + + +template +class AutotunedIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + AutotunedIndex(const Matrix& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) : + dataset_(inputData), distance_(d) + { + target_precision_ = get_param(params, "target_precision",0.8f); + build_weight_ = get_param(params,"build_weight", 0.01f); + memory_weight_ = get_param(params, "memory_weight", 0.0f); + sample_fraction_ = get_param(params,"sample_fraction", 0.1f); + bestIndex_ = NULL; + } + + AutotunedIndex(const AutotunedIndex&); + AutotunedIndex& operator=(const AutotunedIndex&); + + virtual ~AutotunedIndex() + { + if (bestIndex_ != NULL) { + delete bestIndex_; + bestIndex_ = NULL; + } + } + + /** + * Method responsible with building the index. + */ + virtual void buildIndex() + { + std::ostringstream stream; + bestParams_ = estimateBuildParams(); + print_params(bestParams_, stream); + Logger::info("----------------------------------------------------\n"); + Logger::info("Autotuned parameters:\n"); + Logger::info("%s", stream.str().c_str()); + Logger::info("----------------------------------------------------\n"); + + bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_); + bestIndex_->buildIndex(); + speedup_ = estimateSearchParams(bestSearchParams_); + stream.str(std::string()); + print_params(bestSearchParams_, stream); + Logger::info("----------------------------------------------------\n"); + Logger::info("Search parameters:\n"); + Logger::info("%s", stream.str().c_str()); + Logger::info("----------------------------------------------------\n"); + } + + /** + * Saves the index to a stream + */ + virtual void saveIndex(FILE* stream) + { + save_value(stream, (int)bestIndex_->getType()); + bestIndex_->saveIndex(stream); + save_value(stream, get_param(bestSearchParams_, "checks")); + } + + /** + * Loads the index from a stream + */ + virtual void loadIndex(FILE* stream) + { + int index_type; + + load_value(stream, index_type); + IndexParams params; + params["algorithm"] = (flann_algorithm_t)index_type; + bestIndex_ = create_index_by_type(dataset_, params, distance_); + bestIndex_->loadIndex(stream); + int checks; + load_value(stream, checks); + bestSearchParams_["checks"] = checks; + } + + /** + * Method that searches for nearest-neighbors + */ + virtual void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) + { + int checks = get_param(searchParams,"checks",FLANN_CHECKS_AUTOTUNED); + if (checks == FLANN_CHECKS_AUTOTUNED) { + bestIndex_->findNeighbors(result, vec, bestSearchParams_); + } + else { + bestIndex_->findNeighbors(result, vec, searchParams); + } + } + + + IndexParams getParameters() const + { + return bestIndex_->getParameters(); + } + + SearchParams getSearchParameters() const + { + return bestSearchParams_; + } + + float getSpeedup() const + { + return speedup_; + } + + + /** + * Number of features in this index. + */ + virtual size_t size() const + { + return bestIndex_->size(); + } + + /** + * The length of each vector in this index. + */ + virtual size_t veclen() const + { + return bestIndex_->veclen(); + } + + /** + * The amount of memory (in bytes) this index uses. + */ + virtual int usedMemory() const + { + return bestIndex_->usedMemory(); + } + + /** + * Algorithm name + */ + virtual flann_algorithm_t getType() const + { + return FLANN_INDEX_AUTOTUNED; + } + +private: + + struct CostData + { + float searchTimeCost; + float buildTimeCost; + float memoryCost; + float totalCost; + IndexParams params; + }; + + void evaluate_kmeans(CostData& cost) + { + StartStopTimer t; + int checks; + const int nn = 1; + + Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n", + get_param(cost.params,"iterations"), + get_param(cost.params,"branching")); + KMeansIndex kmeans(sampledDataset_, cost.params, distance_); + // measure index build time + t.start(); + kmeans.buildIndex(); + t.stop(); + float buildTime = (float)t.value; + + // measure search time + float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); + + float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float)); + cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory; + cost.searchTimeCost = searchTime; + cost.buildTimeCost = buildTime; + Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_); + } + + + void evaluate_kdtree(CostData& cost) + { + StartStopTimer t; + int checks; + const int nn = 1; + + Logger::info("KDTree using params: trees=%d\n", get_param(cost.params,"trees")); + KDTreeIndex kdtree(sampledDataset_, cost.params, distance_); + + t.start(); + kdtree.buildIndex(); + t.stop(); + float buildTime = (float)t.value; + + //measure search time + float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); + + float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float)); + cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory; + cost.searchTimeCost = searchTime; + cost.buildTimeCost = buildTime; + Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime); + } + + + // struct KMeansSimpleDownhillFunctor { + // + // Autotune& autotuner; + // KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {} + // + // float operator()(int* params) { + // + // float maxFloat = numeric_limits::max(); + // + // if (params[0]<2) return maxFloat; + // if (params[1]<0) return maxFloat; + // + // CostData c; + // c.params["algorithm"] = KMEANS; + // c.params["centers-init"] = CENTERS_RANDOM; + // c.params["branching"] = params[0]; + // c.params["max-iterations"] = params[1]; + // + // autotuner.evaluate_kmeans(c); + // + // return c.timeCost; + // + // } + // }; + // + // struct KDTreeSimpleDownhillFunctor { + // + // Autotune& autotuner; + // KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {} + // + // float operator()(int* params) { + // float maxFloat = numeric_limits::max(); + // + // if (params[0]<1) return maxFloat; + // + // CostData c; + // c.params["algorithm"] = KDTREE; + // c.params["trees"] = params[0]; + // + // autotuner.evaluate_kdtree(c); + // + // return c.timeCost; + // + // } + // }; + + + + void optimizeKMeans(std::vector& costs) + { + Logger::info("KMEANS, Step 1: Exploring parameter space\n"); + + // explore kmeans parameters space using combinations of the parameters below + int maxIterations[] = { 1, 5, 10, 15 }; + int branchingFactors[] = { 16, 32, 64, 128, 256 }; + + int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors); + costs.reserve(costs.size() + kmeansParamSpaceSize); + + // evaluate kmeans for all parameter combinations + for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) { + for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) { + CostData cost; + cost.params["algorithm"] = FLANN_INDEX_KMEANS; + cost.params["centers_init"] = FLANN_CENTERS_RANDOM; + cost.params["iterations"] = maxIterations[i]; + cost.params["branching"] = branchingFactors[j]; + + evaluate_kmeans(cost); + costs.push_back(cost); + } + } + + // Logger::info("KMEANS, Step 2: simplex-downhill optimization\n"); + // + // const int n = 2; + // // choose initial simplex points as the best parameters so far + // int kmeansNMPoints[n*(n+1)]; + // float kmeansVals[n+1]; + // for (int i=0;i& costs) + { + Logger::info("KD-TREE, Step 1: Exploring parameter space\n"); + + // explore kd-tree parameters space using the parameters below + int testTrees[] = { 1, 4, 8, 16, 32 }; + + // evaluate kdtree for all parameter combinations + for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) { + CostData cost; + cost.params["algorithm"] = FLANN_INDEX_KDTREE; + cost.params["trees"] = testTrees[i]; + + evaluate_kdtree(cost); + costs.push_back(cost); + } + + // Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n"); + // + // const int n = 1; + // // choose initial simplex points as the best parameters so far + // int kdtreeNMPoints[n*(n+1)]; + // float kdtreeVals[n+1]; + // for (int i=0;i costs; + + int sampleSize = int(sample_fraction_ * dataset_.rows); + int testSampleSize = std::min(sampleSize / 10, 1000); + + Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_); + + // For a very small dataset, it makes no sense to build any fancy index, just + // use linear search + if (testSampleSize < 10) { + Logger::info("Choosing linear, dataset too small\n"); + return LinearIndexParams(); + } + + // We use a fraction of the original dataset to speedup the autotune algorithm + sampledDataset_ = random_sample(dataset_, sampleSize); + // We use a cross-validation approach, first we sample a testset from the dataset + testDataset_ = random_sample(sampledDataset_, testSampleSize, true); + + // We compute the ground truth using linear search + Logger::info("Computing ground truth... \n"); + gt_matches_ = Matrix(new int[testDataset_.rows], testDataset_.rows, 1); + StartStopTimer t; + t.start(); + compute_ground_truth(sampledDataset_, testDataset_, gt_matches_, 0, distance_); + t.stop(); + + CostData linear_cost; + linear_cost.searchTimeCost = (float)t.value; + linear_cost.buildTimeCost = 0; + linear_cost.memoryCost = 0; + linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR; + + costs.push_back(linear_cost); + + // Start parameter autotune process + Logger::info("Autotuning parameters...\n"); + + optimizeKMeans(costs); + optimizeKDTree(costs); + + float bestTimeCost = costs[0].searchTimeCost; + for (size_t i = 0; i < costs.size(); ++i) { + float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost; + if (timeCost < bestTimeCost) { + bestTimeCost = timeCost; + } + } + + float bestCost = costs[0].searchTimeCost / bestTimeCost; + IndexParams bestParams = costs[0].params; + if (bestTimeCost > 0) { + for (size_t i = 0; i < costs.size(); ++i) { + float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost + + memory_weight_ * costs[i].memoryCost; + if (crtCost < bestCost) { + bestCost = crtCost; + bestParams = costs[i].params; + } + } + } + + delete[] gt_matches_.data; + delete[] testDataset_.data; + delete[] sampledDataset_.data; + + return bestParams; + } + + + + /** + * Estimates the search time parameters needed to get the desired precision. + * Precondition: the index is built + * Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search. + */ + float estimateSearchParams(SearchParams& searchParams) + { + const int nn = 1; + const size_t SAMPLE_COUNT = 1000; + + assert(bestIndex_ != NULL); // must have a valid index + + float speedup = 0; + + int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT); + if (samples > 0) { + Matrix testDataset = random_sample(dataset_, samples); + + Logger::info("Computing ground truth\n"); + + // we need to compute the ground truth first + Matrix gt_matches(new int[testDataset.rows], testDataset.rows, 1); + StartStopTimer t; + t.start(); + compute_ground_truth(dataset_, testDataset, gt_matches, 1, distance_); + t.stop(); + float linear = (float)t.value; + + int checks; + Logger::info("Estimating number of checks\n"); + + float searchTime; + float cb_index; + if (bestIndex_->getType() == FLANN_INDEX_KMEANS) { + Logger::info("KMeans algorithm, estimating cluster border factor\n"); + KMeansIndex* kmeans = (KMeansIndex*)bestIndex_; + float bestSearchTime = -1; + float best_cb_index = -1; + int best_checks = -1; + for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) { + kmeans->set_cb_index(cb_index); + searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); + if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) { + bestSearchTime = searchTime; + best_cb_index = cb_index; + best_checks = checks; + } + } + searchTime = bestSearchTime; + cb_index = best_cb_index; + checks = best_checks; + + kmeans->set_cb_index(best_cb_index); + Logger::info("Optimum cb_index: %g\n", cb_index); + bestParams_["cb_index"] = cb_index; + } + else { + searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); + } + + Logger::info("Required number of checks: %d \n", checks); + searchParams["checks"] = checks; + + speedup = linear / searchTime; + + delete[] gt_matches.data; + delete[] testDataset.data; + } + + return speedup; + } + +private: + NNIndex* bestIndex_; + + IndexParams bestParams_; + SearchParams bestSearchParams_; + + Matrix sampledDataset_; + Matrix testDataset_; + Matrix gt_matches_; + + float speedup_; + + /** + * The dataset used by this index + */ + const Matrix dataset_; + + /** + * Index parameters + */ + float target_precision_; + float build_weight_; + float memory_weight_; + float sample_fraction_; + + Distance distance_; + + +}; +} + +#endif /* OPENCV_FLANN_AUTOTUNED_INDEX_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/composite_index.h b/thirdparty/linux/include/opencv2/flann/composite_index.h new file mode 100644 index 0000000..527ca1a --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/composite_index.h @@ -0,0 +1,194 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_ +#define OPENCV_FLANN_COMPOSITE_INDEX_H_ + +#include "general.h" +#include "nn_index.h" +#include "kdtree_index.h" +#include "kmeans_index.h" + +namespace cvflann +{ + +/** + * Index parameters for the CompositeIndex. + */ +struct CompositeIndexParams : public IndexParams +{ + CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, + flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 ) + { + (*this)["algorithm"] = FLANN_INDEX_KMEANS; + // number of randomized trees to use (for kdtree) + (*this)["trees"] = trees; + // branching factor + (*this)["branching"] = branching; + // max iterations to perform in one kmeans clustering (kmeans tree) + (*this)["iterations"] = iterations; + // algorithm used for picking the initial cluster centers for kmeans tree + (*this)["centers_init"] = centers_init; + // cluster boundary index. Used when searching the kmeans tree + (*this)["cb_index"] = cb_index; + } +}; + + +/** + * This index builds a kd-tree index and a k-means index and performs nearest + * neighbour search both indexes. This gives a slight boost in search performance + * as some of the neighbours that are missed by one index are found by the other. + */ +template +class CompositeIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + /** + * Index constructor + * @param inputData dataset containing the points to index + * @param params Index parameters + * @param d Distance functor + * @return + */ + CompositeIndex(const Matrix& inputData, const IndexParams& params = CompositeIndexParams(), + Distance d = Distance()) : index_params_(params) + { + kdtree_index_ = new KDTreeIndex(inputData, params, d); + kmeans_index_ = new KMeansIndex(inputData, params, d); + + } + + CompositeIndex(const CompositeIndex&); + CompositeIndex& operator=(const CompositeIndex&); + + virtual ~CompositeIndex() + { + delete kdtree_index_; + delete kmeans_index_; + } + + /** + * @return The index type + */ + flann_algorithm_t getType() const + { + return FLANN_INDEX_COMPOSITE; + } + + /** + * @return Size of the index + */ + size_t size() const + { + return kdtree_index_->size(); + } + + /** + * \returns The dimensionality of the features in this index. + */ + size_t veclen() const + { + return kdtree_index_->veclen(); + } + + /** + * \returns The amount of memory (in bytes) used by the index. + */ + int usedMemory() const + { + return kmeans_index_->usedMemory() + kdtree_index_->usedMemory(); + } + + /** + * \brief Builds the index + */ + void buildIndex() + { + Logger::info("Building kmeans tree...\n"); + kmeans_index_->buildIndex(); + Logger::info("Building kdtree tree...\n"); + kdtree_index_->buildIndex(); + } + + /** + * \brief Saves the index to a stream + * \param stream The stream to save the index to + */ + void saveIndex(FILE* stream) + { + kmeans_index_->saveIndex(stream); + kdtree_index_->saveIndex(stream); + } + + /** + * \brief Loads the index from a stream + * \param stream The stream from which the index is loaded + */ + void loadIndex(FILE* stream) + { + kmeans_index_->loadIndex(stream); + kdtree_index_->loadIndex(stream); + } + + /** + * \returns The index parameters + */ + IndexParams getParameters() const + { + return index_params_; + } + + /** + * \brief Method that searches for nearest-neighbours + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) + { + kmeans_index_->findNeighbors(result, vec, searchParams); + kdtree_index_->findNeighbors(result, vec, searchParams); + } + +private: + /** The k-means index */ + KMeansIndex* kmeans_index_; + + /** The kd-tree index */ + KDTreeIndex* kdtree_index_; + + /** The index parameters */ + const IndexParams index_params_; +}; + +} + +#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_ diff --git a/thirdparty/linux/include/opencv2/flann/config.h b/thirdparty/linux/include/opencv2/flann/config.h new file mode 100644 index 0000000..56832fd --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/config.h @@ -0,0 +1,38 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_CONFIG_H_ +#define OPENCV_FLANN_CONFIG_H_ + +#ifdef FLANN_VERSION_ +#undef FLANN_VERSION_ +#endif +#define FLANN_VERSION_ "1.6.10" + +#endif /* OPENCV_FLANN_CONFIG_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/defines.h b/thirdparty/linux/include/opencv2/flann/defines.h new file mode 100644 index 0000000..f0264f7 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/defines.h @@ -0,0 +1,177 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_DEFINES_H_ +#define OPENCV_FLANN_DEFINES_H_ + +#include "config.h" + +#ifdef FLANN_EXPORT +#undef FLANN_EXPORT +#endif +#ifdef WIN32 +/* win32 dll export/import directives */ + #ifdef FLANN_EXPORTS + #define FLANN_EXPORT __declspec(dllexport) + #elif defined(FLANN_STATIC) + #define FLANN_EXPORT + #else + #define FLANN_EXPORT __declspec(dllimport) + #endif +#else +/* unix needs nothing */ + #define FLANN_EXPORT +#endif + + +#ifdef FLANN_DEPRECATED +#undef FLANN_DEPRECATED +#endif +#ifdef __GNUC__ +#define FLANN_DEPRECATED __attribute__ ((deprecated)) +#elif defined(_MSC_VER) +#define FLANN_DEPRECATED __declspec(deprecated) +#else +#pragma message("WARNING: You need to implement FLANN_DEPRECATED for this compiler") +#define FLANN_DEPRECATED +#endif + + +#undef FLANN_PLATFORM_32_BIT +#undef FLANN_PLATFORM_64_BIT +#if defined __amd64__ || defined __x86_64__ || defined _WIN64 || defined _M_X64 +#define FLANN_PLATFORM_64_BIT +#else +#define FLANN_PLATFORM_32_BIT +#endif + + +#undef FLANN_ARRAY_LEN +#define FLANN_ARRAY_LEN(a) (sizeof(a)/sizeof(a[0])) + +namespace cvflann { + +/* Nearest neighbour index algorithms */ +enum flann_algorithm_t +{ + FLANN_INDEX_LINEAR = 0, + FLANN_INDEX_KDTREE = 1, + FLANN_INDEX_KMEANS = 2, + FLANN_INDEX_COMPOSITE = 3, + FLANN_INDEX_KDTREE_SINGLE = 4, + FLANN_INDEX_HIERARCHICAL = 5, + FLANN_INDEX_LSH = 6, + FLANN_INDEX_SAVED = 254, + FLANN_INDEX_AUTOTUNED = 255, + + // deprecated constants, should use the FLANN_INDEX_* ones instead + LINEAR = 0, + KDTREE = 1, + KMEANS = 2, + COMPOSITE = 3, + KDTREE_SINGLE = 4, + SAVED = 254, + AUTOTUNED = 255 +}; + + + +enum flann_centers_init_t +{ + FLANN_CENTERS_RANDOM = 0, + FLANN_CENTERS_GONZALES = 1, + FLANN_CENTERS_KMEANSPP = 2, + FLANN_CENTERS_GROUPWISE = 3, + + // deprecated constants, should use the FLANN_CENTERS_* ones instead + CENTERS_RANDOM = 0, + CENTERS_GONZALES = 1, + CENTERS_KMEANSPP = 2 +}; + +enum flann_log_level_t +{ + FLANN_LOG_NONE = 0, + FLANN_LOG_FATAL = 1, + FLANN_LOG_ERROR = 2, + FLANN_LOG_WARN = 3, + FLANN_LOG_INFO = 4 +}; + +enum flann_distance_t +{ + FLANN_DIST_EUCLIDEAN = 1, + FLANN_DIST_L2 = 1, + FLANN_DIST_MANHATTAN = 2, + FLANN_DIST_L1 = 2, + FLANN_DIST_MINKOWSKI = 3, + FLANN_DIST_MAX = 4, + FLANN_DIST_HIST_INTERSECT = 5, + FLANN_DIST_HELLINGER = 6, + FLANN_DIST_CHI_SQUARE = 7, + FLANN_DIST_CS = 7, + FLANN_DIST_KULLBACK_LEIBLER = 8, + FLANN_DIST_KL = 8, + FLANN_DIST_HAMMING = 9, + + // deprecated constants, should use the FLANN_DIST_* ones instead + EUCLIDEAN = 1, + MANHATTAN = 2, + MINKOWSKI = 3, + MAX_DIST = 4, + HIST_INTERSECT = 5, + HELLINGER = 6, + CS = 7, + KL = 8, + KULLBACK_LEIBLER = 8 +}; + +enum flann_datatype_t +{ + FLANN_INT8 = 0, + FLANN_INT16 = 1, + FLANN_INT32 = 2, + FLANN_INT64 = 3, + FLANN_UINT8 = 4, + FLANN_UINT16 = 5, + FLANN_UINT32 = 6, + FLANN_UINT64 = 7, + FLANN_FLOAT32 = 8, + FLANN_FLOAT64 = 9 +}; + +enum +{ + FLANN_CHECKS_UNLIMITED = -1, + FLANN_CHECKS_AUTOTUNED = -2 +}; + +} + +#endif /* OPENCV_FLANN_DEFINES_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/dist.h b/thirdparty/linux/include/opencv2/flann/dist.h new file mode 100644 index 0000000..9dbe527 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/dist.h @@ -0,0 +1,905 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_DIST_H_ +#define OPENCV_FLANN_DIST_H_ + +#include +#include +#include +#ifdef _MSC_VER +typedef unsigned __int32 uint32_t; +typedef unsigned __int64 uint64_t; +#else +#include +#endif + +#include "defines.h" + +#if (defined WIN32 || defined _WIN32) && defined(_M_ARM) +# include +#endif + +#ifdef __ARM_NEON__ +# include "arm_neon.h" +#endif + +namespace cvflann +{ + +template +inline T abs(T x) { return (x<0) ? -x : x; } + +template<> +inline int abs(int x) { return ::abs(x); } + +template<> +inline float abs(float x) { return fabsf(x); } + +template<> +inline double abs(double x) { return fabs(x); } + +template +struct Accumulator { typedef T Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; + +#undef True +#undef False + +class True +{ +}; + +class False +{ +}; + + +/** + * Squared Euclidean distance functor. + * + * This is the simpler, unrolled version. This is preferable for + * very low dimensionality data (eg 3D points) + */ +template +struct L2_Simple +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const + { + ResultType result = ResultType(); + ResultType diff; + for(size_t i = 0; i < size; ++i ) { + diff = *a++ - *b++; + result += diff*diff; + } + return result; + } + + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return (a-b)*(a-b); + } +}; + + + +/** + * Squared Euclidean distance functor, optimized version + */ +template +struct L2 +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the squared Euclidean distance between two vectors. + * + * This is highly optimised, with loop unrolling, as it is one + * of the most expensive inner loops. + * + * The computation of squared root at the end is omitted for + * efficiency. + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = (ResultType)(a[0] - b[0]); + diff1 = (ResultType)(a[1] - b[1]); + diff2 = (ResultType)(a[2] - b[2]); + diff3 = (ResultType)(a[3] - b[3]); + result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + a += 4; + b += 4; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + diff0 = (ResultType)(*a++ - *b++); + result += diff0 * diff0; + } + return result; + } + + /** + * Partial euclidean distance, using just one dimension. This is used by the + * kd-tree when computing partial distances while traversing the tree. + * + * Squared root is omitted for efficiency. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return (a-b)*(a-b); + } +}; + + +/* + * Manhattan distance functor, optimized version + */ +template +struct L1 +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the Manhattan (L_1) distance between two vectors. + * + * This is highly optimised, with loop unrolling, as it is one + * of the most expensive inner loops. + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = (ResultType)abs(a[0] - b[0]); + diff1 = (ResultType)abs(a[1] - b[1]); + diff2 = (ResultType)abs(a[2] - b[2]); + diff3 = (ResultType)abs(a[3] - b[3]); + result += diff0 + diff1 + diff2 + diff3; + a += 4; + b += 4; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + diff0 = (ResultType)abs(*a++ - *b++); + result += diff0; + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return abs(a-b); + } +}; + + + +template +struct MinkowskiDistance +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + int order; + + MinkowskiDistance(int order_) : order(order_) {} + + /** + * Compute the Minkowsky (L_p) distance between two vectors. + * + * This is highly optimised, with loop unrolling, as it is one + * of the most expensive inner loops. + * + * The computation of squared root at the end is omitted for + * efficiency. + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = (ResultType)abs(a[0] - b[0]); + diff1 = (ResultType)abs(a[1] - b[1]); + diff2 = (ResultType)abs(a[2] - b[2]); + diff3 = (ResultType)abs(a[3] - b[3]); + result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order); + a += 4; + b += 4; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + diff0 = (ResultType)abs(*a++ - *b++); + result += pow(diff0,order); + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return pow(static_cast(abs(a-b)),order); + } +}; + + + +template +struct MaxDistance +{ + typedef False is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the max distance (L_infinity) between two vectors. + * + * This distance is not a valid kdtree distance, it's not dimensionwise additive. + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = abs(a[0] - b[0]); + diff1 = abs(a[1] - b[1]); + diff2 = abs(a[2] - b[2]); + diff3 = abs(a[3] - b[3]); + if (diff0>result) {result = diff0; } + if (diff1>result) {result = diff1; } + if (diff2>result) {result = diff2; } + if (diff3>result) {result = diff3; } + a += 4; + b += 4; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + diff0 = abs(*a++ - *b++); + result = (diff0>result) ? diff0 : result; + } + return result; + } + + /* This distance functor is not dimension-wise additive, which + * makes it an invalid kd-tree distance, not implementing the accum_dist method */ + +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** + * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor + * bit count of A exclusive XOR'ed with B + */ +struct HammingLUT +{ + typedef False is_kdtree_distance; + typedef False is_vector_space_distance; + + typedef unsigned char ElementType; + typedef int ResultType; + + /** this will count the bits in a ^ b + */ + ResultType operator()(const unsigned char* a, const unsigned char* b, size_t size) const + { + static const uchar popCountTable[] = + { + 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8 + }; + ResultType result = 0; + for (size_t i = 0; i < size; i++) { + result += popCountTable[a[i] ^ b[i]]; + } + return result; + } +}; + +/** + * Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set) + * That code was taken from brief.cpp in OpenCV + */ +template +struct Hamming +{ + typedef False is_kdtree_distance; + typedef False is_vector_space_distance; + + + typedef T ElementType; + typedef int ResultType; + + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const + { + ResultType result = 0; +#ifdef __ARM_NEON__ + { + uint32x4_t bits = vmovq_n_u32(0); + for (size_t i = 0; i < size; i += 16) { + uint8x16_t A_vec = vld1q_u8 (a + i); + uint8x16_t B_vec = vld1q_u8 (b + i); + uint8x16_t AxorB = veorq_u8 (A_vec, B_vec); + uint8x16_t bitsSet = vcntq_u8 (AxorB); + uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet); + uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8); + bits = vaddq_u32(bits, bitSet4); + } + uint64x2_t bitSet2 = vpaddlq_u32 (bits); + result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0); + result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2); + } +#elif __GNUC__ + { + //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll) + typedef unsigned long long pop_t; + const size_t modulo = size % sizeof(pop_t); + const pop_t* a2 = reinterpret_cast (a); + const pop_t* b2 = reinterpret_cast (b); + const pop_t* a2_end = a2 + (size / sizeof(pop_t)); + + for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2)); + + if (modulo) { + //in the case where size is not dividable by sizeof(size_t) + //need to mask off the bits at the end + pop_t a_final = 0, b_final = 0; + memcpy(&a_final, a2, modulo); + memcpy(&b_final, b2, modulo); + result += __builtin_popcountll(a_final ^ b_final); + } + } +#else // NO NEON and NOT GNUC + typedef unsigned long long pop_t; + HammingLUT lut; + result = lut(reinterpret_cast (a), + reinterpret_cast (b), size * sizeof(pop_t)); +#endif + return result; + } +}; + +template +struct Hamming2 +{ + typedef False is_kdtree_distance; + typedef False is_vector_space_distance; + + typedef T ElementType; + typedef int ResultType; + + /** This is popcount_3() from: + * http://en.wikipedia.org/wiki/Hamming_weight */ + unsigned int popcnt32(uint32_t n) const + { + n -= ((n >> 1) & 0x55555555); + n = (n & 0x33333333) + ((n >> 2) & 0x33333333); + return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24; + } + +#ifdef FLANN_PLATFORM_64_BIT + unsigned int popcnt64(uint64_t n) const + { + n -= ((n >> 1) & 0x5555555555555555); + n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333); + return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56; + } +#endif + + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const + { +#ifdef FLANN_PLATFORM_64_BIT + const uint64_t* pa = reinterpret_cast(a); + const uint64_t* pb = reinterpret_cast(b); + ResultType result = 0; + size /= (sizeof(uint64_t)/sizeof(unsigned char)); + for(size_t i = 0; i < size; ++i ) { + result += popcnt64(*pa ^ *pb); + ++pa; + ++pb; + } +#else + const uint32_t* pa = reinterpret_cast(a); + const uint32_t* pb = reinterpret_cast(b); + ResultType result = 0; + size /= (sizeof(uint32_t)/sizeof(unsigned char)); + for(size_t i = 0; i < size; ++i ) { + result += popcnt32(*pa ^ *pb); + ++pa; + ++pb; + } +#endif + return result; + } +}; + + + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +template +struct HistIntersectionDistance +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the histogram intersection distance + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType min0, min1, min2, min3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]); + min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]); + min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]); + min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]); + result += min0 + min1 + min2 + min3; + a += 4; + b += 4; + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + min0 = (ResultType)(*a < *b ? *a : *b); + result += min0; + ++a; + ++b; + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return a +struct HellingerDistance +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the Hellinger distance + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = sqrt(static_cast(a[0])) - sqrt(static_cast(b[0])); + diff1 = sqrt(static_cast(a[1])) - sqrt(static_cast(b[1])); + diff2 = sqrt(static_cast(a[2])) - sqrt(static_cast(b[2])); + diff3 = sqrt(static_cast(a[3])) - sqrt(static_cast(b[3])); + result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + a += 4; + b += 4; + } + while (a < last) { + diff0 = sqrt(static_cast(*a++)) - sqrt(static_cast(*b++)); + result += diff0 * diff0; + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + ResultType diff = sqrt(static_cast(a)) - sqrt(static_cast(b)); + return diff * diff; + } +}; + + +template +struct ChiSquareDistance +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the chi-square distance + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType sum, diff; + Iterator1 last = a + size; + + while (a < last) { + sum = (ResultType)(*a + *b); + if (sum>0) { + diff = (ResultType)(*a - *b); + result += diff*diff/sum; + } + ++a; + ++b; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + ResultType result = ResultType(); + ResultType sum, diff; + + sum = (ResultType)(a+b); + if (sum>0) { + diff = (ResultType)(a-b); + result = diff*diff/sum; + } + return result; + } +}; + + +template +struct KL_Divergence +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the Kullback–Leibler divergence + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + Iterator1 last = a + size; + + while (a < last) { + if (* b != 0) { + ResultType ratio = (ResultType)(*a / *b); + if (ratio>0) { + result += *a * log(ratio); + } + } + ++a; + ++b; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + ResultType result = ResultType(); + if( *b != 0 ) { + ResultType ratio = (ResultType)(a / b); + if (ratio>0) { + result = a * log(ratio); + } + } + return result; + } +}; + + + +/* + * This is a "zero iterator". It basically behaves like a zero filled + * array to all algorithms that use arrays as iterators (STL style). + * It's useful when there's a need to compute the distance between feature + * and origin it and allows for better compiler optimisation than using a + * zero-filled array. + */ +template +struct ZeroIterator +{ + + T operator*() + { + return 0; + } + + T operator[](int) + { + return 0; + } + + const ZeroIterator& operator ++() + { + return *this; + } + + ZeroIterator operator ++(int) + { + return *this; + } + + ZeroIterator& operator+=(int) + { + return *this; + } + +}; + + +/* + * Depending on processed distances, some of them are already squared (e.g. L2) + * and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure + * we are working on ^2 distances, thus following templates to ensure that. + */ +template +struct squareDistance +{ + typedef typename Distance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist*dist; } +}; + + +template +struct squareDistance, ElementType> +{ + typedef typename L2_Simple::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + +template +struct squareDistance, ElementType> +{ + typedef typename L2::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + + +template +struct squareDistance, ElementType> +{ + typedef typename MinkowskiDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + +template +struct squareDistance, ElementType> +{ + typedef typename HellingerDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + +template +struct squareDistance, ElementType> +{ + typedef typename ChiSquareDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + + +template +typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist ) +{ + typedef typename Distance::ElementType ElementType; + + squareDistance dummy; + return dummy( dist ); +} + + +/* + * ...and a template to ensure the user that he will process the normal distance, + * and not squared distance, without loosing processing time calling sqrt(ensureSquareDistance) + * that will result in doing actually sqrt(dist*dist) for L1 distance for instance. + */ +template +struct simpleDistance +{ + typedef typename Distance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + + +template +struct simpleDistance, ElementType> +{ + typedef typename L2_Simple::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + +template +struct simpleDistance, ElementType> +{ + typedef typename L2::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + + +template +struct simpleDistance, ElementType> +{ + typedef typename MinkowskiDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + +template +struct simpleDistance, ElementType> +{ + typedef typename HellingerDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + +template +struct simpleDistance, ElementType> +{ + typedef typename ChiSquareDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + + +template +typename Distance::ResultType ensureSimpleDistance( typename Distance::ResultType dist ) +{ + typedef typename Distance::ElementType ElementType; + + simpleDistance dummy; + return dummy( dist ); +} + +} + +#endif //OPENCV_FLANN_DIST_H_ diff --git a/thirdparty/linux/include/opencv2/flann/dummy.h b/thirdparty/linux/include/opencv2/flann/dummy.h new file mode 100644 index 0000000..26bd3fa --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/dummy.h @@ -0,0 +1,16 @@ + +#ifndef OPENCV_FLANN_DUMMY_H_ +#define OPENCV_FLANN_DUMMY_H_ + +namespace cvflann +{ + +#if (defined WIN32 || defined _WIN32 || defined WINCE) && defined CVAPI_EXPORTS +__declspec(dllexport) +#endif +void dummyfunc(); + +} + + +#endif /* OPENCV_FLANN_DUMMY_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/dynamic_bitset.h b/thirdparty/linux/include/opencv2/flann/dynamic_bitset.h new file mode 100644 index 0000000..d795b5d --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/dynamic_bitset.h @@ -0,0 +1,159 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +/*********************************************************************** + * Author: Vincent Rabaud + *************************************************************************/ + +#ifndef OPENCV_FLANN_DYNAMIC_BITSET_H_ +#define OPENCV_FLANN_DYNAMIC_BITSET_H_ + +#ifndef FLANN_USE_BOOST +# define FLANN_USE_BOOST 0 +#endif +//#define FLANN_USE_BOOST 1 +#if FLANN_USE_BOOST +#include +typedef boost::dynamic_bitset<> DynamicBitset; +#else + +#include + +#include "dist.h" + +namespace cvflann { + +/** Class re-implementing the boost version of it + * This helps not depending on boost, it also does not do the bound checks + * and has a way to reset a block for speed + */ +class DynamicBitset +{ +public: + /** default constructor + */ + DynamicBitset() + { + } + + /** only constructor we use in our code + * @param sz the size of the bitset (in bits) + */ + DynamicBitset(size_t sz) + { + resize(sz); + reset(); + } + + /** Sets all the bits to 0 + */ + void clear() + { + std::fill(bitset_.begin(), bitset_.end(), 0); + } + + /** @brief checks if the bitset is empty + * @return true if the bitset is empty + */ + bool empty() const + { + return bitset_.empty(); + } + + /** set all the bits to 0 + */ + void reset() + { + std::fill(bitset_.begin(), bitset_.end(), 0); + } + + /** @brief set one bit to 0 + * @param index + */ + void reset(size_t index) + { + bitset_[index / cell_bit_size_] &= ~(size_t(1) << (index % cell_bit_size_)); + } + + /** @brief sets a specific bit to 0, and more bits too + * This function is useful when resetting a given set of bits so that the + * whole bitset ends up being 0: if that's the case, we don't care about setting + * other bits to 0 + * @param index + */ + void reset_block(size_t index) + { + bitset_[index / cell_bit_size_] = 0; + } + + /** resize the bitset so that it contains at least sz bits + * @param sz + */ + void resize(size_t sz) + { + size_ = sz; + bitset_.resize(sz / cell_bit_size_ + 1); + } + + /** set a bit to true + * @param index the index of the bit to set to 1 + */ + void set(size_t index) + { + bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_); + } + + /** gives the number of contained bits + */ + size_t size() const + { + return size_; + } + + /** check if a bit is set + * @param index the index of the bit to check + * @return true if the bit is set + */ + bool test(size_t index) const + { + return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0; + } + +private: + std::vector bitset_; + size_t size_; + static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t); +}; + +} // namespace cvflann + +#endif + +#endif // OPENCV_FLANN_DYNAMIC_BITSET_H_ diff --git a/thirdparty/linux/include/opencv2/flann/flann.hpp b/thirdparty/linux/include/opencv2/flann/flann.hpp new file mode 100644 index 0000000..227683f --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/flann.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/flann.hpp" diff --git a/thirdparty/linux/include/opencv2/flann/flann_base.hpp b/thirdparty/linux/include/opencv2/flann/flann_base.hpp new file mode 100644 index 0000000..98c33cf --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/flann_base.hpp @@ -0,0 +1,290 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_BASE_HPP_ +#define OPENCV_FLANN_BASE_HPP_ + +#include +#include +#include + +#include "general.h" +#include "matrix.h" +#include "params.h" +#include "saving.h" + +#include "all_indices.h" + +namespace cvflann +{ + +/** + * Sets the log level used for all flann functions + * @param level Verbosity level + */ +inline void log_verbosity(int level) +{ + if (level >= 0) { + Logger::setLevel(level); + } +} + +/** + * (Deprecated) Index parameters for creating a saved index. + */ +struct SavedIndexParams : public IndexParams +{ + SavedIndexParams(cv::String filename) + { + (* this)["algorithm"] = FLANN_INDEX_SAVED; + (*this)["filename"] = filename; + } +}; + + +template +NNIndex* load_saved_index(const Matrix& dataset, const cv::String& filename, Distance distance) +{ + typedef typename Distance::ElementType ElementType; + + FILE* fin = fopen(filename.c_str(), "rb"); + if (fin == NULL) { + return NULL; + } + IndexHeader header = load_header(fin); + if (header.data_type != Datatype::type()) { + throw FLANNException("Datatype of saved index is different than of the one to be created."); + } + if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) { + throw FLANNException("The index saved belongs to a different dataset"); + } + + IndexParams params; + params["algorithm"] = header.index_type; + NNIndex* nnIndex = create_index_by_type(dataset, params, distance); + nnIndex->loadIndex(fin); + fclose(fin); + + return nnIndex; +} + + +template +class Index : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + Index(const Matrix& features, const IndexParams& params, Distance distance = Distance() ) + : index_params_(params) + { + flann_algorithm_t index_type = get_param(params,"algorithm"); + loaded_ = false; + + if (index_type == FLANN_INDEX_SAVED) { + nnIndex_ = load_saved_index(features, get_param(params,"filename"), distance); + loaded_ = true; + } + else { + nnIndex_ = create_index_by_type(features, params, distance); + } + } + + ~Index() + { + delete nnIndex_; + } + + /** + * Builds the index. + */ + void buildIndex() + { + if (!loaded_) { + nnIndex_->buildIndex(); + } + } + + void save(cv::String filename) + { + FILE* fout = fopen(filename.c_str(), "wb"); + if (fout == NULL) { + throw FLANNException("Cannot open file"); + } + save_header(fout, *nnIndex_); + saveIndex(fout); + fclose(fout); + } + + /** + * \brief Saves the index to a stream + * \param stream The stream to save the index to + */ + virtual void saveIndex(FILE* stream) + { + nnIndex_->saveIndex(stream); + } + + /** + * \brief Loads the index from a stream + * \param stream The stream from which the index is loaded + */ + virtual void loadIndex(FILE* stream) + { + nnIndex_->loadIndex(stream); + } + + /** + * \returns number of features in this index. + */ + size_t veclen() const + { + return nnIndex_->veclen(); + } + + /** + * \returns The dimensionality of the features in this index. + */ + size_t size() const + { + return nnIndex_->size(); + } + + /** + * \returns The index type (kdtree, kmeans,...) + */ + flann_algorithm_t getType() const + { + return nnIndex_->getType(); + } + + /** + * \returns The amount of memory (in bytes) used by the index. + */ + virtual int usedMemory() const + { + return nnIndex_->usedMemory(); + } + + + /** + * \returns The index parameters + */ + IndexParams getParameters() const + { + return nnIndex_->getParameters(); + } + + /** + * \brief Perform k-nearest neighbor search + * \param[in] queries The query points for which to find the nearest neighbors + * \param[out] indices The indices of the nearest neighbors found + * \param[out] dists Distances to the nearest neighbors found + * \param[in] knn Number of nearest neighbors to return + * \param[in] params Search parameters + */ + void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) + { + nnIndex_->knnSearch(queries, indices, dists, knn, params); + } + + /** + * \brief Perform radius search + * \param[in] query The query point + * \param[out] indices The indinces of the neighbors found within the given radius + * \param[out] dists The distances to the nearest neighbors found + * \param[in] radius The radius used for search + * \param[in] params Search parameters + * \returns Number of neighbors found + */ + int radiusSearch(const Matrix& query, Matrix& indices, Matrix& dists, float radius, const SearchParams& params) + { + return nnIndex_->radiusSearch(query, indices, dists, radius, params); + } + + /** + * \brief Method that searches for nearest-neighbours + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) + { + nnIndex_->findNeighbors(result, vec, searchParams); + } + + /** + * \brief Returns actual index + */ + FLANN_DEPRECATED NNIndex* getIndex() + { + return nnIndex_; + } + + /** + * \brief Returns index parameters. + * \deprecated use getParameters() instead. + */ + FLANN_DEPRECATED const IndexParams* getIndexParameters() + { + return &index_params_; + } + +private: + /** Pointer to actual index class */ + NNIndex* nnIndex_; + /** Indices if the index was loaded from a file */ + bool loaded_; + /** Parameters passed to the index */ + IndexParams index_params_; +}; + +/** + * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the + * the clustering tree to return a flat clustering. + * @param[in] points Points to be clustered + * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the + * number of clusters requested. + * @param params Clustering parameters (The same as for cvflann::KMeansIndex) + * @param d Distance to be used for clustering (eg: cvflann::L2) + * @return number of clusters computed (can be different than clusters.rows and is the highest number + * of the form (branching-1)*K+1 smaller than clusters.rows). + */ +template +int hierarchicalClustering(const Matrix& points, Matrix& centers, + const KMeansIndexParams& params, Distance d = Distance()) +{ + KMeansIndex kmeans(points, params, d); + kmeans.buildIndex(); + + int clusterNum = kmeans.getClusterCenters(centers); + return clusterNum; +} + +} +#endif /* OPENCV_FLANN_BASE_HPP_ */ diff --git a/thirdparty/linux/include/opencv2/flann/general.h b/thirdparty/linux/include/opencv2/flann/general.h new file mode 100644 index 0000000..9d5402a --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/general.h @@ -0,0 +1,50 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_GENERAL_H_ +#define OPENCV_FLANN_GENERAL_H_ + +#include "opencv2/core.hpp" + +namespace cvflann +{ + +class FLANNException : public cv::Exception +{ +public: + FLANNException(const char* message) : cv::Exception(0, message, "", __FILE__, __LINE__) { } + + FLANNException(const cv::String& message) : cv::Exception(0, message, "", __FILE__, __LINE__) { } +}; + +} + + +#endif /* OPENCV_FLANN_GENERAL_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/ground_truth.h b/thirdparty/linux/include/opencv2/flann/ground_truth.h new file mode 100644 index 0000000..fd8f3ae --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/ground_truth.h @@ -0,0 +1,94 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_GROUND_TRUTH_H_ +#define OPENCV_FLANN_GROUND_TRUTH_H_ + +#include "dist.h" +#include "matrix.h" + + +namespace cvflann +{ + +template +void find_nearest(const Matrix& dataset, typename Distance::ElementType* query, int* matches, int nn, + int skip = 0, Distance distance = Distance()) +{ + typedef typename Distance::ResultType DistanceType; + int n = nn + skip; + + std::vector match(n); + std::vector dists(n); + + dists[0] = distance(dataset[0], query, dataset.cols); + match[0] = 0; + int dcnt = 1; + + for (size_t i=1; i=1 && dists[j] +void compute_ground_truth(const Matrix& dataset, const Matrix& testset, Matrix& matches, + int skip=0, Distance d = Distance()) +{ + for (size_t i=0; i(dataset, testset[i], matches[i], (int)matches.cols, skip, d); + } +} + + +} + +#endif //OPENCV_FLANN_GROUND_TRUTH_H_ diff --git a/thirdparty/linux/include/opencv2/flann/hdf5.h b/thirdparty/linux/include/opencv2/flann/hdf5.h new file mode 100644 index 0000000..80d23b9 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/hdf5.h @@ -0,0 +1,231 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_HDF5_H_ +#define OPENCV_FLANN_HDF5_H_ + +#include + +#include "matrix.h" + + +namespace cvflann +{ + +namespace +{ + +template +hid_t get_hdf5_type() +{ + throw FLANNException("Unsupported type for IO operations"); +} + +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_CHAR; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_UCHAR; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_SHORT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_USHORT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_INT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_UINT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_LONG; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_ULONG; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_FLOAT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_DOUBLE; } +} + + +#define CHECK_ERROR(x,y) if ((x)<0) throw FLANNException((y)); + +template +void save_to_file(const cvflann::Matrix& dataset, const String& filename, const String& name) +{ + +#if H5Eset_auto_vers == 2 + H5Eset_auto( H5E_DEFAULT, NULL, NULL ); +#else + H5Eset_auto( NULL, NULL ); +#endif + + herr_t status; + hid_t file_id; + file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT); + if (file_id < 0) { + file_id = H5Fcreate(filename.c_str(), H5F_ACC_EXCL, H5P_DEFAULT, H5P_DEFAULT); + } + CHECK_ERROR(file_id,"Error creating hdf5 file."); + + hsize_t dimsf[2]; // dataset dimensions + dimsf[0] = dataset.rows; + dimsf[1] = dataset.cols; + + hid_t space_id = H5Screate_simple(2, dimsf, NULL); + hid_t memspace_id = H5Screate_simple(2, dimsf, NULL); + + hid_t dataset_id; +#if H5Dcreate_vers == 2 + dataset_id = H5Dcreate2(file_id, name.c_str(), get_hdf5_type(), space_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); +#else + dataset_id = H5Dcreate(file_id, name.c_str(), get_hdf5_type(), space_id, H5P_DEFAULT); +#endif + + if (dataset_id<0) { +#if H5Dopen_vers == 2 + dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); +#else + dataset_id = H5Dopen(file_id, name.c_str()); +#endif + } + CHECK_ERROR(dataset_id,"Error creating or opening dataset in file."); + + status = H5Dwrite(dataset_id, get_hdf5_type(), memspace_id, space_id, H5P_DEFAULT, dataset.data ); + CHECK_ERROR(status, "Error writing to dataset"); + + H5Sclose(memspace_id); + H5Sclose(space_id); + H5Dclose(dataset_id); + H5Fclose(file_id); + +} + + +template +void load_from_file(cvflann::Matrix& dataset, const String& filename, const String& name) +{ + herr_t status; + hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT); + CHECK_ERROR(file_id,"Error opening hdf5 file."); + + hid_t dataset_id; +#if H5Dopen_vers == 2 + dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); +#else + dataset_id = H5Dopen(file_id, name.c_str()); +#endif + CHECK_ERROR(dataset_id,"Error opening dataset in file."); + + hid_t space_id = H5Dget_space(dataset_id); + + hsize_t dims_out[2]; + H5Sget_simple_extent_dims(space_id, dims_out, NULL); + + dataset = cvflann::Matrix(new T[dims_out[0]*dims_out[1]], dims_out[0], dims_out[1]); + + status = H5Dread(dataset_id, get_hdf5_type(), H5S_ALL, H5S_ALL, H5P_DEFAULT, dataset[0]); + CHECK_ERROR(status, "Error reading dataset"); + + H5Sclose(space_id); + H5Dclose(dataset_id); + H5Fclose(file_id); +} + + +#ifdef HAVE_MPI + +namespace mpi +{ +/** + * Loads a the hyperslice corresponding to this processor from a hdf5 file. + * @param flann_dataset Dataset where the data is loaded + * @param filename HDF5 file name + * @param name Name of dataset inside file + */ +template +void load_from_file(cvflann::Matrix& dataset, const String& filename, const String& name) +{ + MPI_Comm comm = MPI_COMM_WORLD; + MPI_Info info = MPI_INFO_NULL; + + int mpi_size, mpi_rank; + MPI_Comm_size(comm, &mpi_size); + MPI_Comm_rank(comm, &mpi_rank); + + herr_t status; + + hid_t plist_id = H5Pcreate(H5P_FILE_ACCESS); + H5Pset_fapl_mpio(plist_id, comm, info); + hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, plist_id); + CHECK_ERROR(file_id,"Error opening hdf5 file."); + H5Pclose(plist_id); + hid_t dataset_id; +#if H5Dopen_vers == 2 + dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); +#else + dataset_id = H5Dopen(file_id, name.c_str()); +#endif + CHECK_ERROR(dataset_id,"Error opening dataset in file."); + + hid_t space_id = H5Dget_space(dataset_id); + hsize_t dims[2]; + H5Sget_simple_extent_dims(space_id, dims, NULL); + + hsize_t count[2]; + hsize_t offset[2]; + + hsize_t item_cnt = dims[0]/mpi_size+(dims[0]%mpi_size==0 ? 0 : 1); + hsize_t cnt = (mpi_rank(), memspace_id, space_id, plist_id, dataset.data); + CHECK_ERROR(status, "Error reading dataset"); + + H5Pclose(plist_id); + H5Sclose(space_id); + H5Sclose(memspace_id); + H5Dclose(dataset_id); + H5Fclose(file_id); +} +} +#endif // HAVE_MPI +} // namespace cvflann::mpi + +#endif /* OPENCV_FLANN_HDF5_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/heap.h b/thirdparty/linux/include/opencv2/flann/heap.h new file mode 100644 index 0000000..92a6ea6 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/heap.h @@ -0,0 +1,165 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_HEAP_H_ +#define OPENCV_FLANN_HEAP_H_ + +#include +#include + +namespace cvflann +{ + +/** + * Priority Queue Implementation + * + * The priority queue is implemented with a heap. A heap is a complete + * (full) binary tree in which each parent is less than both of its + * children, but the order of the children is unspecified. + */ +template +class Heap +{ + + /** + * Storage array for the heap. + * Type T must be comparable. + */ + std::vector heap; + int length; + + /** + * Number of element in the heap + */ + int count; + + + +public: + /** + * Constructor. + * + * Params: + * sz = heap size + */ + + Heap(int sz) + { + length = sz; + heap.reserve(length); + count = 0; + } + + /** + * + * Returns: heap size + */ + int size() + { + return count; + } + + /** + * Tests if the heap is empty + * + * Returns: true is heap empty, false otherwise + */ + bool empty() + { + return size()==0; + } + + /** + * Clears the heap. + */ + void clear() + { + heap.clear(); + count = 0; + } + + struct CompareT + { + bool operator()(const T& t_1, const T& t_2) const + { + return t_2 < t_1; + } + }; + + /** + * Insert a new element in the heap. + * + * We select the next empty leaf node, and then keep moving any larger + * parents down until the right location is found to store this element. + * + * Params: + * value = the new element to be inserted in the heap + */ + void insert(T value) + { + /* If heap is full, then return without adding this element. */ + if (count == length) { + return; + } + + heap.push_back(value); + static CompareT compareT; + std::push_heap(heap.begin(), heap.end(), compareT); + ++count; + } + + + + /** + * Returns the node of minimum value from the heap (top of the heap). + * + * Params: + * value = out parameter used to return the min element + * Returns: false if heap empty + */ + bool popMin(T& value) + { + if (count == 0) { + return false; + } + + value = heap[0]; + static CompareT compareT; + std::pop_heap(heap.begin(), heap.end(), compareT); + heap.pop_back(); + --count; + + return true; /* Return old last node. */ + } +}; + +} + +#endif //OPENCV_FLANN_HEAP_H_ diff --git a/thirdparty/linux/include/opencv2/flann/hierarchical_clustering_index.h b/thirdparty/linux/include/opencv2/flann/hierarchical_clustering_index.h new file mode 100644 index 0000000..9d890d4 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/hierarchical_clustering_index.h @@ -0,0 +1,848 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ +#define OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ + +#include +#include +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "dist.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" + + +namespace cvflann +{ + +struct HierarchicalClusteringIndexParams : public IndexParams +{ + HierarchicalClusteringIndexParams(int branching = 32, + flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, + int trees = 4, int leaf_size = 100) + { + (*this)["algorithm"] = FLANN_INDEX_HIERARCHICAL; + // The branching factor used in the hierarchical clustering + (*this)["branching"] = branching; + // Algorithm used for picking the initial cluster centers + (*this)["centers_init"] = centers_init; + // number of parallel trees to build + (*this)["trees"] = trees; + // maximum leaf size + (*this)["leaf_size"] = leaf_size; + } +}; + + +/** + * Hierarchical index + * + * Contains a tree constructed through a hierarchical clustering + * and other information for indexing a set of points for nearest-neighbour matching. + */ +template +class HierarchicalClusteringIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + +private: + + + typedef void (HierarchicalClusteringIndex::* centersAlgFunction)(int, int*, int, int*, int&); + + /** + * The function used for choosing the cluster centers. + */ + centersAlgFunction chooseCenters; + + + + /** + * Chooses the initial centers in the k-means clustering in a random manner. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * indices_length = length of indices vector + * + */ + void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length) + { + UniqueRandom r(indices_length); + + int index; + for (index=0; index=0 && rnd < n); + + centers[0] = dsindices[rnd]; + + int index; + for (index=1; indexbest_val) { + best_val = dist; + best_index = j; + } + } + if (best_index!=-1) { + centers[index] = dsindices[best_index]; + } + else { + break; + } + } + centers_length = index; + } + + + /** + * Chooses the initial centers in the k-means using the algorithm + * proposed in the KMeans++ paper: + * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding + * + * Implementation of this function was converted from the one provided in Arthur's code. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * Returns: + */ + void chooseCentersKMeanspp(int k, int* dsindices, int indices_length, int* centers, int& centers_length) + { + int n = indices_length; + + double currentPot = 0; + DistanceType* closestDistSq = new DistanceType[n]; + + // Choose one random center and set the closestDistSq values + int index = rand_int(n); + assert(index >=0 && index < n); + centers[0] = dsindices[index]; + + // Computing distance^2 will have the advantage of even higher probability further to pick new centers + // far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article) + for (int i = 0; i < n; i++) { + closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); + closestDistSq[i] = ensureSquareDistance( closestDistSq[i] ); + currentPot += closestDistSq[i]; + } + + + const int numLocalTries = 1; + + // Choose each center + int centerCount; + for (centerCount = 1; centerCount < k; centerCount++) { + + // Repeat several trials + double bestNewPot = -1; + int bestNewIndex = 0; + for (int localTrial = 0; localTrial < numLocalTries; localTrial++) { + + // Choose our center - have to be slightly careful to return a valid answer even accounting + // for possible rounding errors + double randVal = rand_double(currentPot); + for (index = 0; index < n-1; index++) { + if (randVal <= closestDistSq[index]) break; + else randVal -= closestDistSq[index]; + } + + // Compute the new potential + double newPot = 0; + for (int i = 0; i < n; i++) { + DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); + newPot += std::min( ensureSquareDistance(dist), closestDistSq[i] ); + } + + // Store the best result + if ((bestNewPot < 0)||(newPot < bestNewPot)) { + bestNewPot = newPot; + bestNewIndex = index; + } + } + + // Add the appropriate center + centers[centerCount] = dsindices[bestNewIndex]; + currentPot = bestNewPot; + for (int i = 0; i < n; i++) { + DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols); + closestDistSq[i] = std::min( ensureSquareDistance(dist), closestDistSq[i] ); + } + } + + centers_length = centerCount; + + delete[] closestDistSq; + } + + + /** + * Chooses the initial centers in a way inspired by Gonzales (by Pierre-Emmanuel Viel): + * select the first point of the list as a candidate, then parse the points list. If another + * point is further than current candidate from the other centers, test if it is a good center + * of a local aggregation. If it is, replace current candidate by this point. And so on... + * + * Used with KMeansIndex that computes centers coordinates by averaging positions of clusters points, + * this doesn't make a real difference with previous methods. But used with HierarchicalClusteringIndex + * class that pick centers among existing points instead of computing the barycenters, there is a real + * improvement. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * Returns: + */ + void GroupWiseCenterChooser(int k, int* dsindices, int indices_length, int* centers, int& centers_length) + { + const float kSpeedUpFactor = 1.3f; + + int n = indices_length; + + DistanceType* closestDistSq = new DistanceType[n]; + + // Choose one random center and set the closestDistSq values + int index = rand_int(n); + assert(index >=0 && index < n); + centers[0] = dsindices[index]; + + for (int i = 0; i < n; i++) { + closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); + } + + + // Choose each center + int centerCount; + for (centerCount = 1; centerCount < k; centerCount++) { + + // Repeat several trials + double bestNewPot = -1; + int bestNewIndex = 0; + DistanceType furthest = 0; + for (index = 0; index < n; index++) { + + // We will test only the potential of the points further than current candidate + if( closestDistSq[index] > kSpeedUpFactor * (float)furthest ) { + + // Compute the new potential + double newPot = 0; + for (int i = 0; i < n; i++) { + newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols) + , closestDistSq[i] ); + } + + // Store the best result + if ((bestNewPot < 0)||(newPot <= bestNewPot)) { + bestNewPot = newPot; + bestNewIndex = index; + furthest = closestDistSq[index]; + } + } + } + + // Add the appropriate center + centers[centerCount] = dsindices[bestNewIndex]; + for (int i = 0; i < n; i++) { + closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols) + , closestDistSq[i] ); + } + } + + centers_length = centerCount; + + delete[] closestDistSq; + } + + +public: + + + /** + * Index constructor + * + * Params: + * inputData = dataset with the input features + * params = parameters passed to the hierarchical k-means algorithm + */ + HierarchicalClusteringIndex(const Matrix& inputData, const IndexParams& index_params = HierarchicalClusteringIndexParams(), + Distance d = Distance()) + : dataset(inputData), params(index_params), root(NULL), indices(NULL), distance(d) + { + memoryCounter = 0; + + size_ = dataset.rows; + veclen_ = dataset.cols; + + branching_ = get_param(params,"branching",32); + centers_init_ = get_param(params,"centers_init", FLANN_CENTERS_RANDOM); + trees_ = get_param(params,"trees",4); + leaf_size_ = get_param(params,"leaf_size",100); + + if (centers_init_==FLANN_CENTERS_RANDOM) { + chooseCenters = &HierarchicalClusteringIndex::chooseCentersRandom; + } + else if (centers_init_==FLANN_CENTERS_GONZALES) { + chooseCenters = &HierarchicalClusteringIndex::chooseCentersGonzales; + } + else if (centers_init_==FLANN_CENTERS_KMEANSPP) { + chooseCenters = &HierarchicalClusteringIndex::chooseCentersKMeanspp; + } + else if (centers_init_==FLANN_CENTERS_GROUPWISE) { + chooseCenters = &HierarchicalClusteringIndex::GroupWiseCenterChooser; + } + else { + throw FLANNException("Unknown algorithm for choosing initial centers."); + } + + trees_ = get_param(params,"trees",4); + root = new NodePtr[trees_]; + indices = new int*[trees_]; + + for (int i=0; i(); + computeClustering(root[i], indices[i], (int)size_, branching_,0); + } + } + + + flann_algorithm_t getType() const + { + return FLANN_INDEX_HIERARCHICAL; + } + + + void saveIndex(FILE* stream) + { + save_value(stream, branching_); + save_value(stream, trees_); + save_value(stream, centers_init_); + save_value(stream, leaf_size_); + save_value(stream, memoryCounter); + for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) + { + + int maxChecks = get_param(searchParams,"checks",32); + + // Priority queue storing intermediate branches in the best-bin-first search + Heap* heap = new Heap((int)size_); + + std::vector checked(size_,false); + int checks = 0; + for (int i=0; ipopMin(branch) && (checks BranchSt; + + + + void save_tree(FILE* stream, NodePtr node, int num) + { + save_value(stream, *node); + if (node->childs==NULL) { + int indices_offset = (int)(node->indices - indices[num]); + save_value(stream, indices_offset); + } + else { + for(int i=0; ichilds[i], num); + } + } + } + + + void load_tree(FILE* stream, NodePtr& node, int num) + { + node = pool.allocate(); + load_value(stream, *node); + if (node->childs==NULL) { + int indices_offset; + load_value(stream, indices_offset); + node->indices = indices[num] + indices_offset; + } + else { + node->childs = pool.allocate(branching_); + for(int i=0; ichilds[i], num); + } + } + } + + + + + void computeLabels(int* dsindices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost) + { + cost = 0; + for (int i=0; inew_dist) { + labels[i] = j; + dist = new_dist; + } + } + cost += dist; + } + } + + /** + * The method responsible with actually doing the recursive hierarchical + * clustering + * + * Params: + * node = the node to cluster + * indices = indices of the points belonging to the current node + * branching = the branching factor to use in the clustering + * + * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point) + */ + void computeClustering(NodePtr node, int* dsindices, int indices_length, int branching, int level) + { + node->size = indices_length; + node->level = level; + + if (indices_length < leaf_size_) { // leaf node + node->indices = dsindices; + std::sort(node->indices,node->indices+indices_length); + node->childs = NULL; + return; + } + + std::vector centers(branching); + std::vector labels(indices_length); + + int centers_length; + (this->*chooseCenters)(branching, dsindices, indices_length, ¢ers[0], centers_length); + + if (centers_lengthindices = dsindices; + std::sort(node->indices,node->indices+indices_length); + node->childs = NULL; + return; + } + + + // assign points to clusters + DistanceType cost; + computeLabels(dsindices, indices_length, ¢ers[0], centers_length, &labels[0], cost); + + node->childs = pool.allocate(branching); + int start = 0; + int end = start; + for (int i=0; ichilds[i] = pool.allocate(); + node->childs[i]->pivot = centers[i]; + node->childs[i]->indices = NULL; + computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1); + start=end; + } + } + + + + /** + * Performs one descent in the hierarchical k-means tree. The branches not + * visited are stored in a priority queue. + * + * Params: + * node = node to explore + * result = container for the k-nearest neighbors found + * vec = query points + * checks = how many points in the dataset have been checked so far + * maxChecks = maximum dataset points to checks + */ + + + void findNN(NodePtr node, ResultSet& result, const ElementType* vec, int& checks, int maxChecks, + Heap* heap, std::vector& checked) + { + if (node->childs==NULL) { + if (checks>=maxChecks) { + if (result.full()) return; + } + for (int i=0; isize; ++i) { + int index = node->indices[i]; + if (!checked[index]) { + DistanceType dist = distance(dataset[index], vec, veclen_); + result.addPoint(dist, index); + checked[index] = true; + ++checks; + } + } + } + else { + DistanceType* domain_distances = new DistanceType[branching_]; + int best_index = 0; + domain_distances[best_index] = distance(vec, dataset[node->childs[best_index]->pivot], veclen_); + for (int i=1; ichilds[i]->pivot], veclen_); + if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i])); + } + } + delete[] domain_distances; + findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked); + } + } + +private: + + + /** + * The dataset used by this index + */ + const Matrix dataset; + + /** + * Parameters used by this index + */ + IndexParams params; + + + /** + * Number of features in the dataset. + */ + size_t size_; + + /** + * Length of each feature. + */ + size_t veclen_; + + /** + * The root node in the tree. + */ + NodePtr* root; + + /** + * Array of indices to vectors in the dataset. + */ + int** indices; + + + /** + * The distance + */ + Distance distance; + + /** + * Pooled memory allocator. + * + * Using a pooled memory allocator is more efficient + * than allocating memory directly when there is a large + * number small of memory allocations. + */ + PooledAllocator pool; + + /** + * Memory occupied by the index. + */ + int memoryCounter; + + /** index parameters */ + int branching_; + int trees_; + flann_centers_init_t centers_init_; + int leaf_size_; + + +}; + +} + +#endif /* OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/index_testing.h b/thirdparty/linux/include/opencv2/flann/index_testing.h new file mode 100644 index 0000000..d764004 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/index_testing.h @@ -0,0 +1,318 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_INDEX_TESTING_H_ +#define OPENCV_FLANN_INDEX_TESTING_H_ + +#include +#include +#include + +#include "matrix.h" +#include "nn_index.h" +#include "result_set.h" +#include "logger.h" +#include "timer.h" + + +namespace cvflann +{ + +inline int countCorrectMatches(int* neighbors, int* groundTruth, int n) +{ + int count = 0; + for (int i=0; i +typename Distance::ResultType computeDistanceRaport(const Matrix& inputData, typename Distance::ElementType* target, + int* neighbors, int* groundTruth, int veclen, int n, const Distance& distance) +{ + typedef typename Distance::ResultType DistanceType; + + DistanceType ret = 0; + for (int i=0; i +float search_with_ground_truth(NNIndex& index, const Matrix& inputData, + const Matrix& testData, const Matrix& matches, int nn, int checks, + float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches) +{ + typedef typename Distance::ResultType DistanceType; + + if (matches.cols resultSet(nn+skipMatches); + SearchParams searchParams(checks); + + std::vector indices(nn+skipMatches); + std::vector dists(nn+skipMatches); + int* neighbors = &indices[skipMatches]; + + int correct = 0; + DistanceType distR = 0; + StartStopTimer t; + int repeats = 0; + while (t.value<0.2) { + repeats++; + t.start(); + correct = 0; + distR = 0; + for (size_t i = 0; i < testData.rows; i++) { + resultSet.init(&indices[0], &dists[0]); + index.findNeighbors(resultSet, testData[i], searchParams); + + correct += countCorrectMatches(neighbors,matches[i], nn); + distR += computeDistanceRaport(inputData, testData[i], neighbors, matches[i], (int)testData.cols, nn, distance); + } + t.stop(); + } + time = float(t.value/repeats); + + float precicion = (float)correct/(nn*testData.rows); + + dist = distR/(testData.rows*nn); + + Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n", + checks, precicion, time, 1000.0 * time / testData.rows, dist); + + return precicion; +} + + +template +float test_index_checks(NNIndex& index, const Matrix& inputData, + const Matrix& testData, const Matrix& matches, + int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0) +{ + typedef typename Distance::ResultType DistanceType; + + Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); + Logger::info("---------------------------------------------------------\n"); + + float time = 0; + DistanceType dist = 0; + precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches); + + return time; +} + +template +float test_index_precision(NNIndex& index, const Matrix& inputData, + const Matrix& testData, const Matrix& matches, + float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0) +{ + typedef typename Distance::ResultType DistanceType; + const float SEARCH_EPS = 0.001f; + + Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); + Logger::info("---------------------------------------------------------\n"); + + int c2 = 1; + float p2; + int c1 = 1; + //float p1; + float time; + DistanceType dist; + + p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches); + + if (p2>precision) { + Logger::info("Got as close as I can\n"); + checks = c2; + return time; + } + + while (p2SEARCH_EPS) { + Logger::info("Start linear estimation\n"); + // after we got to values in the vecinity of the desired precision + // use linear approximation get a better estimation + + cx = (c1+c2)/2; + realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches); + while (fabs(realPrecision-precision)>SEARCH_EPS) { + + if (realPrecision +void test_index_precisions(NNIndex& index, const Matrix& inputData, + const Matrix& testData, const Matrix& matches, + float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0) +{ + typedef typename Distance::ResultType DistanceType; + + const float SEARCH_EPS = 0.001; + + // make sure precisions array is sorted + std::sort(precisions, precisions+precisions_length); + + int pindex = 0; + float precision = precisions[pindex]; + + Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); + Logger::info("---------------------------------------------------------\n"); + + int c2 = 1; + float p2; + + int c1 = 1; + float p1; + + float time; + DistanceType dist; + + p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches); + + // if precision for 1 run down the tree is already + // better then some of the requested precisions, then + // skip those + while (precisions[pindex] 0)&&(time > maxTime)&&(p2SEARCH_EPS) { + Logger::info("Start linear estimation\n"); + // after we got to values in the vecinity of the desired precision + // use linear approximation get a better estimation + + cx = (c1+c2)/2; + realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches); + while (fabs(realPrecision-precision)>SEARCH_EPS) { + + if (realPrecision +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "dynamic_bitset.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" + + +namespace cvflann +{ + +struct KDTreeIndexParams : public IndexParams +{ + KDTreeIndexParams(int trees = 4) + { + (*this)["algorithm"] = FLANN_INDEX_KDTREE; + (*this)["trees"] = trees; + } +}; + + +/** + * Randomized kd-tree index + * + * Contains the k-d trees and other information for indexing a set of points + * for nearest-neighbor matching. + */ +template +class KDTreeIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + + /** + * KDTree constructor + * + * Params: + * inputData = dataset with the input features + * params = parameters passed to the kdtree algorithm + */ + KDTreeIndex(const Matrix& inputData, const IndexParams& params = KDTreeIndexParams(), + Distance d = Distance() ) : + dataset_(inputData), index_params_(params), distance_(d) + { + size_ = dataset_.rows; + veclen_ = dataset_.cols; + + trees_ = get_param(index_params_,"trees",4); + tree_roots_ = new NodePtr[trees_]; + + // Create a permutable array of indices to the input vectors. + vind_.resize(size_); + for (size_t i = 0; i < size_; ++i) { + vind_[i] = int(i); + } + + mean_ = new DistanceType[veclen_]; + var_ = new DistanceType[veclen_]; + } + + + KDTreeIndex(const KDTreeIndex&); + KDTreeIndex& operator=(const KDTreeIndex&); + + /** + * Standard destructor + */ + ~KDTreeIndex() + { + if (tree_roots_!=NULL) { + delete[] tree_roots_; + } + delete[] mean_; + delete[] var_; + } + + /** + * Builds the index + */ + void buildIndex() + { + /* Construct the randomized trees. */ + for (int i = 0; i < trees_; i++) { + /* Randomize the order of vectors to allow for unbiased sampling. */ + std::random_shuffle(vind_.begin(), vind_.end()); + tree_roots_[i] = divideTree(&vind_[0], int(size_) ); + } + } + + + flann_algorithm_t getType() const + { + return FLANN_INDEX_KDTREE; + } + + + void saveIndex(FILE* stream) + { + save_value(stream, trees_); + for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) + { + int maxChecks = get_param(searchParams,"checks", 32); + float epsError = 1+get_param(searchParams,"eps",0.0f); + + if (maxChecks==FLANN_CHECKS_UNLIMITED) { + getExactNeighbors(result, vec, epsError); + } + else { + getNeighbors(result, vec, maxChecks, epsError); + } + } + + IndexParams getParameters() const + { + return index_params_; + } + +private: + + + /*--------------------- Internal Data Structures --------------------------*/ + struct Node + { + /** + * Dimension used for subdivision. + */ + int divfeat; + /** + * The values used for subdivision. + */ + DistanceType divval; + /** + * The child nodes. + */ + Node* child1, * child2; + }; + typedef Node* NodePtr; + typedef BranchStruct BranchSt; + typedef BranchSt* Branch; + + + + void save_tree(FILE* stream, NodePtr tree) + { + save_value(stream, *tree); + if (tree->child1!=NULL) { + save_tree(stream, tree->child1); + } + if (tree->child2!=NULL) { + save_tree(stream, tree->child2); + } + } + + + void load_tree(FILE* stream, NodePtr& tree) + { + tree = pool_.allocate(); + load_value(stream, *tree); + if (tree->child1!=NULL) { + load_tree(stream, tree->child1); + } + if (tree->child2!=NULL) { + load_tree(stream, tree->child2); + } + } + + + /** + * Create a tree node that subdivides the list of vecs from vind[first] + * to vind[last]. The routine is called recursively on each sublist. + * Place a pointer to this new tree node in the location pTree. + * + * Params: pTree = the new node to create + * first = index of the first vector + * last = index of the last vector + */ + NodePtr divideTree(int* ind, int count) + { + NodePtr node = pool_.allocate(); // allocate memory + + /* If too few exemplars remain, then make this a leaf node. */ + if ( count == 1) { + node->child1 = node->child2 = NULL; /* Mark as leaf node. */ + node->divfeat = *ind; /* Store index of this vec. */ + } + else { + int idx; + int cutfeat; + DistanceType cutval; + meanSplit(ind, count, idx, cutfeat, cutval); + + node->divfeat = cutfeat; + node->divval = cutval; + node->child1 = divideTree(ind, idx); + node->child2 = divideTree(ind+idx, count-idx); + } + + return node; + } + + + /** + * Choose which feature to use in order to subdivide this set of vectors. + * Make a random choice among those with the highest variance, and use + * its variance as the threshold value. + */ + void meanSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval) + { + memset(mean_,0,veclen_*sizeof(DistanceType)); + memset(var_,0,veclen_*sizeof(DistanceType)); + + /* Compute mean values. Only the first SAMPLE_MEAN values need to be + sampled to get a good estimate. + */ + int cnt = std::min((int)SAMPLE_MEAN+1, count); + for (int j = 0; j < cnt; ++j) { + ElementType* v = dataset_[ind[j]]; + for (size_t k=0; kcount/2) index = lim1; + else if (lim2 v[topind[num-1]])) { + /* Put this element at end of topind. */ + if (num < RAND_DIM) { + topind[num++] = i; /* Add to list. */ + } + else { + topind[num-1] = i; /* Replace last element. */ + } + /* Bubble end value down to right location by repeated swapping. */ + int j = num - 1; + while (j > 0 && v[topind[j]] > v[topind[j-1]]) { + std::swap(topind[j], topind[j-1]); + --j; + } + } + } + /* Select a random integer in range [0,num-1], and return that index. */ + int rnd = rand_int(num); + return (int)topind[rnd]; + } + + + /** + * Subdivide the list of points by a plane perpendicular on axe corresponding + * to the 'cutfeat' dimension at 'cutval' position. + * + * On return: + * dataset[ind[0..lim1-1]][cutfeat]cutval + */ + void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2) + { + /* Move vector indices for left subtree to front of list. */ + int left = 0; + int right = count-1; + for (;; ) { + while (left<=right && dataset_[ind[left]][cutfeat]=cutval) --right; + if (left>right) break; + std::swap(ind[left], ind[right]); ++left; --right; + } + lim1 = left; + right = count-1; + for (;; ) { + while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left; + while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right; + if (left>right) break; + std::swap(ind[left], ind[right]); ++left; --right; + } + lim2 = left; + } + + /** + * Performs an exact nearest neighbor search. The exact search performs a full + * traversal of the tree. + */ + void getExactNeighbors(ResultSet& result, const ElementType* vec, float epsError) + { + // checkID -= 1; /* Set a different unique ID for each search. */ + + if (trees_ > 1) { + fprintf(stderr,"It doesn't make any sense to use more than one tree for exact search"); + } + if (trees_>0) { + searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError); + } + assert(result.full()); + } + + /** + * Performs the approximate nearest-neighbor search. The search is approximate + * because the tree traversal is abandoned after a given number of descends in + * the tree. + */ + void getNeighbors(ResultSet& result, const ElementType* vec, int maxCheck, float epsError) + { + int i; + BranchSt branch; + + int checkCount = 0; + Heap* heap = new Heap((int)size_); + DynamicBitset checked(size_); + + /* Search once through each tree down to root. */ + for (i = 0; i < trees_; ++i) { + searchLevel(result, vec, tree_roots_[i], 0, checkCount, maxCheck, epsError, heap, checked); + } + + /* Keep searching other branches from heap until finished. */ + while ( heap->popMin(branch) && (checkCount < maxCheck || !result.full() )) { + searchLevel(result, vec, branch.node, branch.mindist, checkCount, maxCheck, epsError, heap, checked); + } + + delete heap; + + assert(result.full()); + } + + + /** + * Search starting from a given node of the tree. Based on any mismatches at + * higher levels, all exemplars below this level must have a distance of + * at least "mindistsq". + */ + void searchLevel(ResultSet& result_set, const ElementType* vec, NodePtr node, DistanceType mindist, int& checkCount, int maxCheck, + float epsError, Heap* heap, DynamicBitset& checked) + { + if (result_set.worstDist()child1 == NULL)&&(node->child2 == NULL)) { + /* Do not check same node more than once when searching multiple trees. + Once a vector is checked, we set its location in vind to the + current checkID. + */ + int index = node->divfeat; + if ( checked.test(index) || ((checkCount>=maxCheck)&& result_set.full()) ) return; + checked.set(index); + checkCount++; + + DistanceType dist = distance_(dataset_[index], vec, veclen_); + result_set.addPoint(dist,index); + + return; + } + + /* Which child branch should be taken first? */ + ElementType val = vec[node->divfeat]; + DistanceType diff = val - node->divval; + NodePtr bestChild = (diff < 0) ? node->child1 : node->child2; + NodePtr otherChild = (diff < 0) ? node->child2 : node->child1; + + /* Create a branch record for the branch not taken. Add distance + of this feature boundary (we don't attempt to correct for any + use of this feature in a parent node, which is unlikely to + happen and would have only a small effect). Don't bother + adding more branches to heap after halfway point, as cost of + adding exceeds their value. + */ + + DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); + // if (2 * checkCount < maxCheck || !result.full()) { + if ((new_distsq*epsError < result_set.worstDist())|| !result_set.full()) { + heap->insert( BranchSt(otherChild, new_distsq) ); + } + + /* Call recursively to search next level down. */ + searchLevel(result_set, vec, bestChild, mindist, checkCount, maxCheck, epsError, heap, checked); + } + + /** + * Performs an exact search in the tree starting from a node. + */ + void searchLevelExact(ResultSet& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist, const float epsError) + { + /* If this is a leaf node, then do check and return. */ + if ((node->child1 == NULL)&&(node->child2 == NULL)) { + int index = node->divfeat; + DistanceType dist = distance_(dataset_[index], vec, veclen_); + result_set.addPoint(dist,index); + return; + } + + /* Which child branch should be taken first? */ + ElementType val = vec[node->divfeat]; + DistanceType diff = val - node->divval; + NodePtr bestChild = (diff < 0) ? node->child1 : node->child2; + NodePtr otherChild = (diff < 0) ? node->child2 : node->child1; + + /* Create a branch record for the branch not taken. Add distance + of this feature boundary (we don't attempt to correct for any + use of this feature in a parent node, which is unlikely to + happen and would have only a small effect). Don't bother + adding more branches to heap after halfway point, as cost of + adding exceeds their value. + */ + + DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); + + /* Call recursively to search next level down. */ + searchLevelExact(result_set, vec, bestChild, mindist, epsError); + + if (new_distsq*epsError<=result_set.worstDist()) { + searchLevelExact(result_set, vec, otherChild, new_distsq, epsError); + } + } + + +private: + + enum + { + /** + * To improve efficiency, only SAMPLE_MEAN random values are used to + * compute the mean and variance at each level when building a tree. + * A value of 100 seems to perform as well as using all values. + */ + SAMPLE_MEAN = 100, + /** + * Top random dimensions to consider + * + * When creating random trees, the dimension on which to subdivide is + * selected at random from among the top RAND_DIM dimensions with the + * highest variance. A value of 5 works well. + */ + RAND_DIM=5 + }; + + + /** + * Number of randomized trees that are used + */ + int trees_; + + /** + * Array of indices to vectors in the dataset. + */ + std::vector vind_; + + /** + * The dataset used by this index + */ + const Matrix dataset_; + + IndexParams index_params_; + + size_t size_; + size_t veclen_; + + + DistanceType* mean_; + DistanceType* var_; + + + /** + * Array of k-d trees used to find neighbours. + */ + NodePtr* tree_roots_; + + /** + * Pooled memory allocator. + * + * Using a pooled memory allocator is more efficient + * than allocating memory directly when there is a large + * number small of memory allocations. + */ + PooledAllocator pool_; + + Distance distance_; + + +}; // class KDTreeForest + +} + +#endif //OPENCV_FLANN_KDTREE_INDEX_H_ diff --git a/thirdparty/linux/include/opencv2/flann/kdtree_single_index.h b/thirdparty/linux/include/opencv2/flann/kdtree_single_index.h new file mode 100644 index 0000000..30488ad --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/kdtree_single_index.h @@ -0,0 +1,634 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ +#define OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ + +#include +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" + +namespace cvflann +{ + +struct KDTreeSingleIndexParams : public IndexParams +{ + KDTreeSingleIndexParams(int leaf_max_size = 10, bool reorder = true, int dim = -1) + { + (*this)["algorithm"] = FLANN_INDEX_KDTREE_SINGLE; + (*this)["leaf_max_size"] = leaf_max_size; + (*this)["reorder"] = reorder; + (*this)["dim"] = dim; + } +}; + + +/** + * Randomized kd-tree index + * + * Contains the k-d trees and other information for indexing a set of points + * for nearest-neighbor matching. + */ +template +class KDTreeSingleIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + + /** + * KDTree constructor + * + * Params: + * inputData = dataset with the input features + * params = parameters passed to the kdtree algorithm + */ + KDTreeSingleIndex(const Matrix& inputData, const IndexParams& params = KDTreeSingleIndexParams(), + Distance d = Distance() ) : + dataset_(inputData), index_params_(params), distance_(d) + { + size_ = dataset_.rows; + dim_ = dataset_.cols; + int dim_param = get_param(params,"dim",-1); + if (dim_param>0) dim_ = dim_param; + leaf_max_size_ = get_param(params,"leaf_max_size",10); + reorder_ = get_param(params,"reorder",true); + + // Create a permutable array of indices to the input vectors. + vind_.resize(size_); + for (size_t i = 0; i < size_; i++) { + vind_[i] = (int)i; + } + } + + KDTreeSingleIndex(const KDTreeSingleIndex&); + KDTreeSingleIndex& operator=(const KDTreeSingleIndex&); + + /** + * Standard destructor + */ + ~KDTreeSingleIndex() + { + if (reorder_) delete[] data_.data; + } + + /** + * Builds the index + */ + void buildIndex() + { + computeBoundingBox(root_bbox_); + root_node_ = divideTree(0, (int)size_, root_bbox_ ); // construct the tree + + if (reorder_) { + delete[] data_.data; + data_ = cvflann::Matrix(new ElementType[size_*dim_], size_, dim_); + for (size_t i=0; i& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) + { + assert(queries.cols == veclen()); + assert(indices.rows >= queries.rows); + assert(dists.rows >= queries.rows); + assert(int(indices.cols) >= knn); + assert(int(dists.cols) >= knn); + + KNNSimpleResultSet resultSet(knn); + for (size_t i = 0; i < queries.rows; i++) { + resultSet.init(indices[i], dists[i]); + findNeighbors(resultSet, queries[i], params); + } + } + + IndexParams getParameters() const + { + return index_params_; + } + + /** + * Find set of nearest neighbors to vec. Their indices are stored inside + * the result object. + * + * Params: + * result = the result object in which the indices of the nearest-neighbors are stored + * vec = the vector for which to search the nearest neighbors + * maxCheck = the maximum number of restarts (in a best-bin-first manner) + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) + { + float epsError = 1+get_param(searchParams,"eps",0.0f); + + std::vector dists(dim_,0); + DistanceType distsq = computeInitialDistances(vec, dists); + searchLevel(result, vec, root_node_, distsq, dists, epsError); + } + +private: + + + /*--------------------- Internal Data Structures --------------------------*/ + struct Node + { + /** + * Indices of points in leaf node + */ + int left, right; + /** + * Dimension used for subdivision. + */ + int divfeat; + /** + * The values used for subdivision. + */ + DistanceType divlow, divhigh; + /** + * The child nodes. + */ + Node* child1, * child2; + }; + typedef Node* NodePtr; + + + struct Interval + { + DistanceType low, high; + }; + + typedef std::vector BoundingBox; + + typedef BranchStruct BranchSt; + typedef BranchSt* Branch; + + + + + void save_tree(FILE* stream, NodePtr tree) + { + save_value(stream, *tree); + if (tree->child1!=NULL) { + save_tree(stream, tree->child1); + } + if (tree->child2!=NULL) { + save_tree(stream, tree->child2); + } + } + + + void load_tree(FILE* stream, NodePtr& tree) + { + tree = pool_.allocate(); + load_value(stream, *tree); + if (tree->child1!=NULL) { + load_tree(stream, tree->child1); + } + if (tree->child2!=NULL) { + load_tree(stream, tree->child2); + } + } + + + void computeBoundingBox(BoundingBox& bbox) + { + bbox.resize(dim_); + for (size_t i=0; ibbox[i].high) bbox[i].high = (DistanceType)dataset_[k][i]; + } + } + } + + + /** + * Create a tree node that subdivides the list of vecs from vind[first] + * to vind[last]. The routine is called recursively on each sublist. + * Place a pointer to this new tree node in the location pTree. + * + * Params: pTree = the new node to create + * first = index of the first vector + * last = index of the last vector + */ + NodePtr divideTree(int left, int right, BoundingBox& bbox) + { + NodePtr node = pool_.allocate(); // allocate memory + + /* If too few exemplars remain, then make this a leaf node. */ + if ( (right-left) <= leaf_max_size_) { + node->child1 = node->child2 = NULL; /* Mark as leaf node. */ + node->left = left; + node->right = right; + + // compute bounding-box of leaf points + for (size_t i=0; idataset_[vind_[k]][i]) bbox[i].low=(DistanceType)dataset_[vind_[k]][i]; + if (bbox[i].highdivfeat = cutfeat; + + BoundingBox left_bbox(bbox); + left_bbox[cutfeat].high = cutval; + node->child1 = divideTree(left, left+idx, left_bbox); + + BoundingBox right_bbox(bbox); + right_bbox[cutfeat].low = cutval; + node->child2 = divideTree(left+idx, right, right_bbox); + + node->divlow = left_bbox[cutfeat].high; + node->divhigh = right_bbox[cutfeat].low; + + for (size_t i=0; imax_elem) max_elem = val; + } + } + + void middleSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox) + { + // find the largest span from the approximate bounding box + ElementType max_span = bbox[0].high-bbox[0].low; + cutfeat = 0; + cutval = (bbox[0].high+bbox[0].low)/2; + for (size_t i=1; imax_span) { + max_span = span; + cutfeat = i; + cutval = (bbox[i].high+bbox[i].low)/2; + } + } + + // compute exact span on the found dimension + ElementType min_elem, max_elem; + computeMinMax(ind, count, cutfeat, min_elem, max_elem); + cutval = (min_elem+max_elem)/2; + max_span = max_elem - min_elem; + + // check if a dimension of a largest span exists + size_t k = cutfeat; + for (size_t i=0; imax_span) { + computeMinMax(ind, count, i, min_elem, max_elem); + span = max_elem - min_elem; + if (span>max_span) { + max_span = span; + cutfeat = i; + cutval = (min_elem+max_elem)/2; + } + } + } + int lim1, lim2; + planeSplit(ind, count, cutfeat, cutval, lim1, lim2); + + if (lim1>count/2) index = lim1; + else if (lim2max_span) { + max_span = span; + } + } + DistanceType max_spread = -1; + cutfeat = 0; + for (size_t i=0; i(DistanceType)((1-EPS)*max_span)) { + ElementType min_elem, max_elem; + computeMinMax(ind, count, cutfeat, min_elem, max_elem); + DistanceType spread = (DistanceType)(max_elem-min_elem); + if (spread>max_spread) { + cutfeat = (int)i; + max_spread = spread; + } + } + } + // split in the middle + DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2; + ElementType min_elem, max_elem; + computeMinMax(ind, count, cutfeat, min_elem, max_elem); + + if (split_valmax_elem) cutval = (DistanceType)max_elem; + else cutval = split_val; + + int lim1, lim2; + planeSplit(ind, count, cutfeat, cutval, lim1, lim2); + + if (lim1>count/2) index = lim1; + else if (lim2cutval + */ + void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2) + { + /* Move vector indices for left subtree to front of list. */ + int left = 0; + int right = count-1; + for (;; ) { + while (left<=right && dataset_[ind[left]][cutfeat]=cutval) --right; + if (left>right) break; + std::swap(ind[left], ind[right]); ++left; --right; + } + /* If either list is empty, it means that all remaining features + * are identical. Split in the middle to maintain a balanced tree. + */ + lim1 = left; + right = count-1; + for (;; ) { + while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left; + while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right; + if (left>right) break; + std::swap(ind[left], ind[right]); ++left; --right; + } + lim2 = left; + } + + DistanceType computeInitialDistances(const ElementType* vec, std::vector& dists) + { + DistanceType distsq = 0.0; + + for (size_t i = 0; i < dim_; ++i) { + if (vec[i] < root_bbox_[i].low) { + dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, (int)i); + distsq += dists[i]; + } + if (vec[i] > root_bbox_[i].high) { + dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, (int)i); + distsq += dists[i]; + } + } + + return distsq; + } + + /** + * Performs an exact search in the tree starting from a node. + */ + void searchLevel(ResultSet& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq, + std::vector& dists, const float epsError) + { + /* If this is a leaf node, then do check and return. */ + if ((node->child1 == NULL)&&(node->child2 == NULL)) { + DistanceType worst_dist = result_set.worstDist(); + for (int i=node->left; iright; ++i) { + int index = reorder_ ? i : vind_[i]; + DistanceType dist = distance_(vec, data_[index], dim_, worst_dist); + if (distdivfeat; + ElementType val = vec[idx]; + DistanceType diff1 = val - node->divlow; + DistanceType diff2 = val - node->divhigh; + + NodePtr bestChild; + NodePtr otherChild; + DistanceType cut_dist; + if ((diff1+diff2)<0) { + bestChild = node->child1; + otherChild = node->child2; + cut_dist = distance_.accum_dist(val, node->divhigh, idx); + } + else { + bestChild = node->child2; + otherChild = node->child1; + cut_dist = distance_.accum_dist( val, node->divlow, idx); + } + + /* Call recursively to search next level down. */ + searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError); + + DistanceType dst = dists[idx]; + mindistsq = mindistsq + cut_dist - dst; + dists[idx] = cut_dist; + if (mindistsq*epsError<=result_set.worstDist()) { + searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError); + } + dists[idx] = dst; + } + +private: + + /** + * The dataset used by this index + */ + const Matrix dataset_; + + IndexParams index_params_; + + int leaf_max_size_; + bool reorder_; + + + /** + * Array of indices to vectors in the dataset. + */ + std::vector vind_; + + Matrix data_; + + size_t size_; + size_t dim_; + + /** + * Array of k-d trees used to find neighbours. + */ + NodePtr root_node_; + + BoundingBox root_bbox_; + + /** + * Pooled memory allocator. + * + * Using a pooled memory allocator is more efficient + * than allocating memory directly when there is a large + * number small of memory allocations. + */ + PooledAllocator pool_; + + Distance distance_; +}; // class KDTree + +} + +#endif //OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ diff --git a/thirdparty/linux/include/opencv2/flann/kmeans_index.h b/thirdparty/linux/include/opencv2/flann/kmeans_index.h new file mode 100644 index 0000000..98ad0c8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/kmeans_index.h @@ -0,0 +1,1171 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_KMEANS_INDEX_H_ +#define OPENCV_FLANN_KMEANS_INDEX_H_ + +#include +#include +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "dist.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" +#include "logger.h" + + +namespace cvflann +{ + +struct KMeansIndexParams : public IndexParams +{ + KMeansIndexParams(int branching = 32, int iterations = 11, + flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 ) + { + (*this)["algorithm"] = FLANN_INDEX_KMEANS; + // branching factor + (*this)["branching"] = branching; + // max iterations to perform in one kmeans clustering (kmeans tree) + (*this)["iterations"] = iterations; + // algorithm used for picking the initial cluster centers for kmeans tree + (*this)["centers_init"] = centers_init; + // cluster boundary index. Used when searching the kmeans tree + (*this)["cb_index"] = cb_index; + } +}; + + +/** + * Hierarchical kmeans index + * + * Contains a tree constructed through a hierarchical kmeans clustering + * and other information for indexing a set of points for nearest-neighbour matching. + */ +template +class KMeansIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + + + typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&); + + /** + * The function used for choosing the cluster centers. + */ + centersAlgFunction chooseCenters; + + + + /** + * Chooses the initial centers in the k-means clustering in a random manner. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * indices_length = length of indices vector + * + */ + void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length) + { + UniqueRandom r(indices_length); + + int index; + for (index=0; index=0 && rnd < n); + + centers[0] = indices[rnd]; + + int index; + for (index=1; indexbest_val) { + best_val = dist; + best_index = j; + } + } + if (best_index!=-1) { + centers[index] = indices[best_index]; + } + else { + break; + } + } + centers_length = index; + } + + + /** + * Chooses the initial centers in the k-means using the algorithm + * proposed in the KMeans++ paper: + * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding + * + * Implementation of this function was converted from the one provided in Arthur's code. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * Returns: + */ + void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length) + { + int n = indices_length; + + double currentPot = 0; + DistanceType* closestDistSq = new DistanceType[n]; + + // Choose one random center and set the closestDistSq values + int index = rand_int(n); + assert(index >=0 && index < n); + centers[0] = indices[index]; + + for (int i = 0; i < n; i++) { + closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); + closestDistSq[i] = ensureSquareDistance( closestDistSq[i] ); + currentPot += closestDistSq[i]; + } + + + const int numLocalTries = 1; + + // Choose each center + int centerCount; + for (centerCount = 1; centerCount < k; centerCount++) { + + // Repeat several trials + double bestNewPot = -1; + int bestNewIndex = -1; + for (int localTrial = 0; localTrial < numLocalTries; localTrial++) { + + // Choose our center - have to be slightly careful to return a valid answer even accounting + // for possible rounding errors + double randVal = rand_double(currentPot); + for (index = 0; index < n-1; index++) { + if (randVal <= closestDistSq[index]) break; + else randVal -= closestDistSq[index]; + } + + // Compute the new potential + double newPot = 0; + for (int i = 0; i < n; i++) { + DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); + newPot += std::min( ensureSquareDistance(dist), closestDistSq[i] ); + } + + // Store the best result + if ((bestNewPot < 0)||(newPot < bestNewPot)) { + bestNewPot = newPot; + bestNewIndex = index; + } + } + + // Add the appropriate center + centers[centerCount] = indices[bestNewIndex]; + currentPot = bestNewPot; + for (int i = 0; i < n; i++) { + DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols); + closestDistSq[i] = std::min( ensureSquareDistance(dist), closestDistSq[i] ); + } + } + + centers_length = centerCount; + + delete[] closestDistSq; + } + + + +public: + + flann_algorithm_t getType() const + { + return FLANN_INDEX_KMEANS; + } + + class KMeansDistanceComputer : public cv::ParallelLoopBody + { + public: + KMeansDistanceComputer(Distance _distance, const Matrix& _dataset, + const int _branching, const int* _indices, const Matrix& _dcenters, const size_t _veclen, + int* _count, int* _belongs_to, std::vector& _radiuses, bool& _converged, cv::Mutex& _mtx) + : distance(_distance) + , dataset(_dataset) + , branching(_branching) + , indices(_indices) + , dcenters(_dcenters) + , veclen(_veclen) + , count(_count) + , belongs_to(_belongs_to) + , radiuses(_radiuses) + , converged(_converged) + , mtx(_mtx) + { + } + + void operator()(const cv::Range& range) const + { + const int begin = range.start; + const int end = range.end; + + for( int i = begin; inew_sq_dist) { + new_centroid = j; + sq_dist = new_sq_dist; + } + } + if (sq_dist > radiuses[new_centroid]) { + radiuses[new_centroid] = sq_dist; + } + if (new_centroid != belongs_to[i]) { + count[belongs_to[i]]--; + count[new_centroid]++; + belongs_to[i] = new_centroid; + mtx.lock(); + converged = false; + mtx.unlock(); + } + } + } + + private: + Distance distance; + const Matrix& dataset; + const int branching; + const int* indices; + const Matrix& dcenters; + const size_t veclen; + int* count; + int* belongs_to; + std::vector& radiuses; + bool& converged; + cv::Mutex& mtx; + KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; } + }; + + /** + * Index constructor + * + * Params: + * inputData = dataset with the input features + * params = parameters passed to the hierarchical k-means algorithm + */ + KMeansIndex(const Matrix& inputData, const IndexParams& params = KMeansIndexParams(), + Distance d = Distance()) + : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d) + { + memoryCounter_ = 0; + + size_ = dataset_.rows; + veclen_ = dataset_.cols; + + branching_ = get_param(params,"branching",32); + iterations_ = get_param(params,"iterations",11); + if (iterations_<0) { + iterations_ = (std::numeric_limits::max)(); + } + centers_init_ = get_param(params,"centers_init",FLANN_CENTERS_RANDOM); + + if (centers_init_==FLANN_CENTERS_RANDOM) { + chooseCenters = &KMeansIndex::chooseCentersRandom; + } + else if (centers_init_==FLANN_CENTERS_GONZALES) { + chooseCenters = &KMeansIndex::chooseCentersGonzales; + } + else if (centers_init_==FLANN_CENTERS_KMEANSPP) { + chooseCenters = &KMeansIndex::chooseCentersKMeanspp; + } + else { + throw FLANNException("Unknown algorithm for choosing initial centers."); + } + cb_index_ = 0.4f; + + } + + + KMeansIndex(const KMeansIndex&); + KMeansIndex& operator=(const KMeansIndex&); + + + /** + * Index destructor. + * + * Release the memory used by the index. + */ + virtual ~KMeansIndex() + { + if (root_ != NULL) { + free_centers(root_); + } + if (indices_!=NULL) { + delete[] indices_; + } + } + + /** + * Returns size of index. + */ + size_t size() const + { + return size_; + } + + /** + * Returns the length of an index feature. + */ + size_t veclen() const + { + return veclen_; + } + + + void set_cb_index( float index) + { + cb_index_ = index; + } + + /** + * Computes the inde memory usage + * Returns: memory used by the index + */ + int usedMemory() const + { + return pool_.usedMemory+pool_.wastedMemory+memoryCounter_; + } + + /** + * Builds the index + */ + void buildIndex() + { + if (branching_<2) { + throw FLANNException("Branching factor must be at least 2"); + } + + indices_ = new int[size_]; + for (size_t i=0; i(); + std::memset(root_, 0, sizeof(KMeansNode)); + + computeNodeStatistics(root_, indices_, (int)size_); + computeClustering(root_, indices_, (int)size_, branching_,0); + } + + + void saveIndex(FILE* stream) + { + save_value(stream, branching_); + save_value(stream, iterations_); + save_value(stream, memoryCounter_); + save_value(stream, cb_index_); + save_value(stream, *indices_, (int)size_); + + save_tree(stream, root_); + } + + + void loadIndex(FILE* stream) + { + load_value(stream, branching_); + load_value(stream, iterations_); + load_value(stream, memoryCounter_); + load_value(stream, cb_index_); + if (indices_!=NULL) { + delete[] indices_; + } + indices_ = new int[size_]; + load_value(stream, *indices_, size_); + + if (root_!=NULL) { + free_centers(root_); + } + load_tree(stream, root_); + + index_params_["algorithm"] = getType(); + index_params_["branching"] = branching_; + index_params_["iterations"] = iterations_; + index_params_["centers_init"] = centers_init_; + index_params_["cb_index"] = cb_index_; + + } + + + /** + * Find set of nearest neighbors to vec. Their indices are stored inside + * the result object. + * + * Params: + * result = the result object in which the indices of the nearest-neighbors are stored + * vec = the vector for which to search the nearest neighbors + * searchParams = parameters that influence the search algorithm (checks, cb_index) + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) + { + + int maxChecks = get_param(searchParams,"checks",32); + + if (maxChecks==FLANN_CHECKS_UNLIMITED) { + findExactNN(root_, result, vec); + } + else { + // Priority queue storing intermediate branches in the best-bin-first search + Heap* heap = new Heap((int)size_); + + int checks = 0; + findNN(root_, result, vec, checks, maxChecks, heap); + + BranchSt branch; + while (heap->popMin(branch) && (checks& centers) + { + int numClusters = centers.rows; + if (numClusters<1) { + throw FLANNException("Number of clusters must be at least 1"); + } + + DistanceType variance; + KMeansNodePtr* clusters = new KMeansNodePtr[numClusters]; + + int clusterCount = getMinVarianceClusters(root_, clusters, numClusters, variance); + + Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount); + + for (int i=0; ipivot; + for (size_t j=0; j BranchSt; + + + + + void save_tree(FILE* stream, KMeansNodePtr node) + { + save_value(stream, *node); + save_value(stream, *(node->pivot), (int)veclen_); + if (node->childs==NULL) { + int indices_offset = (int)(node->indices - indices_); + save_value(stream, indices_offset); + } + else { + for(int i=0; ichilds[i]); + } + } + } + + + void load_tree(FILE* stream, KMeansNodePtr& node) + { + node = pool_.allocate(); + load_value(stream, *node); + node->pivot = new DistanceType[veclen_]; + load_value(stream, *(node->pivot), (int)veclen_); + if (node->childs==NULL) { + int indices_offset; + load_value(stream, indices_offset); + node->indices = indices_ + indices_offset; + } + else { + node->childs = pool_.allocate(branching_); + for(int i=0; ichilds[i]); + } + } + } + + + /** + * Helper function + */ + void free_centers(KMeansNodePtr node) + { + delete[] node->pivot; + if (node->childs!=NULL) { + for (int k=0; kchilds[k]); + } + } + } + + /** + * Computes the statistics of a node (mean, radius, variance). + * + * Params: + * node = the node to use + * indices = the indices of the points belonging to the node + */ + void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length) + { + + DistanceType radius = 0; + DistanceType variance = 0; + DistanceType* mean = new DistanceType[veclen_]; + memoryCounter_ += int(veclen_*sizeof(DistanceType)); + + memset(mean,0,veclen_*sizeof(DistanceType)); + + for (size_t i=0; i(), veclen_); + } + for (size_t j=0; j(), veclen_); + + DistanceType tmp = 0; + for (int i=0; iradius) { + radius = tmp; + } + } + + node->variance = variance; + node->radius = radius; + node->pivot = mean; + } + + + /** + * The method responsible with actually doing the recursive hierarchical + * clustering + * + * Params: + * node = the node to cluster + * indices = indices of the points belonging to the current node + * branching = the branching factor to use in the clustering + * + * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point) + */ + void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level) + { + node->size = indices_length; + node->level = level; + + if (indices_length < branching) { + node->indices = indices; + std::sort(node->indices,node->indices+indices_length); + node->childs = NULL; + return; + } + + cv::AutoBuffer centers_idx_buf(branching); + int* centers_idx = (int*)centers_idx_buf; + int centers_length; + (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length); + + if (centers_lengthindices = indices; + std::sort(node->indices,node->indices+indices_length); + node->childs = NULL; + return; + } + + + cv::AutoBuffer dcenters_buf(branching*veclen_); + Matrix dcenters((double*)dcenters_buf,branching,veclen_); + for (int i=0; i radiuses(branching); + cv::AutoBuffer count_buf(branching); + int* count = (int*)count_buf; + for (int i=0; i belongs_to_buf(indices_length); + int* belongs_to = (int*)belongs_to_buf; + for (int i=0; inew_sq_dist) { + belongs_to[i] = j; + sq_dist = new_sq_dist; + } + } + if (sq_dist>radiuses[belongs_to[i]]) { + radiuses[belongs_to[i]] = sq_dist; + } + count[belongs_to[i]]++; + } + + bool converged = false; + int iteration = 0; + while (!converged && iterationchilds = pool_.allocate(branching); + int start = 0; + int end = start; + for (int c=0; c(), veclen_); + variance += d; + mean_radius += sqrt(d); + std::swap(indices[i],indices[end]); + std::swap(belongs_to[i],belongs_to[end]); + end++; + } + } + variance /= s; + mean_radius /= s; + variance -= distance_(centers[c], ZeroIterator(), veclen_); + + node->childs[c] = pool_.allocate(); + std::memset(node->childs[c], 0, sizeof(KMeansNode)); + node->childs[c]->radius = radiuses[c]; + node->childs[c]->pivot = centers[c]; + node->childs[c]->variance = variance; + node->childs[c]->mean_radius = mean_radius; + computeClustering(node->childs[c],indices+start, end-start, branching, level+1); + start=end; + } + + delete[] centers; + } + + + + /** + * Performs one descent in the hierarchical k-means tree. The branches not + * visited are stored in a priority queue. + * + * Params: + * node = node to explore + * result = container for the k-nearest neighbors found + * vec = query points + * checks = how many points in the dataset have been checked so far + * maxChecks = maximum dataset points to checks + */ + + + void findNN(KMeansNodePtr node, ResultSet& result, const ElementType* vec, int& checks, int maxChecks, + Heap* heap) + { + // Ignore those clusters that are too far away + { + DistanceType bsq = distance_(vec, node->pivot, veclen_); + DistanceType rsq = node->radius; + DistanceType wsq = result.worstDist(); + + DistanceType val = bsq-rsq-wsq; + DistanceType val2 = val*val-4*rsq*wsq; + + //if (val>0) { + if ((val>0)&&(val2>0)) { + return; + } + } + + if (node->childs==NULL) { + if (checks>=maxChecks) { + if (result.full()) return; + } + checks += node->size; + for (int i=0; isize; ++i) { + int index = node->indices[i]; + DistanceType dist = distance_(dataset_[index], vec, veclen_); + result.addPoint(dist, index); + } + } + else { + DistanceType* domain_distances = new DistanceType[branching_]; + int closest_center = exploreNodeBranches(node, vec, domain_distances, heap); + delete[] domain_distances; + findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap); + } + } + + /** + * Helper function that computes the nearest childs of a node to a given query point. + * Params: + * node = the node + * q = the query point + * distances = array with the distances to each child node. + * Returns: + */ + int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, Heap* heap) + { + + int best_index = 0; + domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_); + for (int i=1; ichilds[i]->pivot, veclen_); + if (domain_distances[i]childs[best_index]->pivot; + for (int i=0; ichilds[i]->variance; + + // float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q); + // if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i])); + } + } + + return best_index; + } + + + /** + * Function the performs exact nearest neighbor search by traversing the entire tree. + */ + void findExactNN(KMeansNodePtr node, ResultSet& result, const ElementType* vec) + { + // Ignore those clusters that are too far away + { + DistanceType bsq = distance_(vec, node->pivot, veclen_); + DistanceType rsq = node->radius; + DistanceType wsq = result.worstDist(); + + DistanceType val = bsq-rsq-wsq; + DistanceType val2 = val*val-4*rsq*wsq; + + // if (val>0) { + if ((val>0)&&(val2>0)) { + return; + } + } + + + if (node->childs==NULL) { + for (int i=0; isize; ++i) { + int index = node->indices[i]; + DistanceType dist = distance_(dataset_[index], vec, veclen_); + result.addPoint(dist, index); + } + } + else { + int* sort_indices = new int[branching_]; + + getCenterOrdering(node, vec, sort_indices); + + for (int i=0; ichilds[sort_indices[i]],result,vec); + } + + delete[] sort_indices; + } + } + + + /** + * Helper function. + * + * I computes the order in which to traverse the child nodes of a particular node. + */ + void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices) + { + DistanceType* domain_distances = new DistanceType[branching_]; + for (int i=0; ichilds[i]->pivot, veclen_); + + int j=0; + while (domain_distances[j]j; --k) { + domain_distances[k] = domain_distances[k-1]; + sort_indices[k] = sort_indices[k-1]; + } + domain_distances[j] = dist; + sort_indices[j] = i; + } + delete[] domain_distances; + } + + /** + * Method that computes the squared distance from the query point q + * from inside region with center c to the border between this + * region and the region with center p + */ + DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q) + { + DistanceType sum = 0; + DistanceType sum2 = 0; + + for (int i=0; ivariance*root->size; + + while (clusterCount::max)(); + int splitIndex = -1; + + for (int i=0; ichilds != NULL) { + + DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size; + + for (int j=0; jchilds[j]->variance*clusters[i]->childs[j]->size; + } + if (variance clusters_length) break; + + meanVariance = minVariance; + + // split node + KMeansNodePtr toSplit = clusters[splitIndex]; + clusters[splitIndex] = toSplit->childs[0]; + for (int i=1; ichilds[i]; + } + } + + varianceValue = meanVariance/root->size; + return clusterCount; + } + +private: + /** The branching factor used in the hierarchical k-means clustering */ + int branching_; + + /** Maximum number of iterations to use when performing k-means clustering */ + int iterations_; + + /** Algorithm for choosing the cluster centers */ + flann_centers_init_t centers_init_; + + /** + * Cluster border index. This is used in the tree search phase when determining + * the closest cluster to explore next. A zero value takes into account only + * the cluster centres, a value greater then zero also take into account the size + * of the cluster. + */ + float cb_index_; + + /** + * The dataset used by this index + */ + const Matrix dataset_; + + /** Index parameters */ + IndexParams index_params_; + + /** + * Number of features in the dataset. + */ + size_t size_; + + /** + * Length of each feature. + */ + size_t veclen_; + + /** + * The root node in the tree. + */ + KMeansNodePtr root_; + + /** + * Array of indices to vectors in the dataset. + */ + int* indices_; + + /** + * The distance + */ + Distance distance_; + + /** + * Pooled memory allocator. + */ + PooledAllocator pool_; + + /** + * Memory occupied by the index. + */ + int memoryCounter_; +}; + +} + +#endif //OPENCV_FLANN_KMEANS_INDEX_H_ diff --git a/thirdparty/linux/include/opencv2/flann/linear_index.h b/thirdparty/linux/include/opencv2/flann/linear_index.h new file mode 100644 index 0000000..5aa7a5c --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/linear_index.h @@ -0,0 +1,132 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_LINEAR_INDEX_H_ +#define OPENCV_FLANN_LINEAR_INDEX_H_ + +#include "general.h" +#include "nn_index.h" + +namespace cvflann +{ + +struct LinearIndexParams : public IndexParams +{ + LinearIndexParams() + { + (* this)["algorithm"] = FLANN_INDEX_LINEAR; + } +}; + +template +class LinearIndex : public NNIndex +{ +public: + + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + + LinearIndex(const Matrix& inputData, const IndexParams& params = LinearIndexParams(), + Distance d = Distance()) : + dataset_(inputData), index_params_(params), distance_(d) + { + } + + LinearIndex(const LinearIndex&); + LinearIndex& operator=(const LinearIndex&); + + flann_algorithm_t getType() const + { + return FLANN_INDEX_LINEAR; + } + + + size_t size() const + { + return dataset_.rows; + } + + size_t veclen() const + { + return dataset_.cols; + } + + + int usedMemory() const + { + return 0; + } + + void buildIndex() + { + /* nothing to do here for linear search */ + } + + void saveIndex(FILE*) + { + /* nothing to do here for linear search */ + } + + + void loadIndex(FILE*) + { + /* nothing to do here for linear search */ + + index_params_["algorithm"] = getType(); + } + + void findNeighbors(ResultSet& resultSet, const ElementType* vec, const SearchParams& /*searchParams*/) + { + ElementType* data = dataset_.data; + for (size_t i = 0; i < dataset_.rows; ++i, data += dataset_.cols) { + DistanceType dist = distance_(data, vec, dataset_.cols); + resultSet.addPoint(dist, (int)i); + } + } + + IndexParams getParameters() const + { + return index_params_; + } + +private: + /** The dataset */ + const Matrix dataset_; + /** Index parameters */ + IndexParams index_params_; + /** Index distance */ + Distance distance_; + +}; + +} + +#endif // OPENCV_FLANN_LINEAR_INDEX_H_ diff --git a/thirdparty/linux/include/opencv2/flann/logger.h b/thirdparty/linux/include/opencv2/flann/logger.h new file mode 100644 index 0000000..24f3fb6 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/logger.h @@ -0,0 +1,130 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_LOGGER_H +#define OPENCV_FLANN_LOGGER_H + +#include +#include + +#include "defines.h" + + +namespace cvflann +{ + +class Logger +{ + Logger() : stream(stdout), logLevel(FLANN_LOG_WARN) {} + + ~Logger() + { + if ((stream!=NULL)&&(stream!=stdout)) { + fclose(stream); + } + } + + static Logger& instance() + { + static Logger logger; + return logger; + } + + void _setDestination(const char* name) + { + if (name==NULL) { + stream = stdout; + } + else { + stream = fopen(name,"w"); + if (stream == NULL) { + stream = stdout; + } + } + } + + int _log(int level, const char* fmt, va_list arglist) + { + if (level > logLevel ) return -1; + int ret = vfprintf(stream, fmt, arglist); + return ret; + } + +public: + /** + * Sets the logging level. All messages with lower priority will be ignored. + * @param level Logging level + */ + static void setLevel(int level) { instance().logLevel = level; } + + /** + * Sets the logging destination + * @param name Filename or NULL for console + */ + static void setDestination(const char* name) { instance()._setDestination(name); } + + /** + * Print log message + * @param level Log level + * @param fmt Message format + * @return + */ + static int log(int level, const char* fmt, ...) + { + va_list arglist; + va_start(arglist, fmt); + int ret = instance()._log(level,fmt,arglist); + va_end(arglist); + return ret; + } + +#define LOG_METHOD(NAME,LEVEL) \ + static int NAME(const char* fmt, ...) \ + { \ + va_list ap; \ + va_start(ap, fmt); \ + int ret = instance()._log(LEVEL, fmt, ap); \ + va_end(ap); \ + return ret; \ + } + + LOG_METHOD(fatal, FLANN_LOG_FATAL) + LOG_METHOD(error, FLANN_LOG_ERROR) + LOG_METHOD(warn, FLANN_LOG_WARN) + LOG_METHOD(info, FLANN_LOG_INFO) + +private: + FILE* stream; + int logLevel; +}; + +} + +#endif //OPENCV_FLANN_LOGGER_H diff --git a/thirdparty/linux/include/opencv2/flann/lsh_index.h b/thirdparty/linux/include/opencv2/flann/lsh_index.h new file mode 100644 index 0000000..4d4670e --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/lsh_index.h @@ -0,0 +1,392 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +/*********************************************************************** + * Author: Vincent Rabaud + *************************************************************************/ + +#ifndef OPENCV_FLANN_LSH_INDEX_H_ +#define OPENCV_FLANN_LSH_INDEX_H_ + +#include +#include +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "lsh_table.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" + +namespace cvflann +{ + +struct LshIndexParams : public IndexParams +{ + LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2) + { + (* this)["algorithm"] = FLANN_INDEX_LSH; + // The number of hash tables to use + (*this)["table_number"] = table_number; + // The length of the key in the hash tables + (*this)["key_size"] = key_size; + // Number of levels to use in multi-probe (0 for standard LSH) + (*this)["multi_probe_level"] = multi_probe_level; + } +}; + +/** + * Randomized kd-tree index + * + * Contains the k-d trees and other information for indexing a set of points + * for nearest-neighbor matching. + */ +template +class LshIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + /** Constructor + * @param input_data dataset with the input features + * @param params parameters passed to the LSH algorithm + * @param d the distance used + */ + LshIndex(const Matrix& input_data, const IndexParams& params = LshIndexParams(), + Distance d = Distance()) : + dataset_(input_data), index_params_(params), distance_(d) + { + // cv::flann::IndexParams sets integer params as 'int', so it is used with get_param + // in place of 'unsigned int' + table_number_ = (unsigned int)get_param(index_params_,"table_number",12); + key_size_ = (unsigned int)get_param(index_params_,"key_size",20); + multi_probe_level_ = (unsigned int)get_param(index_params_,"multi_probe_level",2); + + feature_size_ = (unsigned)dataset_.cols; + fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_); + } + + + LshIndex(const LshIndex&); + LshIndex& operator=(const LshIndex&); + + /** + * Builds the index + */ + void buildIndex() + { + tables_.resize(table_number_); + for (unsigned int i = 0; i < table_number_; ++i) { + lsh::LshTable& table = tables_[i]; + table = lsh::LshTable(feature_size_, key_size_); + + // Add the features to the table + table.add(dataset_); + } + } + + flann_algorithm_t getType() const + { + return FLANN_INDEX_LSH; + } + + + void saveIndex(FILE* stream) + { + save_value(stream,table_number_); + save_value(stream,key_size_); + save_value(stream,multi_probe_level_); + save_value(stream, dataset_); + } + + void loadIndex(FILE* stream) + { + load_value(stream, table_number_); + load_value(stream, key_size_); + load_value(stream, multi_probe_level_); + load_value(stream, dataset_); + // Building the index is so fast we can afford not storing it + buildIndex(); + + index_params_["algorithm"] = getType(); + index_params_["table_number"] = table_number_; + index_params_["key_size"] = key_size_; + index_params_["multi_probe_level"] = multi_probe_level_; + } + + /** + * Returns size of index. + */ + size_t size() const + { + return dataset_.rows; + } + + /** + * Returns the length of an index feature. + */ + size_t veclen() const + { + return feature_size_; + } + + /** + * Computes the index memory usage + * Returns: memory used by the index + */ + int usedMemory() const + { + return (int)(dataset_.rows * sizeof(int)); + } + + + IndexParams getParameters() const + { + return index_params_; + } + + /** + * \brief Perform k-nearest neighbor search + * \param[in] queries The query points for which to find the nearest neighbors + * \param[out] indices The indices of the nearest neighbors found + * \param[out] dists Distances to the nearest neighbors found + * \param[in] knn Number of nearest neighbors to return + * \param[in] params Search parameters + */ + virtual void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) + { + assert(queries.cols == veclen()); + assert(indices.rows >= queries.rows); + assert(dists.rows >= queries.rows); + assert(int(indices.cols) >= knn); + assert(int(dists.cols) >= knn); + + + KNNUniqueResultSet resultSet(knn); + for (size_t i = 0; i < queries.rows; i++) { + resultSet.clear(); + std::fill_n(indices[i], knn, -1); + std::fill_n(dists[i], knn, std::numeric_limits::max()); + findNeighbors(resultSet, queries[i], params); + if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn); + else resultSet.copy(indices[i], dists[i], knn); + } + } + + + /** + * Find set of nearest neighbors to vec. Their indices are stored inside + * the result object. + * + * Params: + * result = the result object in which the indices of the nearest-neighbors are stored + * vec = the vector for which to search the nearest neighbors + * maxCheck = the maximum number of restarts (in a best-bin-first manner) + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& /*searchParams*/) + { + getNeighbors(vec, result); + } + +private: + /** Defines the comparator on score and index + */ + typedef std::pair ScoreIndexPair; + struct SortScoreIndexPairOnSecond + { + bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const + { + return left.second < right.second; + } + }; + + /** Fills the different xor masks to use when getting the neighbors in multi-probe LSH + * @param key the key we build neighbors from + * @param lowest_index the lowest index of the bit set + * @param level the multi-probe level we are at + * @param xor_masks all the xor mask + */ + void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level, + std::vector& xor_masks) + { + xor_masks.push_back(key); + if (level == 0) return; + for (int index = lowest_index - 1; index >= 0; --index) { + // Create a new key + lsh::BucketKey new_key = key | (1 << index); + fill_xor_mask(new_key, index, level - 1, xor_masks); + } + } + + /** Performs the approximate nearest-neighbor search. + * @param vec the feature to analyze + * @param do_radius flag indicating if we check the radius too + * @param radius the radius if it is a radius search + * @param do_k flag indicating if we limit the number of nn + * @param k_nn the number of nearest neighbors + * @param checked_average used for debugging + */ + void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn, + float& /*checked_average*/) + { + static std::vector score_index_heap; + + if (do_k) { + unsigned int worst_score = std::numeric_limits::max(); + typename std::vector >::const_iterator table = tables_.begin(); + typename std::vector >::const_iterator table_end = tables_.end(); + for (; table != table_end; ++table) { + size_t key = table->getKey(vec); + std::vector::const_iterator xor_mask = xor_masks_.begin(); + std::vector::const_iterator xor_mask_end = xor_masks_.end(); + for (; xor_mask != xor_mask_end; ++xor_mask) { + size_t sub_key = key ^ (*xor_mask); + const lsh::Bucket* bucket = table->getBucketFromKey(sub_key); + if (bucket == 0) continue; + + // Go over each descriptor index + std::vector::const_iterator training_index = bucket->begin(); + std::vector::const_iterator last_training_index = bucket->end(); + DistanceType hamming_distance; + + // Process the rest of the candidates + for (; training_index < last_training_index; ++training_index) { + hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); + + if (hamming_distance < worst_score) { + // Insert the new element + score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index)); + std::push_heap(score_index_heap.begin(), score_index_heap.end()); + + if (score_index_heap.size() > (unsigned int)k_nn) { + // Remove the highest distance value as we have too many elements + std::pop_heap(score_index_heap.begin(), score_index_heap.end()); + score_index_heap.pop_back(); + // Keep track of the worst score + worst_score = score_index_heap.front().first; + } + } + } + } + } + } + else { + typename std::vector >::const_iterator table = tables_.begin(); + typename std::vector >::const_iterator table_end = tables_.end(); + for (; table != table_end; ++table) { + size_t key = table->getKey(vec); + std::vector::const_iterator xor_mask = xor_masks_.begin(); + std::vector::const_iterator xor_mask_end = xor_masks_.end(); + for (; xor_mask != xor_mask_end; ++xor_mask) { + size_t sub_key = key ^ (*xor_mask); + const lsh::Bucket* bucket = table->getBucketFromKey(sub_key); + if (bucket == 0) continue; + + // Go over each descriptor index + std::vector::const_iterator training_index = bucket->begin(); + std::vector::const_iterator last_training_index = bucket->end(); + DistanceType hamming_distance; + + // Process the rest of the candidates + for (; training_index < last_training_index; ++training_index) { + // Compute the Hamming distance + hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); + if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index)); + } + } + } + } + } + + /** Performs the approximate nearest-neighbor search. + * This is a slower version than the above as it uses the ResultSet + * @param vec the feature to analyze + */ + void getNeighbors(const ElementType* vec, ResultSet& result) + { + typename std::vector >::const_iterator table = tables_.begin(); + typename std::vector >::const_iterator table_end = tables_.end(); + for (; table != table_end; ++table) { + size_t key = table->getKey(vec); + std::vector::const_iterator xor_mask = xor_masks_.begin(); + std::vector::const_iterator xor_mask_end = xor_masks_.end(); + for (; xor_mask != xor_mask_end; ++xor_mask) { + size_t sub_key = key ^ (*xor_mask); + const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key); + if (bucket == 0) continue; + + // Go over each descriptor index + std::vector::const_iterator training_index = bucket->begin(); + std::vector::const_iterator last_training_index = bucket->end(); + DistanceType hamming_distance; + + // Process the rest of the candidates + for (; training_index < last_training_index; ++training_index) { + // Compute the Hamming distance + hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols); + result.addPoint(hamming_distance, *training_index); + } + } + } + } + + /** The different hash tables */ + std::vector > tables_; + + /** The data the LSH tables where built from */ + Matrix dataset_; + + /** The size of the features (as ElementType[]) */ + unsigned int feature_size_; + + IndexParams index_params_; + + /** table number */ + unsigned int table_number_; + /** key size */ + unsigned int key_size_; + /** How far should we look for neighbors in multi-probe LSH */ + unsigned int multi_probe_level_; + + /** The XOR masks to apply to a key to get the neighboring buckets */ + std::vector xor_masks_; + + Distance distance_; +}; +} + +#endif //OPENCV_FLANN_LSH_INDEX_H_ diff --git a/thirdparty/linux/include/opencv2/flann/lsh_table.h b/thirdparty/linux/include/opencv2/flann/lsh_table.h new file mode 100644 index 0000000..8ef2bd3 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/lsh_table.h @@ -0,0 +1,492 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +/*********************************************************************** + * Author: Vincent Rabaud + *************************************************************************/ + +#ifndef OPENCV_FLANN_LSH_TABLE_H_ +#define OPENCV_FLANN_LSH_TABLE_H_ + +#include +#include +#include +#include +// TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP +#ifdef __GXX_EXPERIMENTAL_CXX0X__ +# define USE_UNORDERED_MAP 1 +#else +# define USE_UNORDERED_MAP 0 +#endif +#if USE_UNORDERED_MAP +#include +#else +#include +#endif +#include +#include + +#include "dynamic_bitset.h" +#include "matrix.h" + +namespace cvflann +{ + +namespace lsh +{ + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** What is stored in an LSH bucket + */ +typedef uint32_t FeatureIndex; +/** The id from which we can get a bucket back in an LSH table + */ +typedef unsigned int BucketKey; + +/** A bucket in an LSH table + */ +typedef std::vector Bucket; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** POD for stats about an LSH table + */ +struct LshStats +{ + std::vector bucket_sizes_; + size_t n_buckets_; + size_t bucket_size_mean_; + size_t bucket_size_median_; + size_t bucket_size_min_; + size_t bucket_size_max_; + size_t bucket_size_std_dev; + /** Each contained vector contains three value: beginning/end for interval, number of elements in the bin + */ + std::vector > size_histogram_; +}; + +/** Overload the << operator for LshStats + * @param out the streams + * @param stats the stats to display + * @return the streams + */ +inline std::ostream& operator <<(std::ostream& out, const LshStats& stats) +{ + int w = 20; + out << "Lsh Table Stats:\n" << std::setw(w) << std::setiosflags(std::ios::right) << "N buckets : " + << stats.n_buckets_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "mean size : " + << std::setiosflags(std::ios::left) << stats.bucket_size_mean_ << "\n" << std::setw(w) + << std::setiosflags(std::ios::right) << "median size : " << stats.bucket_size_median_ << "\n" << std::setw(w) + << std::setiosflags(std::ios::right) << "min size : " << std::setiosflags(std::ios::left) + << stats.bucket_size_min_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "max size : " + << std::setiosflags(std::ios::left) << stats.bucket_size_max_; + + // Display the histogram + out << std::endl << std::setw(w) << std::setiosflags(std::ios::right) << "histogram : " + << std::setiosflags(std::ios::left); + for (std::vector >::const_iterator iterator = stats.size_histogram_.begin(), end = + stats.size_histogram_.end(); iterator != end; ++iterator) out << (*iterator)[0] << "-" << (*iterator)[1] << ": " << (*iterator)[2] << ", "; + + return out; +} + + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Lsh hash table. As its key is a sub-feature, and as usually + * the size of it is pretty small, we keep it as a continuous memory array. + * The value is an index in the corpus of features (we keep it as an unsigned + * int for pure memory reasons, it could be a size_t) + */ +template +class LshTable +{ +public: + /** A container of all the feature indices. Optimized for space + */ +#if USE_UNORDERED_MAP + typedef std::unordered_map BucketsSpace; +#else + typedef std::map BucketsSpace; +#endif + + /** A container of all the feature indices. Optimized for speed + */ + typedef std::vector BucketsSpeed; + + /** Default constructor + */ + LshTable() + { + } + + /** Default constructor + * Create the mask and allocate the memory + * @param feature_size is the size of the feature (considered as a ElementType[]) + * @param key_size is the number of bits that are turned on in the feature + */ + LshTable(unsigned int feature_size, unsigned int key_size) + { + (void)feature_size; + (void)key_size; + std::cerr << "LSH is not implemented for that type" << std::endl; + assert(0); + } + + /** Add a feature to the table + * @param value the value to store for that feature + * @param feature the feature itself + */ + void add(unsigned int value, const ElementType* feature) + { + // Add the value to the corresponding bucket + BucketKey key = (lsh::BucketKey)getKey(feature); + + switch (speed_level_) { + case kArray: + // That means we get the buckets from an array + buckets_speed_[key].push_back(value); + break; + case kBitsetHash: + // That means we can check the bitset for the presence of a key + key_bitset_.set(key); + buckets_space_[key].push_back(value); + break; + case kHash: + { + // That means we have to check for the hash table for the presence of a key + buckets_space_[key].push_back(value); + break; + } + } + } + + /** Add a set of features to the table + * @param dataset the values to store + */ + void add(Matrix dataset) + { +#if USE_UNORDERED_MAP + buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2); +#endif + // Add the features to the table + for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]); + // Now that the table is full, optimize it for speed/space + optimize(); + } + + /** Get a bucket given the key + * @param key + * @return + */ + inline const Bucket* getBucketFromKey(BucketKey key) const + { + // Generate other buckets + switch (speed_level_) { + case kArray: + // That means we get the buckets from an array + return &buckets_speed_[key]; + break; + case kBitsetHash: + // That means we can check the bitset for the presence of a key + if (key_bitset_.test(key)) return &buckets_space_.find(key)->second; + else return 0; + break; + case kHash: + { + // That means we have to check for the hash table for the presence of a key + BucketsSpace::const_iterator bucket_it, bucket_end = buckets_space_.end(); + bucket_it = buckets_space_.find(key); + // Stop here if that bucket does not exist + if (bucket_it == bucket_end) return 0; + else return &bucket_it->second; + break; + } + } + return 0; + } + + /** Compute the sub-signature of a feature + */ + size_t getKey(const ElementType* /*feature*/) const + { + std::cerr << "LSH is not implemented for that type" << std::endl; + assert(0); + return 1; + } + + /** Get statistics about the table + * @return + */ + LshStats getStats() const; + +private: + /** defines the speed fo the implementation + * kArray uses a vector for storing data + * kBitsetHash uses a hash map but checks for the validity of a key with a bitset + * kHash uses a hash map only + */ + enum SpeedLevel + { + kArray, kBitsetHash, kHash + }; + + /** Initialize some variables + */ + void initialize(size_t key_size) + { + const size_t key_size_lower_bound = 1; + //a value (size_t(1) << key_size) must fit the size_t type so key_size has to be strictly less than size of size_t + const size_t key_size_upper_bound = (std::min)(sizeof(BucketKey) * CHAR_BIT + 1, sizeof(size_t) * CHAR_BIT); + if (key_size < key_size_lower_bound || key_size >= key_size_upper_bound) + { + CV_Error(cv::Error::StsBadArg, cv::format("Invalid key_size (=%d). Valid values for your system are %d <= key_size < %d.", (int)key_size, (int)key_size_lower_bound, (int)key_size_upper_bound)); + } + + speed_level_ = kHash; + key_size_ = (unsigned)key_size; + } + + /** Optimize the table for speed/space + */ + void optimize() + { + // If we are already using the fast storage, no need to do anything + if (speed_level_ == kArray) return; + + // Use an array if it will be more than half full + if (buckets_space_.size() > ((size_t(1) << key_size_) / 2)) { + speed_level_ = kArray; + // Fill the array version of it + buckets_speed_.resize(size_t(1) << key_size_); + for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) buckets_speed_[key_bucket->first] = key_bucket->second; + + // Empty the hash table + buckets_space_.clear(); + return; + } + + // If the bitset is going to use less than 10% of the RAM of the hash map (at least 1 size_t for the key and two + // for the vector) or less than 512MB (key_size_ <= 30) + if (((std::max(buckets_space_.size(), buckets_speed_.size()) * CHAR_BIT * 3 * sizeof(BucketKey)) / 10 + >= (size_t(1) << key_size_)) || (key_size_ <= 32)) { + speed_level_ = kBitsetHash; + key_bitset_.resize(size_t(1) << key_size_); + key_bitset_.reset(); + // Try with the BucketsSpace + for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) key_bitset_.set(key_bucket->first); + } + else { + speed_level_ = kHash; + key_bitset_.clear(); + } + } + + /** The vector of all the buckets if they are held for speed + */ + BucketsSpeed buckets_speed_; + + /** The hash table of all the buckets in case we cannot use the speed version + */ + BucketsSpace buckets_space_; + + /** What is used to store the data */ + SpeedLevel speed_level_; + + /** If the subkey is small enough, it will keep track of which subkeys are set through that bitset + * That is just a speedup so that we don't look in the hash table (which can be mush slower that checking a bitset) + */ + DynamicBitset key_bitset_; + + /** The size of the sub-signature in bits + */ + unsigned int key_size_; + + // Members only used for the unsigned char specialization + /** The mask to apply to a feature to get the hash key + * Only used in the unsigned char case + */ + std::vector mask_; +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// +// Specialization for unsigned char + +template<> +inline LshTable::LshTable(unsigned int feature_size, unsigned int subsignature_size) +{ + initialize(subsignature_size); + // Allocate the mask + mask_ = std::vector((size_t)ceil((float)(feature_size * sizeof(char)) / (float)sizeof(size_t)), 0); + + // A bit brutal but fast to code + std::vector indices(feature_size * CHAR_BIT); + for (size_t i = 0; i < feature_size * CHAR_BIT; ++i) indices[i] = i; + std::random_shuffle(indices.begin(), indices.end()); + + // Generate a random set of order of subsignature_size_ bits + for (unsigned int i = 0; i < key_size_; ++i) { + size_t index = indices[i]; + + // Set that bit in the mask + size_t divisor = CHAR_BIT * sizeof(size_t); + size_t idx = index / divisor; //pick the right size_t index + mask_[idx] |= size_t(1) << (index % divisor); //use modulo to find the bit offset + } + + // Set to 1 if you want to display the mask for debug +#if 0 + { + size_t bcount = 0; + BOOST_FOREACH(size_t mask_block, mask_){ + out << std::setw(sizeof(size_t) * CHAR_BIT / 4) << std::setfill('0') << std::hex << mask_block + << std::endl; + bcount += __builtin_popcountll(mask_block); + } + out << "bit count : " << std::dec << bcount << std::endl; + out << "mask size : " << mask_.size() << std::endl; + return out; + } +#endif +} + +/** Return the Subsignature of a feature + * @param feature the feature to analyze + */ +template<> +inline size_t LshTable::getKey(const unsigned char* feature) const +{ + // no need to check if T is dividable by sizeof(size_t) like in the Hamming + // distance computation as we have a mask + const size_t* feature_block_ptr = reinterpret_cast ((const void*)feature); + + // Figure out the subsignature of the feature + // Given the feature ABCDEF, and the mask 001011, the output will be + // 000CEF + size_t subsignature = 0; + size_t bit_index = 1; + + for (std::vector::const_iterator pmask_block = mask_.begin(); pmask_block != mask_.end(); ++pmask_block) { + // get the mask and signature blocks + size_t feature_block = *feature_block_ptr; + size_t mask_block = *pmask_block; + while (mask_block) { + // Get the lowest set bit in the mask block + size_t lowest_bit = mask_block & (-(ptrdiff_t)mask_block); + // Add it to the current subsignature if necessary + subsignature += (feature_block & lowest_bit) ? bit_index : 0; + // Reset the bit in the mask block + mask_block ^= lowest_bit; + // increment the bit index for the subsignature + bit_index <<= 1; + } + // Check the next feature block + ++feature_block_ptr; + } + return subsignature; +} + +template<> +inline LshStats LshTable::getStats() const +{ + LshStats stats; + stats.bucket_size_mean_ = 0; + if ((buckets_speed_.empty()) && (buckets_space_.empty())) { + stats.n_buckets_ = 0; + stats.bucket_size_median_ = 0; + stats.bucket_size_min_ = 0; + stats.bucket_size_max_ = 0; + return stats; + } + + if (!buckets_speed_.empty()) { + for (BucketsSpeed::const_iterator pbucket = buckets_speed_.begin(); pbucket != buckets_speed_.end(); ++pbucket) { + stats.bucket_sizes_.push_back((lsh::FeatureIndex)pbucket->size()); + stats.bucket_size_mean_ += pbucket->size(); + } + stats.bucket_size_mean_ /= buckets_speed_.size(); + stats.n_buckets_ = buckets_speed_.size(); + } + else { + for (BucketsSpace::const_iterator x = buckets_space_.begin(); x != buckets_space_.end(); ++x) { + stats.bucket_sizes_.push_back((lsh::FeatureIndex)x->second.size()); + stats.bucket_size_mean_ += x->second.size(); + } + stats.bucket_size_mean_ /= buckets_space_.size(); + stats.n_buckets_ = buckets_space_.size(); + } + + std::sort(stats.bucket_sizes_.begin(), stats.bucket_sizes_.end()); + + // BOOST_FOREACH(int size, stats.bucket_sizes_) + // std::cout << size << " "; + // std::cout << std::endl; + stats.bucket_size_median_ = stats.bucket_sizes_[stats.bucket_sizes_.size() / 2]; + stats.bucket_size_min_ = stats.bucket_sizes_.front(); + stats.bucket_size_max_ = stats.bucket_sizes_.back(); + + // TODO compute mean and std + /*float mean, stddev; + stats.bucket_size_mean_ = mean; + stats.bucket_size_std_dev = stddev;*/ + + // Include a histogram of the buckets + unsigned int bin_start = 0; + unsigned int bin_end = 20; + bool is_new_bin = true; + for (std::vector::iterator iterator = stats.bucket_sizes_.begin(), end = stats.bucket_sizes_.end(); iterator + != end; ) + if (*iterator < bin_end) { + if (is_new_bin) { + stats.size_histogram_.push_back(std::vector(3, 0)); + stats.size_histogram_.back()[0] = bin_start; + stats.size_histogram_.back()[1] = bin_end - 1; + is_new_bin = false; + } + ++stats.size_histogram_.back()[2]; + ++iterator; + } + else { + bin_start += 20; + bin_end += 20; + is_new_bin = true; + } + + return stats; +} + +// End the two namespaces +} +} + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +#endif /* OPENCV_FLANN_LSH_TABLE_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/matrix.h b/thirdparty/linux/include/opencv2/flann/matrix.h new file mode 100644 index 0000000..51b6c63 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/matrix.h @@ -0,0 +1,116 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_DATASET_H_ +#define OPENCV_FLANN_DATASET_H_ + +#include + +#include "general.h" + +namespace cvflann +{ + +/** + * Class that implements a simple rectangular matrix stored in a memory buffer and + * provides convenient matrix-like access using the [] operators. + */ +template +class Matrix +{ +public: + typedef T type; + + size_t rows; + size_t cols; + size_t stride; + T* data; + + Matrix() : rows(0), cols(0), stride(0), data(NULL) + { + } + + Matrix(T* data_, size_t rows_, size_t cols_, size_t stride_ = 0) : + rows(rows_), cols(cols_), stride(stride_), data(data_) + { + if (stride==0) stride = cols; + } + + /** + * Convenience function for deallocating the storage data. + */ + FLANN_DEPRECATED void free() + { + fprintf(stderr, "The cvflann::Matrix::free() method is deprecated " + "and it does not do any memory deallocation any more. You are" + "responsible for deallocating the matrix memory (by doing" + "'delete[] matrix.data' for example)"); + } + + /** + * Operator that return a (pointer to a) row of the data. + */ + T* operator[](size_t index) const + { + return data+index*stride; + } +}; + + +class UntypedMatrix +{ +public: + size_t rows; + size_t cols; + void* data; + flann_datatype_t type; + + UntypedMatrix(void* data_, long rows_, long cols_) : + rows(rows_), cols(cols_), data(data_) + { + } + + ~UntypedMatrix() + { + } + + + template + Matrix as() + { + return Matrix((T*)data, rows, cols); + } +}; + + + +} + +#endif //OPENCV_FLANN_DATASET_H_ diff --git a/thirdparty/linux/include/opencv2/flann/miniflann.hpp b/thirdparty/linux/include/opencv2/flann/miniflann.hpp new file mode 100644 index 0000000..5d25f5e --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/miniflann.hpp @@ -0,0 +1,158 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_MINIFLANN_HPP +#define OPENCV_MINIFLANN_HPP + +#include "opencv2/core.hpp" +#include "opencv2/flann/defines.h" + +namespace cv +{ + +namespace flann +{ + +struct CV_EXPORTS IndexParams +{ + IndexParams(); + ~IndexParams(); + + String getString(const String& key, const String& defaultVal=String()) const; + int getInt(const String& key, int defaultVal=-1) const; + double getDouble(const String& key, double defaultVal=-1) const; + + void setString(const String& key, const String& value); + void setInt(const String& key, int value); + void setDouble(const String& key, double value); + void setFloat(const String& key, float value); + void setBool(const String& key, bool value); + void setAlgorithm(int value); + + void getAll(std::vector& names, + std::vector& types, + std::vector& strValues, + std::vector& numValues) const; + + void* params; +}; + +struct CV_EXPORTS KDTreeIndexParams : public IndexParams +{ + KDTreeIndexParams(int trees=4); +}; + +struct CV_EXPORTS LinearIndexParams : public IndexParams +{ + LinearIndexParams(); +}; + +struct CV_EXPORTS CompositeIndexParams : public IndexParams +{ + CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, + cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f ); +}; + +struct CV_EXPORTS AutotunedIndexParams : public IndexParams +{ + AutotunedIndexParams(float target_precision = 0.8f, float build_weight = 0.01f, + float memory_weight = 0, float sample_fraction = 0.1f); +}; + +struct CV_EXPORTS HierarchicalClusteringIndexParams : public IndexParams +{ + HierarchicalClusteringIndexParams(int branching = 32, + cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, int trees = 4, int leaf_size = 100 ); +}; + +struct CV_EXPORTS KMeansIndexParams : public IndexParams +{ + KMeansIndexParams(int branching = 32, int iterations = 11, + cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f ); +}; + +struct CV_EXPORTS LshIndexParams : public IndexParams +{ + LshIndexParams(int table_number, int key_size, int multi_probe_level); +}; + +struct CV_EXPORTS SavedIndexParams : public IndexParams +{ + SavedIndexParams(const String& filename); +}; + +struct CV_EXPORTS SearchParams : public IndexParams +{ + SearchParams( int checks = 32, float eps = 0, bool sorted = true ); +}; + +class CV_EXPORTS_W Index +{ +public: + CV_WRAP Index(); + CV_WRAP Index(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2); + virtual ~Index(); + + CV_WRAP virtual void build(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2); + CV_WRAP virtual void knnSearch(InputArray query, OutputArray indices, + OutputArray dists, int knn, const SearchParams& params=SearchParams()); + + CV_WRAP virtual int radiusSearch(InputArray query, OutputArray indices, + OutputArray dists, double radius, int maxResults, + const SearchParams& params=SearchParams()); + + CV_WRAP virtual void save(const String& filename) const; + CV_WRAP virtual bool load(InputArray features, const String& filename); + CV_WRAP virtual void release(); + CV_WRAP cvflann::flann_distance_t getDistance() const; + CV_WRAP cvflann::flann_algorithm_t getAlgorithm() const; + +protected: + cvflann::flann_distance_t distType; + cvflann::flann_algorithm_t algo; + int featureType; + void* index; +}; + +} } // namespace cv::flann + +#endif diff --git a/thirdparty/linux/include/opencv2/flann/nn_index.h b/thirdparty/linux/include/opencv2/flann/nn_index.h new file mode 100644 index 0000000..381d4bc --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/nn_index.h @@ -0,0 +1,177 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_NNINDEX_H +#define OPENCV_FLANN_NNINDEX_H + +#include "general.h" +#include "matrix.h" +#include "result_set.h" +#include "params.h" + +namespace cvflann +{ + +/** + * Nearest-neighbour index base class + */ +template +class NNIndex +{ + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + +public: + + virtual ~NNIndex() {} + + /** + * \brief Builds the index + */ + virtual void buildIndex() = 0; + + /** + * \brief Perform k-nearest neighbor search + * \param[in] queries The query points for which to find the nearest neighbors + * \param[out] indices The indices of the nearest neighbors found + * \param[out] dists Distances to the nearest neighbors found + * \param[in] knn Number of nearest neighbors to return + * \param[in] params Search parameters + */ + virtual void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) + { + assert(queries.cols == veclen()); + assert(indices.rows >= queries.rows); + assert(dists.rows >= queries.rows); + assert(int(indices.cols) >= knn); + assert(int(dists.cols) >= knn); + +#if 0 + KNNResultSet resultSet(knn); + for (size_t i = 0; i < queries.rows; i++) { + resultSet.init(indices[i], dists[i]); + findNeighbors(resultSet, queries[i], params); + } +#else + KNNUniqueResultSet resultSet(knn); + for (size_t i = 0; i < queries.rows; i++) { + resultSet.clear(); + findNeighbors(resultSet, queries[i], params); + if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn); + else resultSet.copy(indices[i], dists[i], knn); + } +#endif + } + + /** + * \brief Perform radius search + * \param[in] query The query point + * \param[out] indices The indinces of the neighbors found within the given radius + * \param[out] dists The distances to the nearest neighbors found + * \param[in] radius The radius used for search + * \param[in] params Search parameters + * \returns Number of neighbors found + */ + virtual int radiusSearch(const Matrix& query, Matrix& indices, Matrix& dists, float radius, const SearchParams& params) + { + if (query.rows != 1) { + fprintf(stderr, "I can only search one feature at a time for range search\n"); + return -1; + } + assert(query.cols == veclen()); + assert(indices.cols == dists.cols); + + int n = 0; + int* indices_ptr = NULL; + DistanceType* dists_ptr = NULL; + if (indices.cols > 0) { + n = (int)indices.cols; + indices_ptr = indices[0]; + dists_ptr = dists[0]; + } + + RadiusUniqueResultSet resultSet((DistanceType)radius); + resultSet.clear(); + findNeighbors(resultSet, query[0], params); + if (n>0) { + if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices_ptr, dists_ptr, n); + else resultSet.copy(indices_ptr, dists_ptr, n); + } + + return (int)resultSet.size(); + } + + /** + * \brief Saves the index to a stream + * \param stream The stream to save the index to + */ + virtual void saveIndex(FILE* stream) = 0; + + /** + * \brief Loads the index from a stream + * \param stream The stream from which the index is loaded + */ + virtual void loadIndex(FILE* stream) = 0; + + /** + * \returns number of features in this index. + */ + virtual size_t size() const = 0; + + /** + * \returns The dimensionality of the features in this index. + */ + virtual size_t veclen() const = 0; + + /** + * \returns The amount of memory (in bytes) used by the index. + */ + virtual int usedMemory() const = 0; + + /** + * \returns The index type (kdtree, kmeans,...) + */ + virtual flann_algorithm_t getType() const = 0; + + /** + * \returns The index parameters + */ + virtual IndexParams getParameters() const = 0; + + + /** + * \brief Method that searches for nearest-neighbours + */ + virtual void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) = 0; +}; + +} + +#endif //OPENCV_FLANN_NNINDEX_H diff --git a/thirdparty/linux/include/opencv2/flann/object_factory.h b/thirdparty/linux/include/opencv2/flann/object_factory.h new file mode 100644 index 0000000..7f971c5 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/object_factory.h @@ -0,0 +1,91 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_OBJECT_FACTORY_H_ +#define OPENCV_FLANN_OBJECT_FACTORY_H_ + +#include + +namespace cvflann +{ + +class CreatorNotFound +{ +}; + +template +class ObjectFactory +{ + typedef ObjectFactory ThisClass; + typedef std::map ObjectRegistry; + + // singleton class, private constructor + ObjectFactory() {} + +public: + + bool subscribe(UniqueIdType id, ObjectCreator creator) + { + if (object_registry.find(id) != object_registry.end()) return false; + + object_registry[id] = creator; + return true; + } + + bool unregister(UniqueIdType id) + { + return object_registry.erase(id) == 1; + } + + ObjectCreator create(UniqueIdType id) + { + typename ObjectRegistry::const_iterator iter = object_registry.find(id); + + if (iter == object_registry.end()) { + throw CreatorNotFound(); + } + + return iter->second; + } + + static ThisClass& instance() + { + static ThisClass the_factory; + return the_factory; + } +private: + ObjectRegistry object_registry; +}; + +} + +#endif /* OPENCV_FLANN_OBJECT_FACTORY_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/params.h b/thirdparty/linux/include/opencv2/flann/params.h new file mode 100644 index 0000000..95ef4cd --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/params.h @@ -0,0 +1,99 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_PARAMS_H_ +#define OPENCV_FLANN_PARAMS_H_ + +#include "any.h" +#include "general.h" +#include +#include + + +namespace cvflann +{ + +typedef std::map IndexParams; + +struct SearchParams : public IndexParams +{ + SearchParams(int checks = 32, float eps = 0, bool sorted = true ) + { + // how many leafs to visit when searching for neighbours (-1 for unlimited) + (*this)["checks"] = checks; + // search for eps-approximate neighbours (default: 0) + (*this)["eps"] = eps; + // only for radius search, require neighbours sorted by distance (default: true) + (*this)["sorted"] = sorted; + } +}; + + +template +T get_param(const IndexParams& params, cv::String name, const T& default_value) +{ + IndexParams::const_iterator it = params.find(name); + if (it != params.end()) { + return it->second.cast(); + } + else { + return default_value; + } +} + +template +T get_param(const IndexParams& params, cv::String name) +{ + IndexParams::const_iterator it = params.find(name); + if (it != params.end()) { + return it->second.cast(); + } + else { + throw FLANNException(cv::String("Missing parameter '")+name+cv::String("' in the parameters given")); + } +} + +inline void print_params(const IndexParams& params, std::ostream& stream) +{ + IndexParams::const_iterator it; + + for(it=params.begin(); it!=params.end(); ++it) { + stream << it->first << " : " << it->second << std::endl; + } +} + +inline void print_params(const IndexParams& params) +{ + print_params(params, std::cout); +} + +} + + +#endif /* OPENCV_FLANN_PARAMS_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/random.h b/thirdparty/linux/include/opencv2/flann/random.h new file mode 100644 index 0000000..a3cf5ec --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/random.h @@ -0,0 +1,133 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_RANDOM_H +#define OPENCV_FLANN_RANDOM_H + +#include +#include +#include + +#include "general.h" + +namespace cvflann +{ + +/** + * Seeds the random number generator + * @param seed Random seed + */ +inline void seed_random(unsigned int seed) +{ + srand(seed); +} + +/* + * Generates a random double value. + */ +/** + * Generates a random double value. + * @param high Upper limit + * @param low Lower limit + * @return Random double value + */ +inline double rand_double(double high = 1.0, double low = 0) +{ + return low + ((high-low) * (std::rand() / (RAND_MAX + 1.0))); +} + +/** + * Generates a random integer value. + * @param high Upper limit + * @param low Lower limit + * @return Random integer value + */ +inline int rand_int(int high = RAND_MAX, int low = 0) +{ + return low + (int) ( double(high-low) * (std::rand() / (RAND_MAX + 1.0))); +} + +/** + * Random number generator that returns a distinct number from + * the [0,n) interval each time. + */ +class UniqueRandom +{ + std::vector vals_; + int size_; + int counter_; + +public: + /** + * Constructor. + * @param n Size of the interval from which to generate + * @return + */ + UniqueRandom(int n) + { + init(n); + } + + /** + * Initializes the number generator. + * @param n the size of the interval from which to generate random numbers. + */ + void init(int n) + { + // create and initialize an array of size n + vals_.resize(n); + size_ = n; + for (int i = 0; i < size_; ++i) vals_[i] = i; + + // shuffle the elements in the array + std::random_shuffle(vals_.begin(), vals_.end()); + + counter_ = 0; + } + + /** + * Return a distinct random integer in greater or equal to 0 and less + * than 'n' on each call. It should be called maximum 'n' times. + * Returns: a random integer + */ + int next() + { + if (counter_ == size_) { + return -1; + } + else { + return vals_[counter_++]; + } + } +}; + +} + +#endif //OPENCV_FLANN_RANDOM_H diff --git a/thirdparty/linux/include/opencv2/flann/result_set.h b/thirdparty/linux/include/opencv2/flann/result_set.h new file mode 100644 index 0000000..9750019 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/result_set.h @@ -0,0 +1,543 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_RESULTSET_H +#define OPENCV_FLANN_RESULTSET_H + +#include +#include +#include +#include +#include +#include + +namespace cvflann +{ + +/* This record represents a branch point when finding neighbors in + the tree. It contains a record of the minimum distance to the query + point, as well as the node at which the search resumes. + */ + +template +struct BranchStruct +{ + T node; /* Tree node at which search resumes */ + DistanceType mindist; /* Minimum distance to query for all nodes below. */ + + BranchStruct() {} + BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {} + + bool operator<(const BranchStruct& rhs) const + { + return mindist +class ResultSet +{ +public: + virtual ~ResultSet() {} + + virtual bool full() const = 0; + + virtual void addPoint(DistanceType dist, int index) = 0; + + virtual DistanceType worstDist() const = 0; + +}; + +/** + * KNNSimpleResultSet does not ensure that the element it holds are unique. + * Is used in those cases where the nearest neighbour algorithm used does not + * attempt to insert the same element multiple times. + */ +template +class KNNSimpleResultSet : public ResultSet +{ + int* indices; + DistanceType* dists; + int capacity; + int count; + DistanceType worst_distance_; + +public: + KNNSimpleResultSet(int capacity_) : capacity(capacity_), count(0) + { + } + + void init(int* indices_, DistanceType* dists_) + { + indices = indices_; + dists = dists_; + count = 0; + worst_distance_ = (std::numeric_limits::max)(); + dists[capacity-1] = worst_distance_; + } + + size_t size() const + { + return count; + } + + bool full() const + { + return count == capacity; + } + + + void addPoint(DistanceType dist, int index) + { + if (dist >= worst_distance_) return; + int i; + for (i=count; i>0; --i) { +#ifdef FLANN_FIRST_MATCH + if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) ) +#else + if (dists[i-1]>dist) +#endif + { + if (i +class KNNResultSet : public ResultSet +{ + int* indices; + DistanceType* dists; + int capacity; + int count; + DistanceType worst_distance_; + +public: + KNNResultSet(int capacity_) : capacity(capacity_), count(0) + { + } + + void init(int* indices_, DistanceType* dists_) + { + indices = indices_; + dists = dists_; + count = 0; + worst_distance_ = (std::numeric_limits::max)(); + dists[capacity-1] = worst_distance_; + } + + size_t size() const + { + return count; + } + + bool full() const + { + return count == capacity; + } + + + void addPoint(DistanceType dist, int index) + { + if (dist >= worst_distance_) return; + int i; + for (i = count; i > 0; --i) { +#ifdef FLANN_FIRST_MATCH + if ( (dists[i-1]<=dist) && ((dist!=dists[i-1])||(indices[i-1]<=index)) ) +#else + if (dists[i-1]<=dist) +#endif + { + // Check for duplicate indices + int j = i - 1; + while ((j >= 0) && (dists[j] == dist)) { + if (indices[j] == index) { + return; + } + --j; + } + break; + } + } + + if (count < capacity) ++count; + for (int j = count-1; j > i; --j) { + dists[j] = dists[j-1]; + indices[j] = indices[j-1]; + } + dists[i] = dist; + indices[i] = index; + worst_distance_ = dists[capacity-1]; + } + + DistanceType worstDist() const + { + return worst_distance_; + } +}; + + +/** + * A result-set class used when performing a radius based search. + */ +template +class RadiusResultSet : public ResultSet +{ + DistanceType radius; + int* indices; + DistanceType* dists; + size_t capacity; + size_t count; + +public: + RadiusResultSet(DistanceType radius_, int* indices_, DistanceType* dists_, int capacity_) : + radius(radius_), indices(indices_), dists(dists_), capacity(capacity_) + { + init(); + } + + ~RadiusResultSet() + { + } + + void init() + { + count = 0; + } + + size_t size() const + { + return count; + } + + bool full() const + { + return true; + } + + void addPoint(DistanceType dist, int index) + { + if (dist0)&&(count < capacity)) { + dists[count] = dist; + indices[count] = index; + } + count++; + } + } + + DistanceType worstDist() const + { + return radius; + } + +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Class that holds the k NN neighbors + * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays + */ +template +class UniqueResultSet : public ResultSet +{ +public: + struct DistIndex + { + DistIndex(DistanceType dist, unsigned int index) : + dist_(dist), index_(index) + { + } + bool operator<(const DistIndex dist_index) const + { + return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_); + } + DistanceType dist_; + unsigned int index_; + }; + + /** Default cosntructor */ + UniqueResultSet() : + worst_distance_(std::numeric_limits::max()) + { + } + + /** Check the status of the set + * @return true if we have k NN + */ + inline bool full() const + { + return is_full_; + } + + /** Remove all elements in the set + */ + virtual void clear() = 0; + + /** Copy the set to two C arrays + * @param indices pointer to a C array of indices + * @param dist pointer to a C array of distances + * @param n_neighbors the number of neighbors to copy + */ + virtual void copy(int* indices, DistanceType* dist, int n_neighbors = -1) const + { + if (n_neighbors < 0) { + for (typename std::set::const_iterator dist_index = dist_indices_.begin(), dist_index_end = + dist_indices_.end(); dist_index != dist_index_end; ++dist_index, ++indices, ++dist) { + *indices = dist_index->index_; + *dist = dist_index->dist_; + } + } + else { + int i = 0; + for (typename std::set::const_iterator dist_index = dist_indices_.begin(), dist_index_end = + dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) { + *indices = dist_index->index_; + *dist = dist_index->dist_; + } + } + } + + /** Copy the set to two C arrays but sort it according to the distance first + * @param indices pointer to a C array of indices + * @param dist pointer to a C array of distances + * @param n_neighbors the number of neighbors to copy + */ + virtual void sortAndCopy(int* indices, DistanceType* dist, int n_neighbors = -1) const + { + copy(indices, dist, n_neighbors); + } + + /** The number of neighbors in the set + * @return + */ + size_t size() const + { + return dist_indices_.size(); + } + + /** The distance of the furthest neighbor + * If we don't have enough neighbors, it returns the max possible value + * @return + */ + inline DistanceType worstDist() const + { + return worst_distance_; + } +protected: + /** Flag to say if the set is full */ + bool is_full_; + + /** The worst distance found so far */ + DistanceType worst_distance_; + + /** The best candidates so far */ + std::set dist_indices_; +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Class that holds the k NN neighbors + * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays + */ +template +class KNNUniqueResultSet : public UniqueResultSet +{ +public: + /** Constructor + * @param capacity the number of neighbors to store at max + */ + KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity) + { + this->is_full_ = false; + this->clear(); + } + + /** Add a possible candidate to the best neighbors + * @param dist distance for that neighbor + * @param index index of that neighbor + */ + inline void addPoint(DistanceType dist, int index) + { + // Don't do anything if we are worse than the worst + if (dist >= worst_distance_) return; + dist_indices_.insert(DistIndex(dist, index)); + + if (is_full_) { + if (dist_indices_.size() > capacity_) { + dist_indices_.erase(*dist_indices_.rbegin()); + worst_distance_ = dist_indices_.rbegin()->dist_; + } + } + else if (dist_indices_.size() == capacity_) { + is_full_ = true; + worst_distance_ = dist_indices_.rbegin()->dist_; + } + } + + /** Remove all elements in the set + */ + void clear() + { + dist_indices_.clear(); + worst_distance_ = std::numeric_limits::max(); + is_full_ = false; + } + +protected: + typedef typename UniqueResultSet::DistIndex DistIndex; + using UniqueResultSet::is_full_; + using UniqueResultSet::worst_distance_; + using UniqueResultSet::dist_indices_; + + /** The number of neighbors to keep */ + unsigned int capacity_; +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Class that holds the radius nearest neighbors + * It is more accurate than RadiusResult as it is not limited in the number of neighbors + */ +template +class RadiusUniqueResultSet : public UniqueResultSet +{ +public: + /** Constructor + * @param radius the maximum distance of a neighbor + */ + RadiusUniqueResultSet(DistanceType radius) : + radius_(radius) + { + is_full_ = true; + } + + /** Add a possible candidate to the best neighbors + * @param dist distance for that neighbor + * @param index index of that neighbor + */ + void addPoint(DistanceType dist, int index) + { + if (dist <= radius_) dist_indices_.insert(DistIndex(dist, index)); + } + + /** Remove all elements in the set + */ + inline void clear() + { + dist_indices_.clear(); + } + + + /** Check the status of the set + * @return alwys false + */ + inline bool full() const + { + return true; + } + + /** The distance of the furthest neighbor + * If we don't have enough neighbors, it returns the max possible value + * @return + */ + inline DistanceType worstDist() const + { + return radius_; + } +private: + typedef typename UniqueResultSet::DistIndex DistIndex; + using UniqueResultSet::dist_indices_; + using UniqueResultSet::is_full_; + + /** The furthest distance a neighbor can be */ + DistanceType radius_; +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Class that holds the k NN neighbors within a radius distance + */ +template +class KNNRadiusUniqueResultSet : public KNNUniqueResultSet +{ +public: + /** Constructor + * @param capacity the number of neighbors to store at max + * @param radius the maximum distance of a neighbor + */ + KNNRadiusUniqueResultSet(unsigned int capacity, DistanceType radius) + { + this->capacity_ = capacity; + this->radius_ = radius; + this->dist_indices_.reserve(capacity_); + this->clear(); + } + + /** Remove all elements in the set + */ + void clear() + { + dist_indices_.clear(); + worst_distance_ = radius_; + is_full_ = false; + } +private: + using KNNUniqueResultSet::dist_indices_; + using KNNUniqueResultSet::is_full_; + using KNNUniqueResultSet::worst_distance_; + + /** The maximum number of neighbors to consider */ + unsigned int capacity_; + + /** The maximum distance of a neighbor */ + DistanceType radius_; +}; +} + +#endif //OPENCV_FLANN_RESULTSET_H diff --git a/thirdparty/linux/include/opencv2/flann/sampling.h b/thirdparty/linux/include/opencv2/flann/sampling.h new file mode 100644 index 0000000..396f177 --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/sampling.h @@ -0,0 +1,81 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_SAMPLING_H_ +#define OPENCV_FLANN_SAMPLING_H_ + +#include "matrix.h" +#include "random.h" + +namespace cvflann +{ + +template +Matrix random_sample(Matrix& srcMatrix, long size, bool remove = false) +{ + Matrix newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols); + + T* src,* dest; + for (long i=0; i +Matrix random_sample(const Matrix& srcMatrix, size_t size) +{ + UniqueRandom rand((int)srcMatrix.rows); + Matrix newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols); + + T* src,* dest; + for (size_t i=0; i +#include + +#include "general.h" +#include "nn_index.h" + +#ifdef FLANN_SIGNATURE_ +#undef FLANN_SIGNATURE_ +#endif +#define FLANN_SIGNATURE_ "FLANN_INDEX" + +namespace cvflann +{ + +template +struct Datatype {}; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_INT8; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_INT16; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_INT32; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_UINT8; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_UINT16; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_UINT32; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_FLOAT32; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_FLOAT64; } }; + + +/** + * Structure representing the index header. + */ +struct IndexHeader +{ + char signature[16]; + char version[16]; + flann_datatype_t data_type; + flann_algorithm_t index_type; + size_t rows; + size_t cols; +}; + +/** + * Saves index header to stream + * + * @param stream - Stream to save to + * @param index - The index to save + */ +template +void save_header(FILE* stream, const NNIndex& index) +{ + IndexHeader header; + memset(header.signature, 0, sizeof(header.signature)); + strcpy(header.signature, FLANN_SIGNATURE_); + memset(header.version, 0, sizeof(header.version)); + strcpy(header.version, FLANN_VERSION_); + header.data_type = Datatype::type(); + header.index_type = index.getType(); + header.rows = index.size(); + header.cols = index.veclen(); + + std::fwrite(&header, sizeof(header),1,stream); +} + + +/** + * + * @param stream - Stream to load from + * @return Index header + */ +inline IndexHeader load_header(FILE* stream) +{ + IndexHeader header; + size_t read_size = fread(&header,sizeof(header),1,stream); + + if (read_size!=(size_t)1) { + throw FLANNException("Invalid index file, cannot read"); + } + + if (strcmp(header.signature,FLANN_SIGNATURE_)!=0) { + throw FLANNException("Invalid index file, wrong signature"); + } + + return header; + +} + + +template +void save_value(FILE* stream, const T& value, size_t count = 1) +{ + fwrite(&value, sizeof(value),count, stream); +} + +template +void save_value(FILE* stream, const cvflann::Matrix& value) +{ + fwrite(&value, sizeof(value),1, stream); + fwrite(value.data, sizeof(T),value.rows*value.cols, stream); +} + +template +void save_value(FILE* stream, const std::vector& value) +{ + size_t size = value.size(); + fwrite(&size, sizeof(size_t), 1, stream); + fwrite(&value[0], sizeof(T), size, stream); +} + +template +void load_value(FILE* stream, T& value, size_t count = 1) +{ + size_t read_cnt = fread(&value, sizeof(value), count, stream); + if (read_cnt != count) { + throw FLANNException("Cannot read from file"); + } +} + +template +void load_value(FILE* stream, cvflann::Matrix& value) +{ + size_t read_cnt = fread(&value, sizeof(value), 1, stream); + if (read_cnt != 1) { + throw FLANNException("Cannot read from file"); + } + value.data = new T[value.rows*value.cols]; + read_cnt = fread(value.data, sizeof(T), value.rows*value.cols, stream); + if (read_cnt != (size_t)(value.rows*value.cols)) { + throw FLANNException("Cannot read from file"); + } +} + + +template +void load_value(FILE* stream, std::vector& value) +{ + size_t size; + size_t read_cnt = fread(&size, sizeof(size_t), 1, stream); + if (read_cnt!=1) { + throw FLANNException("Cannot read from file"); + } + value.resize(size); + read_cnt = fread(&value[0], sizeof(T), size, stream); + if (read_cnt != size) { + throw FLANNException("Cannot read from file"); + } +} + +} + +#endif /* OPENCV_FLANN_SAVING_H_ */ diff --git a/thirdparty/linux/include/opencv2/flann/simplex_downhill.h b/thirdparty/linux/include/opencv2/flann/simplex_downhill.h new file mode 100644 index 0000000..145901a --- /dev/null +++ b/thirdparty/linux/include/opencv2/flann/simplex_downhill.h @@ -0,0 +1,186 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_SIMPLEX_DOWNHILL_H_ +#define OPENCV_FLANN_SIMPLEX_DOWNHILL_H_ + +namespace cvflann +{ + +/** + Adds val to array vals (and point to array points) and keeping the arrays sorted by vals. + */ +template +void addValue(int pos, float val, float* vals, T* point, T* points, int n) +{ + vals[pos] = val; + for (int i=0; i0 && vals[j] +float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL ) +{ + const int MAX_ITERATIONS = 10; + + assert(n>0); + + T* p_o = new T[n]; + T* p_r = new T[n]; + T* p_e = new T[n]; + + int alpha = 1; + + int iterations = 0; + + bool ownVals = false; + if (vals == NULL) { + ownVals = true; + vals = new float[n+1]; + for (int i=0; i MAX_ITERATIONS) break; + + // compute average of simplex points (except the highest point) + for (int j=0; j=vals[0])&&(val_r=vals[n]) { + for (int i=0; i +#include "opencv2/core.hpp" +#include "opencv2/core/utility.hpp" + +namespace cvflann +{ + +/** + * A start-stop timer class. + * + * Can be used to time portions of code. + */ +class StartStopTimer +{ + int64 startTime; + +public: + /** + * Value of the timer. + */ + double value; + + + /** + * Constructor. + */ + StartStopTimer() + { + reset(); + } + + /** + * Starts the timer. + */ + void start() + { + startTime = cv::getTickCount(); + } + + /** + * Stops the timer and updates timer value. + */ + void stop() + { + int64 stopTime = cv::getTickCount(); + value += ( (double)stopTime - startTime) / cv::getTickFrequency(); + } + + /** + * Resets the timer value to 0. + */ + void reset() + { + value = 0; + } + +}; + +} + +#endif // FLANN_TIMER_H diff --git a/thirdparty/linux/include/opencv2/freetype.hpp b/thirdparty/linux/include/opencv2/freetype.hpp new file mode 100644 index 0000000..40cdda6 --- /dev/null +++ b/thirdparty/linux/include/opencv2/freetype.hpp @@ -0,0 +1,190 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ +//################################################################################ +// +// Created by Kumataro +// +//################################################################################ + +#ifndef _OPENCV_FREETYPE_H_ +#define _OPENCV_FREETYPE_H_ +#ifdef __cplusplus + +#include + +/** +@defgroup freetype Drawing UTF-8 strings with freetype/harfbuzz + +This modules is to draw UTF-8 strings with freetype/harfbuzz. + +1. Install freetype2 and harfbuzz in your system. +2. Create FreeType2 instance with createFreeType2() function. +3. Load font file with loadFontData() function. +4. Draw text with putText() function. + +- If thickness parameter is negative, drawing glyph is filled. +- If thickness parameter is positive, drawing glyph is outlined with thickness. +- If line_type parameter is 16(or CV_AA), drawing glyph is smooth. + +*/ + +namespace cv { +namespace freetype { +//! @addtogroup freetype +//! @{ +class CV_EXPORTS_W FreeType2 : public Algorithm +{ +public: +/** @brief Load font data. + +The function loadFontData loads font data. + +@param fontFileName FontFile Name +@param id face_index to select a font faces in a single file. +*/ + + CV_WRAP virtual void loadFontData(String fontFileName, int id) = 0; + +/** @brief Set Split Number from Bezier-curve to line + +The function setSplitNumber set the number of split points from bezier-curve to line. +If you want to draw large glyph, large is better. +If you want to draw small glyph, small is better. + +@param num number of split points from bezier-curve to line +*/ + + CV_WRAP virtual void setSplitNumber( int num ) = 0; + +/** @brief Draws a text string. + +The function putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by "Tofu" or non-drawn. + +@param img Image. +@param text Text string to be drawn. +@param org Bottom-left/Top-left corner of the text string in the image. +@param fontHeight Drawing font size by pixel unit. +@param color Text color. +@param thickness Thickness of the lines used to draw a text when negative, the glyph is filled. Otherwise, the glyph is drawn with this thickness. +@param line_type Line type. See the line for details. +@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner. +*/ + + CV_WRAP virtual void putText( + InputOutputArray img, const String& text, Point org, + int fontHeight, Scalar color, + int thickness, int line_type, bool bottomLeftOrigin + ) = 0; + +/** @brief Calculates the width and height of a text string. + +The function getTextSize calculates and returns the approximate size of a box that contains the specified text. +That is, the following code renders some text, the tight box surrounding it, and the baseline: : +@code + String text = "Funny text inside the box"; + int fontHeight = 60; + int thickness = -1; + int linestyle = 8; + + Mat img(600, 800, CV_8UC3, Scalar::all(0)); + + int baseline=0; + + cv::Ptr ft2; + ft2 = cv::freetype::createFreeType2(); + ft2->loadFontData( "./mplus-1p-regular.ttf", 0 ); + + Size textSize = ft2->getTextSize(text, + fontHeight, + thickness, + &baseline); + + if(thickness > 0){ + baseline += thickness; + } + + // center the text + Point textOrg((img.cols - textSize.width) / 2, + (img.rows + textSize.height) / 2); + + // draw the box + rectangle(img, textOrg + Point(0, baseline), + textOrg + Point(textSize.width, -textSize.height), + Scalar(0,255,0),1,8); + + // ... and the baseline first + line(img, textOrg + Point(0, thickness), + textOrg + Point(textSize.width, thickness), + Scalar(0, 0, 255),1,8); + + // then put the text itself + ft2->putText(img, text, textOrg, fontHeight, + Scalar::all(255), thickness, linestyle, true ); +@endcode + +@param text Input text string. +@param fontHeight Drawing font size by pixel unit. +@param thickness Thickness of lines used to render the text. See putText for details. +@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text +point. +@return The size of a box that contains the specified text. + +@see cv::putText + */ +CV_WRAP virtual Size getTextSize(const String& text, + int fontHeight, int thickness, + CV_OUT int* baseLine) = 0; + +}; + +/** @brief Create FreeType2 Instance + +The function createFreeType2 create instance to draw UTF-8 strings. + +*/ + CV_EXPORTS_W Ptr createFreeType2(); + +//! @] +} } // namespace freetype + +#endif +#endif diff --git a/thirdparty/linux/include/opencv2/fuzzy.hpp b/thirdparty/linux/include/opencv2/fuzzy.hpp new file mode 100644 index 0000000..8a532c0 --- /dev/null +++ b/thirdparty/linux/include/opencv2/fuzzy.hpp @@ -0,0 +1,66 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, +// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_FUZZY_H__ +#define __OPENCV_FUZZY_H__ + +#include "opencv2/fuzzy/types.hpp" +#include "opencv2/fuzzy/fuzzy_F0_math.hpp" +#include "opencv2/fuzzy/fuzzy_image.hpp" + +/** +@defgroup fuzzy Image processing based on fuzzy mathematics + +Namespace for all functions is **ft**. The module brings implementation of the last image processing algorithms based on fuzzy mathematics. + + @{ + @defgroup f0_math Math with F0-transfrom support + +Fuzzy transform (F-transform) of the 0th degree transform whole image to a vector of its components. These components are used in latter computation. + + @defgroup f_image Fuzzy image processing + +Image proceesing based on F-transform is fast to process and easy to understand. + @} + +*/ + +#endif // __OPENCV_FUZZY_H__ diff --git a/thirdparty/linux/include/opencv2/fuzzy/fuzzy_F0_math.hpp b/thirdparty/linux/include/opencv2/fuzzy/fuzzy_F0_math.hpp new file mode 100644 index 0000000..5b24157 --- /dev/null +++ b/thirdparty/linux/include/opencv2/fuzzy/fuzzy_F0_math.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, +// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_FUZZY_F0_MATH_H__ +#define __OPENCV_FUZZY_F0_MATH_H__ + +#include "opencv2/fuzzy/types.hpp" +#include "opencv2/core.hpp" + +namespace cv +{ + +namespace ft +{ + //! @addtogroup f0_math + //! @{ + + /** @brief Computes components of the array using direct F0-transform. + @param matrix Input array. + @param kernel Kernel used for processing. Function **createKernel** can be used. + @param components Output 32-bit array for the components. + @param mask Mask can be used for unwanted area marking. + + The function computes components using predefined kernel and mask. + + @note + F-transform technique is described in paper @cite Perf:FT. + */ + CV_EXPORTS_AS(FT02D_components1) void FT02D_components(InputArray matrix, InputArray kernel, OutputArray components, InputArray mask); + + /** @brief Computes components of the array using direct F0-transform. + @param matrix Input array. + @param kernel Kernel used for processing. Function **createKernel** can be used. + @param components Output 32-bit array for the components. + + The function computes components using predefined kernel. + + @note + F-transform technique is described in paper @cite Perf:FT. + */ + CV_EXPORTS_W void FT02D_components(InputArray matrix, InputArray kernel, OutputArray components); + + /** @brief Computes inverse F0-transfrom. + @param components Input 32-bit single channel array for the components. + @param kernel Kernel used for processing. Function **createKernel** can be used. + @param output Output 32-bit array. + @param width Width of the output array. + @param height Height of the output array. + + @note + F-transform technique is described in paper @cite Perf:FT. + */ + CV_EXPORTS_W void FT02D_inverseFT(InputArray components, InputArray kernel, OutputArray output, int width, int height); + + /** @brief Computes F0-transfrom and inverse F0-transfrom at once. + @param matrix Input matrix. + @param kernel Kernel used for processing. Function **createKernel** can be used. + @param output Output 32-bit array. + @param mask Mask used for unwanted area marking. + + This function computes F-transfrom and inverse F-transfotm in one step. It is fully sufficient and optimized for **Mat**. + */ + CV_EXPORTS_AS(FT02D_process1) void FT02D_process(InputArray matrix, InputArray kernel, OutputArray output, InputArray mask); + + /** @brief Computes F0-transfrom and inverse F0-transfrom at once. + @param matrix Input matrix. + @param kernel Kernel used for processing. Function **createKernel** can be used. + @param output Output 32-bit array. + + This function computes F-transfrom and inverse F-transfotm in one step. It is fully sufficient and optimized for **Mat**. + */ + CV_EXPORTS_W void FT02D_process(InputArray matrix, InputArray kernel, OutputArray output); + + /** @brief Computes F0-transfrom and inverse F0-transfrom at once and return state. + @param matrix Input matrix. + @param kernel Kernel used for processing. Function **createKernel** can be used. + @param output Output 32-bit array. + @param mask Mask used for unwanted area marking. + @param maskOutput Mask after one iteration. + @param firstStop If **true** function returns -1 when first problem appears. In case of **false**, the process is completed and summation of all problems returned. + + This function computes iteration of F-transfrom and inverse F-transfotm and handle image and mask change. The function is used in *inpaint* function. + */ + CV_EXPORTS_W int FT02D_iteration(InputArray matrix, InputArray kernel, OutputArray output, InputArray mask, OutputArray maskOutput, bool firstStop); + + //! @} +} +} + +#endif // __OPENCV_FUZZY_F0_MATH_H__ diff --git a/thirdparty/linux/include/opencv2/fuzzy/fuzzy_image.hpp b/thirdparty/linux/include/opencv2/fuzzy/fuzzy_image.hpp new file mode 100644 index 0000000..e5287a9 --- /dev/null +++ b/thirdparty/linux/include/opencv2/fuzzy/fuzzy_image.hpp @@ -0,0 +1,109 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, +// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_FUZZY_IMAGE_H__ +#define __OPENCV_FUZZY_IMAGE_H__ + +#include "types.hpp" +#include "opencv2/core.hpp" + +namespace cv +{ + +namespace ft +{ + //! @addtogroup f_image + //! @{ + + /** @brief Creates kernel from basic functions. + @param A Basic function used in axis **x**. + @param B Basic function used in axis **y**. + @param kernel Final 32-b kernel derived from **A** and **B**. + @param chn Number of kernel channels. + + The function creates kernel usable for latter fuzzy image processing. + */ + CV_EXPORTS_AS(createKernel1) void createKernel(InputArray A, InputArray B, OutputArray kernel, const int chn); + + /** @brief Creates kernel from general functions. + @param function Function type could be one of the following: + - **LINEAR** Linear basic function. + @param radius Radius of the basic function. + @param kernel Final 32-b kernel. + @param chn Number of kernel channels. + + The function creates kernel from predefined functions. + */ + CV_EXPORTS_W void createKernel(int function, int radius, OutputArray kernel, const int chn); + + /** @brief Image inpainting + @param image Input image. + @param mask Mask used for unwanted area marking. + @param output Output 32-bit image. + @param radius Radius of the basic function. + @param function Function type could be one of the following: + - **LINEAR** Linear basic function. + @param algorithm Algorithm could be one of the following: + - **ONE_STEP** One step algorithm. + - **MULTI_STEP** Algorithm automaticaly increasing radius of the basic function. + - **ITERATIVE** Iterative algorithm running in more steps using partial computations. + + This function provides inpainting technique based on the fuzzy mathematic. + + @note + The algorithms are described in paper @cite Perf:rec. + */ + CV_EXPORTS_W void inpaint(InputArray image, InputArray mask, OutputArray output, int radius, int function, int algorithm); + + /** @brief Image filtering + @param image Input image. + @param kernel Final 32-bit kernel. + @param output Output 32-bit image. + + Filtering of the input image by means of F-transform. + */ + CV_EXPORTS_W void filter(InputArray image, InputArray kernel, OutputArray output); + + //! @} +} +} + +#endif // __OPENCV_FUZZY_IMAGE_H__ diff --git a/thirdparty/linux/include/opencv2/fuzzy/types.hpp b/thirdparty/linux/include/opencv2/fuzzy/types.hpp new file mode 100644 index 0000000..ec831e6 --- /dev/null +++ b/thirdparty/linux/include/opencv2/fuzzy/types.hpp @@ -0,0 +1,70 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, +// Pavel Vlasanek, all rights reserved. Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_FUZZY_TYPES_H__ +#define __OPENCV_FUZZY_TYPES_H__ + +namespace cv +{ + +namespace ft +{ + //! @addtogroup fuzzy + //! @{ + + enum + { + LINEAR = 1, + SINUS = 2 + }; + + enum + { + ONE_STEP = 1, + MULTI_STEP = 2, + ITERATIVE = 3 + }; + + //! @} +} +} + +#endif // __OPENCV_FUZZY_TYPES_H__ diff --git a/thirdparty/linux/include/opencv2/hdf.hpp b/thirdparty/linux/include/opencv2/hdf.hpp new file mode 100644 index 0000000..4ca6d11 --- /dev/null +++ b/thirdparty/linux/include/opencv2/hdf.hpp @@ -0,0 +1,54 @@ +/********************************************************************* + * Software License Agreement (BSD License) + * + * Copyright (c) 2015 + * Balint Cristian + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the copyright holders nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + *********************************************************************/ + +#ifndef __OPENCV_HDF_HPP__ +#define __OPENCV_HDF_HPP__ + +#include "opencv2/hdf/hdf5.hpp" + +/** @defgroup hdf Hierarchical Data Format I/O routines + +This module provides storage routines for Hierarchical Data Format objects. + + @{ + @defgroup hdf5 Hierarchical Data Format version 5 + +Hierarchical Data Format version 5 +-------------------------------------------------------- + + + @} +*/ + +#endif diff --git a/thirdparty/linux/include/opencv2/hdf/hdf5.hpp b/thirdparty/linux/include/opencv2/hdf/hdf5.hpp new file mode 100644 index 0000000..2c6deaa --- /dev/null +++ b/thirdparty/linux/include/opencv2/hdf/hdf5.hpp @@ -0,0 +1,707 @@ +/********************************************************************* + * Software License Agreement (BSD License) + * + * Copyright (c) 2015 + * Balint Cristian + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the copyright holders nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + *********************************************************************/ + +#ifndef __OPENCV_HDF5_HPP__ +#define __OPENCV_HDF5_HPP__ + +#include + + +namespace cv +{ +namespace hdf +{ +using namespace std; + +//! @addtogroup hdf5 +//! @{ + + +/** @brief Hierarchical Data Format version 5 interface. + +Notice that module is compiled only when hdf5 is correctly installed. + + */ +class CV_EXPORTS_W HDF5 +{ +public: + + CV_WRAP enum + { + H5_UNLIMITED = -1, H5_NONE = -1, H5_GETDIMS = 100, H5_GETMAXDIMS = 101, H5_GETCHUNKDIMS = 102, + }; + + virtual ~HDF5() {} + + /** @brief Close and release hdf5 object. + */ + CV_WRAP virtual void close( ) = 0; + + /** @brief Create a group. + @param grlabel specify the hdf5 group label. + + Create a hdf5 group. + + @note Groups are useful for better organise multiple datasets. It is possible to create subgroups within any group. + Existence of a particular group can be checked using hlexists(). In case of subgroups label would be e.g: 'Group1/SubGroup1' + where SubGroup1 is within the root group Group1. + + - In this example Group1 will have one subgrup labeled SubGroup1: + @code{.cpp} + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // create Group1 if does not exists + if ( ! h5io->hlexists( "Group1" ) ) + h5io->grcreate( "Group1" ); + else + printf("Group1 already created, skipping\n" ); + // create SubGroup1 if does not exists + if ( ! h5io->hlexists( "Group1/SubGroup1" ) ) + h5io->grcreate( "Group1/SubGroup1" ); + else + printf("SubGroup1 already created, skipping\n" ); + // release + h5io->close(); + @endcode + + @note When a dataset is created with dscreate() or kpcreate() it can be created right within a group by specifying + full path within the label, in our example would be: 'Group1/SubGroup1/MyDataSet'. It is not thread safe. + */ + CV_WRAP virtual void grcreate( String grlabel ) = 0; + + /** @brief Check if label exists or not. + @param label specify the hdf5 dataset label. + + Returns **true** if dataset exists, and **false** if does not. + + @note Checks if dataset, group or other object type (hdf5 link) exists under the label name. It is thread safe. + */ + CV_WRAP virtual bool hlexists( String label ) const = 0; + + /* @overload */ + CV_WRAP virtual void dscreate( const int rows, const int cols, const int type, + String dslabel ) const = 0; + /* @overload */ + CV_WRAP virtual void dscreate( const int rows, const int cols, const int type, + String dslabel, const int compresslevel ) const = 0; + /* @overload */ + CV_WRAP virtual void dscreate( const int rows, const int cols, const int type, + String dslabel, const int compresslevel, const vector& dims_chunks ) const = 0; + /** @brief Create and allocate storage for two dimensional single or multi channel dataset. + @param rows declare amount of rows + @param cols declare amount of cols + @param type type to be used + @param dslabel specify the hdf5 dataset label, any existing dataset with the same label will be overwritten. + @param compresslevel specify the compression level 0-9 to be used, H5_NONE is default and means no compression. + @param dims_chunks each array member specify chunking sizes to be used for block i/o, + by default NULL means none at all. + + @note If the dataset already exists an exception will be thrown. + + - Existence of the dataset can be checked using hlexists(), see in this example: + @code{.cpp} + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // create space for 100x50 CV_64FC2 matrix + if ( ! h5io->hlexists( "hilbert" ) ) + h5io->dscreate( 100, 50, CV_64FC2, "hilbert" ); + else + printf("DS already created, skipping\n" ); + // release + h5io->close(); + @endcode + + @note Activating compression requires internal chunking. Chunking can significantly improve access + speed booth at read or write time especially for windowed access logic that shifts offset inside dataset. + If no custom chunking is specified default one will be invoked by the size of **whole** dataset + as single big chunk of data. + + - See example of level 9 compression using internal default chunking: + @code{.cpp} + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // create level 9 compressed space for CV_64FC2 matrix + if ( ! h5io->hlexists( "hilbert", 9 ) ) + h5io->dscreate( 100, 50, CV_64FC2, "hilbert", 9 ); + else + printf("DS already created, skipping\n" ); + // release + h5io->close(); + @endcode + + @note A value of H5_UNLIMITED for **rows** or **cols** or booth means **unlimited** data on the specified dimension, + thus is possible to expand anytime such dataset on row, col or booth directions. Presence of H5_UNLIMITED on any + dimension **require** to define custom chunking. No default chunking will be defined in unlimited scenario since + default size on that dimension will be zero, and will grow once dataset is written. Writing into dataset that have + H5_UNLIMITED on some of its dimension requires dsinsert() that allow growth on unlimited dimension instead of dswrite() + that allows to write only in predefined data space. + + - Example below shows no compression but unlimited dimension on cols using 100x100 internal chunking: + @code{.cpp} + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // create level 9 compressed space for CV_64FC2 matrix + int chunks[2] = { 100, 100 }; + h5io->dscreate( 100, cv::hdf::HDF5::H5_UNLIMITED, CV_64FC2, "hilbert", cv::hdf::HDF5::H5_NONE, chunks ); + // release + h5io->close(); + @endcode + + @note It is **not** thread safe, it must be called only once at dataset creation otherwise exception will occur. + Multiple datasets inside single hdf5 file is allowed. + */ + CV_WRAP virtual void dscreate( const int rows, const int cols, const int type, + String dslabel, const int compresslevel, const int* dims_chunks ) const = 0; + + /* @overload */ + CV_WRAP virtual void dscreate( const int n_dims, const int* sizes, const int type, + String dslabel ) const = 0; + /* @overload */ + CV_WRAP virtual void dscreate( const int n_dims, const int* sizes, const int type, + String dslabel, const int compresslevel ) const = 0; + /* @overload */ + CV_WRAP virtual void dscreate( const vector& sizes, const int type, + String dslabel, const int compresslevel = HDF5::H5_NONE, + const vector& dims_chunks = vector() ) const = 0; + /** @brief Create and allocate storage for n-dimensional dataset, single or mutichannel type. + @param n_dims declare number of dimensions + @param sizes array containing sizes for each dimensions + @param type type to be used + @param dslabel specify the hdf5 dataset label, any existing dataset with the same label will be overwritten. + @param compresslevel specify the compression level 0-9 to be used, H5_NONE is default and means no compression. + @param dims_chunks each array member specify chunking sizes to be used for block i/o, + by default NULL means none at all. + @note If the dataset already exists an exception will be thrown. Existence of the dataset can be checked + using hlexists(). + + - See example below that creates a 6 dimensional storage space: + @code{.cpp} + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // create space for 6 dimensional CV_64FC2 matrix + if ( ! h5io->hlexists( "nddata" ) ) + int n_dims = 5; + int dsdims[n_dims] = { 100, 100, 20, 10, 5, 5 }; + h5io->dscreate( n_dims, sizes, CV_64FC2, "nddata" ); + else + printf("DS already created, skipping\n" ); + // release + h5io->close(); + @endcode + + @note Activating compression requires internal chunking. Chunking can significantly improve access + speed booth at read or write time especially for windowed access logic that shifts offset inside dataset. + If no custom chunking is specified default one will be invoked by the size of **whole** dataset + as single big chunk of data. + + - See example of level 0 compression (shallow) using chunking against first + dimension, thus storage will consists by 100 chunks of data: + @code{.cpp} + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // create space for 6 dimensional CV_64FC2 matrix + if ( ! h5io->hlexists( "nddata" ) ) + int n_dims = 5; + int dsdims[n_dims] = { 100, 100, 20, 10, 5, 5 }; + int chunks[n_dims] = { 1, 100, 20, 10, 5, 5 }; + h5io->dscreate( n_dims, dsdims, CV_64FC2, "nddata", 0, chunks ); + else + printf("DS already created, skipping\n" ); + // release + h5io->close(); + @endcode + + @note A value of H5_UNLIMITED inside the **sizes** array means **unlimited** data on that dimension, thus is + possible to expand anytime such dataset on those unlimited directions. Presence of H5_UNLIMITED on any dimension + **require** to define custom chunking. No default chunking will be defined in unlimited scenario since default size + on that dimension will be zero, and will grow once dataset is written. Writing into dataset that have H5_UNLIMITED on + some of its dimension requires dsinsert() instead of dswrite() that allow growth on unlimited dimension instead of + dswrite() that allows to write only in predefined data space. + + - Example below shows a 3 dimensional dataset using no compression with all unlimited sizes and one unit chunking: + @code{.cpp} + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + int n_dims = 3; + int chunks[n_dims] = { 1, 1, 1 }; + int dsdims[n_dims] = { cv::hdf::HDF5::H5_UNLIMITED, cv::hdf::HDF5::H5_UNLIMITED, cv::hdf::HDF5::H5_UNLIMITED }; + h5io->dscreate( n_dims, dsdims, CV_64FC2, "nddata", cv::hdf::HDF5::H5_NONE, chunks ); + // release + h5io->close(); + @endcode + */ + CV_WRAP virtual void dscreate( const int n_dims, const int* sizes, const int type, + String dslabel, const int compresslevel, const int* dims_chunks ) const = 0; + + /** @brief Fetch dataset sizes + @param dslabel specify the hdf5 dataset label to be measured. + @param dims_flag will fetch dataset dimensions on H5_GETDIMS, and dataset maximum dimensions on H5_GETMAXDIMS. + + Returns vector object containing sizes of dataset on each dimensions. + + @note Resulting vector size will match the amount of dataset dimensions. By default H5_GETDIMS will return + actual dataset dimensions. Using H5_GETMAXDIM flag will get maximum allowed dimension which normally match + actual dataset dimension but can hold H5_UNLIMITED value if dataset was prepared in **unlimited** mode on + some of its dimension. It can be useful to check existing dataset dimensions before overwrite it as whole or subset. + Trying to write with oversized source data into dataset target will thrown exception. The H5_GETCHUNKDIMS will + return the dimension of chunk if dataset was created with chunking options otherwise returned vector size + will be zero. + */ + CV_WRAP virtual vector dsgetsize( String dslabel, int dims_flag = HDF5::H5_GETDIMS ) const = 0; + + /** @brief Fetch dataset type + @param dslabel specify the hdf5 dataset label to be checked. + + Returns the stored matrix type. This is an identifier compatible with the CvMat type system, + like e.g. CV_16SC5 (16-bit signed 5-channel array), and so on. + + @note Result can be parsed with CV_MAT_CN() to obtain amount of channels and CV_MAT_DEPTH() to obtain native cvdata type. + It is thread safe. + */ + CV_WRAP virtual int dsgettype( String dslabel ) const = 0; + + /* @overload */ + CV_WRAP virtual void dswrite( InputArray Array, String dslabel ) const = 0; + /* @overload */ + CV_WRAP virtual void dswrite( InputArray Array, String dslabel, + const int* dims_offset ) const = 0; + /* @overload */ + CV_WRAP virtual void dswrite( InputArray Array, String dslabel, + const vector& dims_offset, + const vector& dims_counts = vector() ) const = 0; + /** @brief Write or overwrite a Mat object into specified dataset of hdf5 file. + @param Array specify Mat data array to be written. + @param dslabel specify the target hdf5 dataset label. + @param dims_offset each array member specify the offset location + over dataset's each dimensions from where InputArray will be (over)written into dataset. + @param dims_counts each array member specify the amount of data over dataset's + each dimensions from InputArray that will be written into dataset. + + Writes Mat object into targeted dataset. + + @note If dataset is not created and does not exist it will be created **automatically**. Only Mat is supported and + it must to be **continuous**. It is thread safe but it is recommended that writes to happen over separate non overlapping + regions. Multiple datasets can be written inside single hdf5 file. + + - Example below writes a 100x100 CV_64FC2 matrix into a dataset. No dataset precreation required. If routine + is called multiple times dataset will be just overwritten: + @code{.cpp} + // dual channel hilbert matrix + cv::Mat H(100, 100, CV_64FC2); + for(int i = 0; i < H.rows; i++) + for(int j = 0; j < H.cols; j++) + { + H.at(i,j)[0] = 1./(i+j+1); + H.at(i,j)[1] = -1./(i+j+1); + count++; + } + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // write / overwrite dataset + h5io->dswrite( H, "hilbert" ); + // release + h5io->close(); + @endcode + + - Example below writes a smaller 50x100 matrix into 100x100 compressed space optimised by two 50x100 chunks. + Matrix is written twice into first half (0->50) and second half (50->100) of data space using offset. + @code{.cpp} + // dual channel hilbert matrix + cv::Mat H(50, 100, CV_64FC2); + for(int i = 0; i < H.rows; i++) + for(int j = 0; j < H.cols; j++) + { + H.at(i,j)[0] = 1./(i+j+1); + H.at(i,j)[1] = -1./(i+j+1); + count++; + } + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // optimise dataset by two chunks + int chunks[2] = { 50, 100 }; + // create 100x100 CV_64FC2 compressed space + h5io->dscreate( 100, 100, CV_64FC2, "hilbert", 9, chunks ); + // write into first half + int offset1[2] = { 0, 0 }; + h5io->dswrite( H, "hilbert", offset1 ); + // write into second half + int offset2[2] = { 50, 0 }; + h5io->dswrite( H, "hilbert", offset2 ); + // release + h5io->close(); + @endcode + */ + CV_WRAP virtual void dswrite( InputArray Array, String dslabel, + const int* dims_offset, const int* dims_counts ) const = 0; + + /* @overload */ + CV_WRAP virtual void dsinsert( InputArray Array, String dslabel ) const = 0; + /* @overload */ + CV_WRAP virtual void dsinsert( InputArray Array, + String dslabel, const int* dims_offset ) const = 0; + /* @overload */ + CV_WRAP virtual void dsinsert( InputArray Array, + String dslabel, const vector& dims_offset, + const vector& dims_counts = vector() ) const = 0; + /** @brief Insert or overwrite a Mat object into specified dataset and autoexpand dataset size if **unlimited** property allows. + @param Array specify Mat data array to be written. + @param dslabel specify the target hdf5 dataset label. + @param dims_offset each array member specify the offset location + over dataset's each dimensions from where InputArray will be (over)written into dataset. + @param dims_counts each array member specify the amount of data over dataset's + each dimensions from InputArray that will be written into dataset. + + Writes Mat object into targeted dataset and **autoexpand** dataset dimension if allowed. + + @note Unlike dswrite(), datasets are **not** created **automatically**. Only Mat is supported and it must to be **continuous**. + If dsinsert() happen over outer regions of dataset dimensions and on that dimension of dataset is in **unlimited** mode then + dataset is expanded, otherwise exception is thrown. To create datasets with **unlimited** property on specific or more + dimensions see dscreate() and the optional H5_UNLIMITED flag at creation time. It is not thread safe over same dataset + but multiple datasets can be merged inside single hdf5 file. + + - Example below creates **unlimited** rows x 100 cols and expand rows 5 times with dsinsert() using single 100x100 CV_64FC2 + over the dataset. Final size will have 5x100 rows and 100 cols, reflecting H matrix five times over row's span. Chunks size is + 100x100 just optimized against the H matrix size having compression disabled. If routine is called multiple times dataset will be + just overwritten: + @code{.cpp} + // dual channel hilbert matrix + cv::Mat H(50, 100, CV_64FC2); + for(int i = 0; i < H.rows; i++) + for(int j = 0; j < H.cols; j++) + { + H.at(i,j)[0] = 1./(i+j+1); + H.at(i,j)[1] = -1./(i+j+1); + count++; + } + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // optimise dataset by chunks + int chunks[2] = { 100, 100 }; + // create Unlimited x 100 CV_64FC2 space + h5io->dscreate( cv::hdf::HDF5::H5_UNLIMITED, 100, CV_64FC2, "hilbert", cv::hdf::HDF5::H5_NONE, chunks ); + // write into first half + int offset[2] = { 0, 0 }; + for ( int t = 0; t < 5; t++ ) + { + offset[0] += 100 * t; + h5io->dsinsert( H, "hilbert", offset ); + } + // release + h5io->close(); + @endcode + */ + CV_WRAP virtual void dsinsert( InputArray Array, String dslabel, + const int* dims_offset, const int* dims_counts ) const = 0; + + + /* @overload */ + CV_WRAP virtual void dsread( OutputArray Array, String dslabel ) const = 0; + /* @overload */ + CV_WRAP virtual void dsread( OutputArray Array, + String dslabel, const int* dims_offset ) const = 0; + /* @overload */ + CV_WRAP virtual void dsread( OutputArray Array, String dslabel, + const vector& dims_offset, + const vector& dims_counts = vector() ) const = 0; + /** @brief Read specific dataset from hdf5 file into Mat object. + @param Array Mat container where data reads will be returned. + @param dslabel specify the source hdf5 dataset label. + @param dims_offset each array member specify the offset location over + each dimensions from where dataset starts to read into OutputArray. + @param dims_counts each array member specify the amount over dataset's each + dimensions of dataset to read into OutputArray. + + Reads out Mat object reflecting the stored dataset. + + @note If hdf5 file does not exist an exception will be thrown. Use hlexists() to check dataset presence. + It is thread safe. + + - Example below reads a dataset: + @code{.cpp} + // open hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // blank Mat container + cv::Mat H; + // read hibert dataset + h5io->read( H, "hilbert" ); + // release + h5io->close(); + @endcode + + - Example below perform read of 3x5 submatrix from second row and third element. + @code{.cpp} + // open hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // blank Mat container + cv::Mat H; + int offset[2] = { 1, 2 }; + int counts[2] = { 3, 5 }; + // read hibert dataset + h5io->read( H, "hilbert", offset, counts ); + // release + h5io->close(); + @endcode + */ + CV_WRAP virtual void dsread( OutputArray Array, String dslabel, + const int* dims_offset, const int* dims_counts ) const = 0; + + /** @brief Fetch keypoint dataset size + @param kplabel specify the hdf5 dataset label to be measured. + @param dims_flag will fetch dataset dimensions on H5_GETDIMS, and dataset maximum dimensions on H5_GETMAXDIMS. + + Returns size of keypoints dataset. + + @note Resulting size will match the amount of keypoints. By default H5_GETDIMS will return actual dataset dimension. + Using H5_GETMAXDIM flag will get maximum allowed dimension which normally match actual dataset dimension but can hold + H5_UNLIMITED value if dataset was prepared in **unlimited** mode. It can be useful to check existing dataset dimension + before overwrite it as whole or subset. Trying to write with oversized source data into dataset target will thrown + exception. The H5_GETCHUNKDIMS will return the dimension of chunk if dataset was created with chunking options otherwise + returned vector size will be zero. + */ + CV_WRAP virtual int kpgetsize( String kplabel, int dims_flag = HDF5::H5_GETDIMS ) const = 0; + + /** @brief Create and allocate special storage for cv::KeyPoint dataset. + @param size declare fixed number of KeyPoints + @param kplabel specify the hdf5 dataset label, any existing dataset with the same label will be overwritten. + @param compresslevel specify the compression level 0-9 to be used, H5_NONE is default and means no compression. + @param chunks each array member specify chunking sizes to be used for block i/o, + H5_NONE is default and means no compression. + @note If the dataset already exists an exception will be thrown. Existence of the dataset can be checked + using hlexists(). + + - See example below that creates space for 100 keypoints in the dataset: + @code{.cpp} + // open hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + if ( ! h5io->hlexists( "keypoints" ) ) + h5io->kpcreate( 100, "keypoints" ); + else + printf("DS already created, skipping\n" ); + @endcode + + @note A value of H5_UNLIMITED for **size** means **unlimited** keypoints, thus is possible to expand anytime such + dataset by adding or inserting. Presence of H5_UNLIMITED **require** to define custom chunking. No default chunking + will be defined in unlimited scenario since default size on that dimension will be zero, and will grow once dataset + is written. Writing into dataset that have H5_UNLIMITED on some of its dimension requires kpinsert() that allow + growth on unlimited dimension instead of kpwrite() that allows to write only in predefined data space. + + - See example below that creates unlimited space for keypoints chunking size of 100 but no compression: + @code{.cpp} + // open hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + if ( ! h5io->hlexists( "keypoints" ) ) + h5io->kpcreate( cv::hdf::HDF5::H5_UNLIMITED, "keypoints", cv::hdf::HDF5::H5_NONE, 100 ); + else + printf("DS already created, skipping\n" ); + @endcode + */ + virtual void kpcreate( const int size, String kplabel, + const int compresslevel = H5_NONE, const int chunks = H5_NONE ) const = 0; + + /** @brief Write or overwrite list of KeyPoint into specified dataset of hdf5 file. + @param keypoints specify keypoints data list to be written. + @param kplabel specify the target hdf5 dataset label. + @param offset specify the offset location on dataset from where keypoints will be (over)written into dataset. + @param counts specify the amount of keypoints that will be written into dataset. + + Writes vector object into targeted dataset. + + @note If dataset is not created and does not exist it will be created **automatically**. It is thread safe but + it is recommended that writes to happen over separate non overlapping regions. Multiple datasets can be written + inside single hdf5 file. + + - Example below writes a 100 keypoints into a dataset. No dataset precreation required. If routine is called multiple + times dataset will be just overwritten: + @code{.cpp} + // generate 100 dummy keypoints + std::vector keypoints; + for(int i = 0; i < 100; i++) + keypoints.push_back( cv::KeyPoint(i, -i, 1, -1, 0, 0, -1) ); + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // write / overwrite dataset + h5io->kpwrite( keypoints, "keypoints" ); + // release + h5io->close(); + @endcode + + - Example below uses smaller set of 50 keypoints and writes into compressed space of 100 keypoints optimised by 10 chunks. + Same keypoint set is written three times, first into first half (0->50) and at second half (50->75) then into remaining slots + (75->99) of data space using offset and count parameters to settle the window for write access.If routine is called multiple times + dataset will be just overwritten: + @code{.cpp} + // generate 50 dummy keypoints + std::vector keypoints; + for(int i = 0; i < 50; i++) + keypoints.push_back( cv::KeyPoint(i, -i, 1, -1, 0, 0, -1) ); + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // create maximum compressed space of size 100 with chunk size 10 + h5io->kpcreate( 100, "keypoints", 9, 10 ); + // write into first half + h5io->kpwrite( keypoints, "keypoints", 0 ); + // write first 25 keypoints into second half + h5io->kpwrite( keypoints, "keypoints", 50, 25 ); + // write first 25 keypoints into remained space of second half + h5io->kpwrite( keypoints, "keypoints", 75, 25 ); + // release + h5io->close(); + @endcode + */ + virtual void kpwrite( const vector keypoints, String kplabel, + const int offset = H5_NONE, const int counts = H5_NONE ) const = 0; + + /** @brief Insert or overwrite list of KeyPoint into specified dataset and autoexpand dataset size if **unlimited** property allows. + @param keypoints specify keypoints data list to be written. + @param kplabel specify the target hdf5 dataset label. + @param offset specify the offset location on dataset from where keypoints will be (over)written into dataset. + @param counts specify the amount of keypoints that will be written into dataset. + + Writes vector object into targeted dataset and **autoexpand** dataset dimension if allowed. + + @note Unlike kpwrite(), datasets are **not** created **automatically**. If dsinsert() happen over outer region of dataset + and dataset has been created in **unlimited** mode then dataset is expanded, otherwise exception is thrown. To create datasets + with **unlimited** property see kpcreate() and the optional H5_UNLIMITED flag at creation time. It is not thread safe over same + dataset but multiple datasets can be merged inside single hdf5 file. + + - Example below creates **unlimited** space for keypoints storage, and inserts a list of 10 keypoints ten times into that space. + Final dataset will have 100 keypoints. Chunks size is 10 just optimized against list of keypoints. If routine is called multiple + times dataset will be just overwritten: + @code{.cpp} + // generate 10 dummy keypoints + std::vector keypoints; + for(int i = 0; i < 10; i++) + keypoints.push_back( cv::KeyPoint(i, -i, 1, -1, 0, 0, -1) ); + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // create unlimited size space with chunk size of 10 + h5io->kpcreate( cv::hdf::HDF5::H5_UNLIMITED, "keypoints", -1, 10 ); + // insert 10 times same 10 keypoints + for(int i = 0; i < 10; i++) + h5io->kpinsert( keypoints, "keypoints", i * 10 ); + // release + h5io->close(); + @endcode + */ + virtual void kpinsert( const vector keypoints, String kplabel, + const int offset = H5_NONE, const int counts = H5_NONE ) const = 0; + + /** @brief Read specific keypoint dataset from hdf5 file into vector object. + @param keypoints vector container where data reads will be returned. + @param kplabel specify the source hdf5 dataset label. + @param offset specify the offset location over dataset from where read starts. + @param counts specify the amount of keypoints from dataset to read. + + Reads out vector object reflecting the stored dataset. + + @note If hdf5 file does not exist an exception will be thrown. Use hlexists() to check dataset presence. + It is thread safe. + + - Example below reads a dataset containing keypoints starting with second entry: + @code{.cpp} + // open hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // blank KeyPoint container + std::vector keypoints; + // read keypoints starting second one + h5io->kpread( keypoints, "keypoints", 1 ); + // release + h5io->close(); + @endcode + + - Example below perform read of 3 keypoints from second entry. + @code{.cpp} + // open hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // blank KeyPoint container + std::vector keypoints; + // read three keypoints starting second one + h5io->kpread( keypoints, "keypoints", 1, 3 ); + // release + h5io->close(); + @endcode + */ + virtual void kpread( vector& keypoints, String kplabel, + const int offset = H5_NONE, const int counts = H5_NONE ) const = 0; + +}; + + /** @brief Open or create hdf5 file + @param HDF5Filename specify the HDF5 filename. + + Returns pointer to the hdf5 object class + + @note If hdf5 file does not exist it will be created. Any operations except dscreate() functions on object + will be thread safe. Multiple datasets can be created inside single hdf5 file, and can be accessed + from same hdf5 object from multiple instances as long read or write operations are done over + non-overlapping regions of dataset. Single hdf5 file also can be opened by multiple instances, + reads and writes can be instantiated at the same time as long non-overlapping regions are involved. Object + is released using close(). + + - Example below open and then release the file. + @code{.cpp} + // open / autocreate hdf5 file + cv::Ptr h5io = cv::hdf::open( "mytest.h5" ); + // ... + // release + h5io->close(); + @endcode + + ![Visualization of 10x10 CV_64FC2 (Hilbert matrix) using HDFView tool](pics/hdfview_demo.gif) + + - Text dump (3x3 Hilbert matrix) of hdf5 dataset using **h5dump** tool: + @code{.txt} + $ h5dump test.h5 + HDF5 "test.h5" { + GROUP "/" { + DATASET "hilbert" { + DATATYPE H5T_ARRAY { [2] H5T_IEEE_F64LE } + DATASPACE SIMPLE { ( 3, 3 ) / ( 3, 3 ) } + DATA { + (0,0): [ 1, -1 ], [ 0.5, -0.5 ], [ 0.333333, -0.333333 ], + (1,0): [ 0.5, -0.5 ], [ 0.333333, -0.333333 ], [ 0.25, -0.25 ], + (2,0): [ 0.333333, -0.333333 ], [ 0.25, -0.25 ], [ 0.2, -0.2 ] + } + } + } + } + @endcode + */ + CV_EXPORTS_W Ptr open( String HDF5Filename ); + +//! @} + +} // end namespace hdf +} // end namespace cv +#endif // _OPENCV_HDF5_HPP_ diff --git a/thirdparty/linux/include/opencv2/highgui.hpp b/thirdparty/linux/include/opencv2/highgui.hpp new file mode 100644 index 0000000..16ef8c4 --- /dev/null +++ b/thirdparty/linux/include/opencv2/highgui.hpp @@ -0,0 +1,790 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HIGHGUI_HPP +#define OPENCV_HIGHGUI_HPP + +#include "opencv2/core.hpp" +#ifdef HAVE_OPENCV_IMGCODECS +#include "opencv2/imgcodecs.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOIO +#include "opencv2/videoio.hpp" +#endif + +/** +@defgroup highgui High-level GUI + +While OpenCV was designed for use in full-scale applications and can be used within functionally +rich UI frameworks (such as Qt\*, WinForms\*, or Cocoa\*) or without any UI at all, sometimes there +it is required to try functionality quickly and visualize the results. This is what the HighGUI +module has been designed for. + +It provides easy interface to: + +- Create and manipulate windows that can display images and "remember" their content (no need to + handle repaint events from OS). +- Add trackbars to the windows, handle simple mouse events as well as keyboard commands. + +@{ + @defgroup highgui_opengl OpenGL support + @defgroup highgui_qt Qt New Functions + + ![image](pics/qtgui.png) + + This figure explains new functionality implemented with Qt\* GUI. The new GUI provides a statusbar, + a toolbar, and a control panel. The control panel can have trackbars and buttonbars attached to it. + If you cannot see the control panel, press Ctrl+P or right-click any Qt window and select **Display + properties window**. + + - To attach a trackbar, the window name parameter must be NULL. + + - To attach a buttonbar, a button must be created. If the last bar attached to the control panel + is a buttonbar, the new button is added to the right of the last button. If the last bar + attached to the control panel is a trackbar, or the control panel is empty, a new buttonbar is + created. Then, a new button is attached to it. + + See below the example used to generate the figure: + @code + int main(int argc, char *argv[]) + { + + int value = 50; + int value2 = 0; + + + namedWindow("main1",WINDOW_NORMAL); + namedWindow("main2",WINDOW_AUTOSIZE | CV_GUI_NORMAL); + createTrackbar( "track1", "main1", &value, 255, NULL); + + String nameb1 = "button1"; + String nameb2 = "button2"; + + createButton(nameb1,callbackButton,&nameb1,QT_CHECKBOX,1); + createButton(nameb2,callbackButton,NULL,QT_CHECKBOX,0); + createTrackbar( "track2", NULL, &value2, 255, NULL); + createButton("button5",callbackButton1,NULL,QT_RADIOBOX,0); + createButton("button6",callbackButton2,NULL,QT_RADIOBOX,1); + + setMouseCallback( "main2",on_mouse,NULL ); + + Mat img1 = imread("files/flower.jpg"); + VideoCapture video; + video.open("files/hockey.avi"); + + Mat img2,img3; + + while( waitKey(33) != 27 ) + { + img1.convertTo(img2,-1,1,value); + video >> img3; + + imshow("main1",img2); + imshow("main2",img3); + } + + destroyAllWindows(); + + return 0; + } + @endcode + + + @defgroup highgui_winrt WinRT support + + This figure explains new functionality implemented with WinRT GUI. The new GUI provides an Image control, + and a slider panel. Slider panel holds trackbars attached to it. + + Sliders are attached below the image control. Every new slider is added below the previous one. + + See below the example used to generate the figure: + @code + void sample_app::MainPage::ShowWindow() + { + static cv::String windowName("sample"); + cv::winrt_initContainer(this->cvContainer); + cv::namedWindow(windowName); // not required + + cv::Mat image = cv::imread("Assets/sample.jpg"); + cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4); + cv::cvtColor(image, converted, COLOR_BGR2BGRA); + cv::imshow(windowName, converted); // this will create window if it hasn't been created before + + int state = 42; + cv::TrackbarCallback callback = [](int pos, void* userdata) + { + if (pos == 0) { + cv::destroyWindow(windowName); + } + }; + cv::TrackbarCallback callbackTwin = [](int pos, void* userdata) + { + if (pos >= 70) { + cv::destroyAllWindows(); + } + }; + cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback); + cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin); + } + @endcode + + @defgroup highgui_c C API +@} +*/ + +///////////////////////// graphical user interface ////////////////////////// +namespace cv +{ + +//! @addtogroup highgui +//! @{ + +//! Flags for cv::namedWindow +enum WindowFlags { + WINDOW_NORMAL = 0x00000000, //!< the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size. + WINDOW_AUTOSIZE = 0x00000001, //!< the user cannot resize the window, the size is constrainted by the image displayed. + WINDOW_OPENGL = 0x00001000, //!< window with opengl support. + + WINDOW_FULLSCREEN = 1, //!< change the window to fullscreen. + WINDOW_FREERATIO = 0x00000100, //!< the image expends as much as it can (no ratio constraint). + WINDOW_KEEPRATIO = 0x00000000, //!< the ratio of the image is respected. + WINDOW_GUI_EXPANDED=0x00000000, //!< status bar and tool bar + WINDOW_GUI_NORMAL = 0x00000010, //!< old fashious way + }; + +//! Flags for cv::setWindowProperty / cv::getWindowProperty +enum WindowPropertyFlags { + WND_PROP_FULLSCREEN = 0, //!< fullscreen property (can be WINDOW_NORMAL or WINDOW_FULLSCREEN). + WND_PROP_AUTOSIZE = 1, //!< autosize property (can be WINDOW_NORMAL or WINDOW_AUTOSIZE). + WND_PROP_ASPECT_RATIO = 2, //!< window's aspect ration (can be set to WINDOW_FREERATIO or WINDOW_KEEPRATIO). + WND_PROP_OPENGL = 3, //!< opengl support. + WND_PROP_VISIBLE = 4 //!< checks whether the window exists and is visible + }; + +//! Mouse Events see cv::MouseCallback +enum MouseEventTypes { + EVENT_MOUSEMOVE = 0, //!< indicates that the mouse pointer has moved over the window. + EVENT_LBUTTONDOWN = 1, //!< indicates that the left mouse button is pressed. + EVENT_RBUTTONDOWN = 2, //!< indicates that the right mouse button is pressed. + EVENT_MBUTTONDOWN = 3, //!< indicates that the middle mouse button is pressed. + EVENT_LBUTTONUP = 4, //!< indicates that left mouse button is released. + EVENT_RBUTTONUP = 5, //!< indicates that right mouse button is released. + EVENT_MBUTTONUP = 6, //!< indicates that middle mouse button is released. + EVENT_LBUTTONDBLCLK = 7, //!< indicates that left mouse button is double clicked. + EVENT_RBUTTONDBLCLK = 8, //!< indicates that right mouse button is double clicked. + EVENT_MBUTTONDBLCLK = 9, //!< indicates that middle mouse button is double clicked. + EVENT_MOUSEWHEEL = 10,//!< positive and negative values mean forward and backward scrolling, respectively. + EVENT_MOUSEHWHEEL = 11 //!< positive and negative values mean right and left scrolling, respectively. + }; + +//! Mouse Event Flags see cv::MouseCallback +enum MouseEventFlags { + EVENT_FLAG_LBUTTON = 1, //!< indicates that the left mouse button is down. + EVENT_FLAG_RBUTTON = 2, //!< indicates that the right mouse button is down. + EVENT_FLAG_MBUTTON = 4, //!< indicates that the middle mouse button is down. + EVENT_FLAG_CTRLKEY = 8, //!< indicates that CTRL Key is pressed. + EVENT_FLAG_SHIFTKEY = 16,//!< indicates that SHIFT Key is pressed. + EVENT_FLAG_ALTKEY = 32 //!< indicates that ALT Key is pressed. + }; + +//! Qt font weight +enum QtFontWeights { + QT_FONT_LIGHT = 25, //!< Weight of 25 + QT_FONT_NORMAL = 50, //!< Weight of 50 + QT_FONT_DEMIBOLD = 63, //!< Weight of 63 + QT_FONT_BOLD = 75, //!< Weight of 75 + QT_FONT_BLACK = 87 //!< Weight of 87 + }; + +//! Qt font style +enum QtFontStyles { + QT_STYLE_NORMAL = 0, //!< Normal font. + QT_STYLE_ITALIC = 1, //!< Italic font. + QT_STYLE_OBLIQUE = 2 //!< Oblique font. + }; + +//! Qt "button" type +enum QtButtonTypes { + QT_PUSH_BUTTON = 0, //!< Push button. + QT_CHECKBOX = 1, //!< Checkbox button. + QT_RADIOBOX = 2, //!< Radiobox button. + QT_NEW_BUTTONBAR = 1024 //!< Button should create a new buttonbar + }; + +/** @brief Callback function for mouse events. see cv::setMouseCallback +@param event one of the cv::MouseEventTypes constants. +@param x The x-coordinate of the mouse event. +@param y The y-coordinate of the mouse event. +@param flags one of the cv::MouseEventFlags constants. +@param userdata The optional parameter. + */ +typedef void (*MouseCallback)(int event, int x, int y, int flags, void* userdata); + +/** @brief Callback function for Trackbar see cv::createTrackbar +@param pos current position of the specified trackbar. +@param userdata The optional parameter. + */ +typedef void (*TrackbarCallback)(int pos, void* userdata); + +/** @brief Callback function defined to be called every frame. See cv::setOpenGlDrawCallback +@param userdata The optional parameter. + */ +typedef void (*OpenGlDrawCallback)(void* userdata); + +/** @brief Callback function for a button created by cv::createButton +@param state current state of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. +@param userdata The optional parameter. + */ +typedef void (*ButtonCallback)(int state, void* userdata); + +/** @brief Creates a window. + +The function namedWindow creates a window that can be used as a placeholder for images and +trackbars. Created windows are referred to by their names. + +If a window with the same name already exists, the function does nothing. + +You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated +memory usage. For a simple program, you do not really have to call these functions because all the +resources and windows of the application are closed automatically by the operating system upon exit. + +@note + +Qt backend supports additional flags: + - **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the + window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the + displayed image (see imshow ), and you cannot change the window size manually. + - **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image + with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio. + - **WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED:** WINDOW_GUI_NORMAL is the old way to draw the window + without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI. +By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED + +@param winname Name of the window in the window caption that may be used as a window identifier. +@param flags Flags of the window. The supported flags are: (cv::WindowFlags) + */ +CV_EXPORTS_W void namedWindow(const String& winname, int flags = WINDOW_AUTOSIZE); + +/** @brief Destroys the specified window. + +The function destroyWindow destroys the window with the given name. + +@param winname Name of the window to be destroyed. + */ +CV_EXPORTS_W void destroyWindow(const String& winname); + +/** @brief Destroys all of the HighGUI windows. + +The function destroyAllWindows destroys all of the opened HighGUI windows. + */ +CV_EXPORTS_W void destroyAllWindows(); + +CV_EXPORTS_W int startWindowThread(); + +/** @brief Similar to #waitKey, but returns full key code. + +@note + +Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc + +*/ +CV_EXPORTS_W int waitKeyEx(int delay = 0); + +/** @brief Waits for a pressed key. + +The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay +milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the +function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is +running on your computer at that time. It returns the code of the pressed key or -1 if no key was +pressed before the specified time had elapsed. + +@note + +This function is the only method in HighGUI that can fetch and handle events, so it needs to be +called periodically for normal event processing unless HighGUI is used within an environment that +takes care of event processing. + +@note + +The function only works if there is at least one HighGUI window created and the window is active. +If there are several HighGUI windows, any of them can be active. + +@param delay Delay in milliseconds. 0 is the special value that means "forever". + */ +CV_EXPORTS_W int waitKey(int delay = 0); + +/** @brief Displays an image in the specified window. + +The function imshow displays an image in the specified window. If the window was created with the +cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution. +Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth: + +- If the image is 8-bit unsigned, it is displayed as is. +- If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the + value range [0,255\*256] is mapped to [0,255]. +- If the image is 32-bit floating-point, the pixel values are multiplied by 255. That is, the + value range [0,1] is mapped to [0,255]. + +If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and +cuda::GpuMat as input. + +If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE. + +If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow. + +@note This function should be followed by cv::waitKey function which displays the image for specified +milliseconds. Otherwise, it won't display the image. For example, **waitKey(0)** will display the window +infinitely until any keypress (it is suitable for image display). **waitKey(25)** will display a frame +for 25 ms, after which display will be automatically closed. (If you put it in a loop to read +videos, it will display the video frame-by-frame) + +@note + +[__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard. + +[__Windows Backend Only__] Pressing Ctrl+S will show a dialog to save the image. + +@param winname Name of the window. +@param mat Image to be shown. + */ +CV_EXPORTS_W void imshow(const String& winname, InputArray mat); + +/** @brief Resizes window to the specified size + +@note + +- The specified window size is for the image area. Toolbars are not counted. +- Only windows created without cv::WINDOW_AUTOSIZE flag can be resized. + +@param winname Window name. +@param width The new window width. +@param height The new window height. + */ +CV_EXPORTS_W void resizeWindow(const String& winname, int width, int height); + +/** @brief Moves window to the specified position + +@param winname Name of the window. +@param x The new x-coordinate of the window. +@param y The new y-coordinate of the window. + */ +CV_EXPORTS_W void moveWindow(const String& winname, int x, int y); + +/** @brief Changes parameters of a window dynamically. + +The function setWindowProperty enables changing properties of a window. + +@param winname Name of the window. +@param prop_id Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags) +@param prop_value New value of the window property. The supported flags are: (cv::WindowFlags) + */ +CV_EXPORTS_W void setWindowProperty(const String& winname, int prop_id, double prop_value); + +/** @brief Updates window title +@param winname Name of the window. +@param title New title. +*/ +CV_EXPORTS_W void setWindowTitle(const String& winname, const String& title); + +/** @brief Provides parameters of a window. + +The function getWindowProperty returns properties of a window. + +@param winname Name of the window. +@param prop_id Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags) + +@sa setWindowProperty + */ +CV_EXPORTS_W double getWindowProperty(const String& winname, int prop_id); + +/** @brief Sets mouse handler for the specified window + +@param winname Name of the window. +@param onMouse Mouse callback. See OpenCV samples, such as +, on how to specify and +use the callback. +@param userdata The optional parameter passed to the callback. + */ +CV_EXPORTS void setMouseCallback(const String& winname, MouseCallback onMouse, void* userdata = 0); + +/** @brief Gets the mouse-wheel motion delta, when handling mouse-wheel events cv::EVENT_MOUSEWHEEL and +cv::EVENT_MOUSEHWHEEL. + +For regular mice with a scroll-wheel, delta will be a multiple of 120. The value 120 corresponds to +a one notch rotation of the wheel or the threshold for action to be taken and one such action should +occur for each delta. Some high-precision mice with higher-resolution freely-rotating wheels may +generate smaller values. + +For cv::EVENT_MOUSEWHEEL positive and negative values mean forward and backward scrolling, +respectively. For cv::EVENT_MOUSEHWHEEL, where available, positive and negative values mean right and +left scrolling, respectively. + +With the C API, the macro CV_GET_WHEEL_DELTA(flags) can be used alternatively. + +@note + +Mouse-wheel events are currently supported only on Windows. + +@param flags The mouse callback flags parameter. + */ +CV_EXPORTS int getMouseWheelDelta(int flags); + +/** @brief Creates a trackbar and attaches it to the specified window. + +The function createTrackbar creates a trackbar (a slider or range control) with the specified name +and range, assigns a variable value to be a position synchronized with the trackbar and specifies +the callback function onChange to be called on the trackbar position change. The created trackbar is +displayed in the specified window winname. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar should be attached to the +control panel. + +Clicking the label of each trackbar enables editing the trackbar values manually. + +@param trackbarname Name of the created trackbar. +@param winname Name of the window that will be used as a parent of the created trackbar. +@param value Optional pointer to an integer variable whose value reflects the position of the +slider. Upon creation, the slider position is defined by this variable. +@param count Maximal position of the slider. The minimal position is always 0. +@param onChange Pointer to the function to be called every time the slider changes position. This +function should be prototyped as void Foo(int,void\*); , where the first parameter is the trackbar +position and the second parameter is the user data (see the next parameter). If the callback is +the NULL pointer, no callbacks are called, but only value is updated. +@param userdata User data that is passed as is to the callback. It can be used to handle trackbar +events without using global variables. + */ +CV_EXPORTS int createTrackbar(const String& trackbarname, const String& winname, + int* value, int count, + TrackbarCallback onChange = 0, + void* userdata = 0); + +/** @brief Returns the trackbar position. + +The function returns the current position of the specified trackbar. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control +panel. + +@param trackbarname Name of the trackbar. +@param winname Name of the window that is the parent of the trackbar. + */ +CV_EXPORTS_W int getTrackbarPos(const String& trackbarname, const String& winname); + +/** @brief Sets the trackbar position. + +The function sets the position of the specified trackbar in the specified window. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control +panel. + +@param trackbarname Name of the trackbar. +@param winname Name of the window that is the parent of trackbar. +@param pos New position. + */ +CV_EXPORTS_W void setTrackbarPos(const String& trackbarname, const String& winname, int pos); + +/** @brief Sets the trackbar maximum position. + +The function sets the maximum position of the specified trackbar in the specified window. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control +panel. + +@param trackbarname Name of the trackbar. +@param winname Name of the window that is the parent of trackbar. +@param maxval New maximum position. + */ +CV_EXPORTS_W void setTrackbarMax(const String& trackbarname, const String& winname, int maxval); + +/** @brief Sets the trackbar minimum position. + +The function sets the minimum position of the specified trackbar in the specified window. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control +panel. + +@param trackbarname Name of the trackbar. +@param winname Name of the window that is the parent of trackbar. +@param minval New maximum position. + */ +CV_EXPORTS_W void setTrackbarMin(const String& trackbarname, const String& winname, int minval); + +//! @addtogroup highgui_opengl OpenGL support +//! @{ + +/** @brief Displays OpenGL 2D texture in the specified window. + +@param winname Name of the window. +@param tex OpenGL 2D texture data. + */ +CV_EXPORTS void imshow(const String& winname, const ogl::Texture2D& tex); + +/** @brief Sets a callback function to be called to draw on top of displayed image. + +The function setOpenGlDrawCallback can be used to draw 3D data on the window. See the example of +callback function below: +@code + void on_opengl(void* param) + { + glLoadIdentity(); + + glTranslated(0.0, 0.0, -1.0); + + glRotatef( 55, 1, 0, 0 ); + glRotatef( 45, 0, 1, 0 ); + glRotatef( 0, 0, 0, 1 ); + + static const int coords[6][4][3] = { + { { +1, -1, -1 }, { -1, -1, -1 }, { -1, +1, -1 }, { +1, +1, -1 } }, + { { +1, +1, -1 }, { -1, +1, -1 }, { -1, +1, +1 }, { +1, +1, +1 } }, + { { +1, -1, +1 }, { +1, -1, -1 }, { +1, +1, -1 }, { +1, +1, +1 } }, + { { -1, -1, -1 }, { -1, -1, +1 }, { -1, +1, +1 }, { -1, +1, -1 } }, + { { +1, -1, +1 }, { -1, -1, +1 }, { -1, -1, -1 }, { +1, -1, -1 } }, + { { -1, -1, +1 }, { +1, -1, +1 }, { +1, +1, +1 }, { -1, +1, +1 } } + }; + + for (int i = 0; i < 6; ++i) { + glColor3ub( i*20, 100+i*10, i*42 ); + glBegin(GL_QUADS); + for (int j = 0; j < 4; ++j) { + glVertex3d(0.2 * coords[i][j][0], 0.2 * coords[i][j][1], 0.2 * coords[i][j][2]); + } + glEnd(); + } + } +@endcode + +@param winname Name of the window. +@param onOpenGlDraw Pointer to the function to be called every frame. This function should be +prototyped as void Foo(void\*) . +@param userdata Pointer passed to the callback function.(__Optional__) + */ +CV_EXPORTS void setOpenGlDrawCallback(const String& winname, OpenGlDrawCallback onOpenGlDraw, void* userdata = 0); + +/** @brief Sets the specified window as current OpenGL context. + +@param winname Name of the window. + */ +CV_EXPORTS void setOpenGlContext(const String& winname); + +/** @brief Force window to redraw its context and call draw callback ( See cv::setOpenGlDrawCallback ). + +@param winname Name of the window. + */ +CV_EXPORTS void updateWindow(const String& winname); + +//! @} highgui_opengl + +//! @addtogroup highgui_qt +//! @{ + +/** @brief QtFont available only for Qt. See cv::fontQt + */ +struct QtFont +{ + const char* nameFont; //!< Name of the font + Scalar color; //!< Color of the font. Scalar(blue_component, green_component, red_component[, alpha_component]) + int font_face; //!< See cv::QtFontStyles + const int* ascii; //!< font data and metrics + const int* greek; + const int* cyrillic; + float hscale, vscale; + float shear; //!< slope coefficient: 0 - normal, >0 - italic + int thickness; //!< See cv::QtFontWeights + float dx; //!< horizontal interval between letters + int line_type; //!< PointSize +}; + +/** @brief Creates the font to draw a text on an image. + +The function fontQt creates a cv::QtFont object. This cv::QtFont is not compatible with putText . + +A basic usage of this function is the following: : +@code + QtFont font = fontQt("Times"); + addText( img1, "Hello World !", Point(50,50), font); +@endcode + +@param nameFont Name of the font. The name should match the name of a system font (such as +*Times*). If the font is not found, a default one is used. +@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the +font is set to a system-dependent default value. Generally, this is 12 points. +@param color Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV_RGB +for simplicity. +@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. +@param style Font style. Available operation flags are : cv::QtFontStyles +@param spacing Spacing between characters. It can be negative or positive. + */ +CV_EXPORTS QtFont fontQt(const String& nameFont, int pointSize = -1, + Scalar color = Scalar::all(0), int weight = QT_FONT_NORMAL, + int style = QT_STYLE_NORMAL, int spacing = 0); + +/** @brief Draws a text on the image. + +The function addText draws *text* on the image *img* using a specific font *font* (see example cv::fontQt +) + +@param img 8-bit 3-channel image where the text should be drawn. +@param text Text to write on an image. +@param org Point(x,y) where the text should start on an image. +@param font Font to use to draw a text. + */ +CV_EXPORTS void addText( const Mat& img, const String& text, Point org, const QtFont& font); + +/** @brief Draws a text on the image. + +@param img 8-bit 3-channel image where the text should be drawn. +@param text Text to write on an image. +@param org Point(x,y) where the text should start on an image. +@param nameFont Name of the font. The name should match the name of a system font (such as +*Times*). If the font is not found, a default one is used. +@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the +font is set to a system-dependent default value. Generally, this is 12 points. +@param color Color of the font in BGRA where A = 255 is fully transparent. +@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. +@param style Font style. Available operation flags are : cv::QtFontStyles +@param spacing Spacing between characters. It can be negative or positive. + */ +CV_EXPORTS_W void addText(const Mat& img, const String& text, Point org, const String& nameFont, int pointSize = -1, Scalar color = Scalar::all(0), + int weight = QT_FONT_NORMAL, int style = QT_STYLE_NORMAL, int spacing = 0); + +/** @brief Displays a text on a window image as an overlay for a specified duration. + +The function displayOverlay displays useful information/tips on top of the window for a certain +amount of time *delayms*. The function does not modify the image, displayed in the window, that is, +after the specified delay the original content of the window is restored. + +@param winname Name of the window. +@param text Overlay text to write on a window image. +@param delayms The period (in milliseconds), during which the overlay text is displayed. If this +function is called before the previous overlay text timed out, the timer is restarted and the text +is updated. If this value is zero, the text never disappears. + */ +CV_EXPORTS_W void displayOverlay(const String& winname, const String& text, int delayms = 0); + +/** @brief Displays a text on the window statusbar during the specified period of time. + +The function displayStatusBar displays useful information/tips on top of the window for a certain +amount of time *delayms* . This information is displayed on the window statusbar (the window must be +created with the CV_GUI_EXPANDED flags). + +@param winname Name of the window. +@param text Text to write on the window statusbar. +@param delayms Duration (in milliseconds) to display the text. If this function is called before +the previous text timed out, the timer is restarted and the text is updated. If this value is +zero, the text never disappears. + */ +CV_EXPORTS_W void displayStatusBar(const String& winname, const String& text, int delayms = 0); + +/** @brief Saves parameters of the specified window. + +The function saveWindowParameters saves size, location, flags, trackbars value, zoom and panning +location of the window windowName. + +@param windowName Name of the window. + */ +CV_EXPORTS void saveWindowParameters(const String& windowName); + +/** @brief Loads parameters of the specified window. + +The function loadWindowParameters loads size, location, flags, trackbars value, zoom and panning +location of the window windowName. + +@param windowName Name of the window. + */ +CV_EXPORTS void loadWindowParameters(const String& windowName); + +CV_EXPORTS int startLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); + +CV_EXPORTS void stopLoop(); + +/** @brief Attaches a button to the control panel. + +The function createButton attaches a button to the control panel. Each button is added to a +buttonbar to the right of the last button. A new buttonbar is created if nothing was attached to the +control panel before, or if the last element attached to the control panel was a trackbar or if the +QT_NEW_BUTTONBAR flag is added to the type. + +See below various examples of the cv::createButton function call: : +@code + createButton(NULL,callbackButton);//create a push button "button 0", that will call callbackButton. + createButton("button2",callbackButton,NULL,QT_CHECKBOX,0); + createButton("button3",callbackButton,&value); + createButton("button5",callbackButton1,NULL,QT_RADIOBOX); + createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON,1); + createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON|QT_NEW_BUTTONBAR);// create a push button in a new row +@endcode + +@param bar_name Name of the button. +@param on_change Pointer to the function to be called every time the button changes its state. +This function should be prototyped as void Foo(int state,\*void); . *state* is the current state +of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. +@param userdata Pointer passed to the callback function. +@param type Optional type of the button. Available types are: (cv::QtButtonTypes) +@param initial_button_state Default state of the button. Use for checkbox and radiobox. Its +value could be 0 or 1. (__Optional__) +*/ +CV_EXPORTS int createButton( const String& bar_name, ButtonCallback on_change, + void* userdata = 0, int type = QT_PUSH_BUTTON, + bool initial_button_state = false); + +//! @} highgui_qt + +//! @} highgui + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/highgui/highgui_c.h" +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/highgui/highgui.hpp b/thirdparty/linux/include/opencv2/highgui/highgui.hpp new file mode 100644 index 0000000..160c9cf --- /dev/null +++ b/thirdparty/linux/include/opencv2/highgui/highgui.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/highgui.hpp" diff --git a/thirdparty/linux/include/opencv2/highgui/highgui_c.h b/thirdparty/linux/include/opencv2/highgui/highgui_c.h new file mode 100644 index 0000000..71c08ca --- /dev/null +++ b/thirdparty/linux/include/opencv2/highgui/highgui_c.h @@ -0,0 +1,257 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HIGHGUI_H +#define OPENCV_HIGHGUI_H + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#ifdef HAVE_OPENCV_IMGCODECS +#include "opencv2/imgcodecs/imgcodecs_c.h" +#endif +#ifdef HAVE_OPENCV_VIDEOIO +#include "opencv2/videoio/videoio_c.h" +#endif + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** @addtogroup highgui_c + @{ + */ + +/****************************************************************************************\ +* Basic GUI functions * +\****************************************************************************************/ +//YV +//-----------New for Qt +/* For font */ +enum { CV_FONT_LIGHT = 25,//QFont::Light, + CV_FONT_NORMAL = 50,//QFont::Normal, + CV_FONT_DEMIBOLD = 63,//QFont::DemiBold, + CV_FONT_BOLD = 75,//QFont::Bold, + CV_FONT_BLACK = 87 //QFont::Black +}; + +enum { CV_STYLE_NORMAL = 0,//QFont::StyleNormal, + CV_STYLE_ITALIC = 1,//QFont::StyleItalic, + CV_STYLE_OBLIQUE = 2 //QFont::StyleOblique +}; +/* ---------*/ + +//for color cvScalar(blue_component, green_component, red_component[, alpha_component]) +//and alpha= 0 <-> 0xFF (not transparent <-> transparent) +CVAPI(CvFont) cvFontQt(const char* nameFont, int pointSize CV_DEFAULT(-1), CvScalar color CV_DEFAULT(cvScalarAll(0)), int weight CV_DEFAULT(CV_FONT_NORMAL), int style CV_DEFAULT(CV_STYLE_NORMAL), int spacing CV_DEFAULT(0)); + +CVAPI(void) cvAddText(const CvArr* img, const char* text, CvPoint org, CvFont *arg2); + +CVAPI(void) cvDisplayOverlay(const char* name, const char* text, int delayms CV_DEFAULT(0)); +CVAPI(void) cvDisplayStatusBar(const char* name, const char* text, int delayms CV_DEFAULT(0)); + +CVAPI(void) cvSaveWindowParameters(const char* name); +CVAPI(void) cvLoadWindowParameters(const char* name); +CVAPI(int) cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); +CVAPI(void) cvStopLoop( void ); + +typedef void (CV_CDECL *CvButtonCallback)(int state, void* userdata); +enum {CV_PUSH_BUTTON = 0, CV_CHECKBOX = 1, CV_RADIOBOX = 2}; +CVAPI(int) cvCreateButton( const char* button_name CV_DEFAULT(NULL),CvButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL) , int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0)); +//---------------------- + + +/* this function is used to set some external parameters in case of X Window */ +CVAPI(int) cvInitSystem( int argc, char** argv ); + +CVAPI(int) cvStartWindowThread( void ); + +// --------- YV --------- +enum +{ + //These 3 flags are used by cvSet/GetWindowProperty + CV_WND_PROP_FULLSCREEN = 0, //to change/get window's fullscreen property + CV_WND_PROP_AUTOSIZE = 1, //to change/get window's autosize property + CV_WND_PROP_ASPECTRATIO= 2, //to change/get window's aspectratio property + CV_WND_PROP_OPENGL = 3, //to change/get window's opengl support + CV_WND_PROP_VISIBLE = 4, + + //These 2 flags are used by cvNamedWindow and cvSet/GetWindowProperty + CV_WINDOW_NORMAL = 0x00000000, //the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size + CV_WINDOW_AUTOSIZE = 0x00000001, //the user cannot resize the window, the size is constrainted by the image displayed + CV_WINDOW_OPENGL = 0x00001000, //window with opengl support + + //Those flags are only for Qt + CV_GUI_EXPANDED = 0x00000000, //status bar and tool bar + CV_GUI_NORMAL = 0x00000010, //old fashious way + + //These 3 flags are used by cvNamedWindow and cvSet/GetWindowProperty + CV_WINDOW_FULLSCREEN = 1,//change the window to fullscreen + CV_WINDOW_FREERATIO = 0x00000100,//the image expends as much as it can (no ratio constraint) + CV_WINDOW_KEEPRATIO = 0x00000000//the ration image is respected. +}; + +/* create window */ +CVAPI(int) cvNamedWindow( const char* name, int flags CV_DEFAULT(CV_WINDOW_AUTOSIZE) ); + +/* Set and Get Property of the window */ +CVAPI(void) cvSetWindowProperty(const char* name, int prop_id, double prop_value); +CVAPI(double) cvGetWindowProperty(const char* name, int prop_id); + +/* display image within window (highgui windows remember their content) */ +CVAPI(void) cvShowImage( const char* name, const CvArr* image ); + +/* resize/move window */ +CVAPI(void) cvResizeWindow( const char* name, int width, int height ); +CVAPI(void) cvMoveWindow( const char* name, int x, int y ); + + +/* destroy window and all the trackers associated with it */ +CVAPI(void) cvDestroyWindow( const char* name ); + +CVAPI(void) cvDestroyAllWindows(void); + +/* get native window handle (HWND in case of Win32 and Widget in case of X Window) */ +CVAPI(void*) cvGetWindowHandle( const char* name ); + +/* get name of highgui window given its native handle */ +CVAPI(const char*) cvGetWindowName( void* window_handle ); + + +typedef void (CV_CDECL *CvTrackbarCallback)(int pos); + +/* create trackbar and display it on top of given window, set callback */ +CVAPI(int) cvCreateTrackbar( const char* trackbar_name, const char* window_name, + int* value, int count, CvTrackbarCallback on_change CV_DEFAULT(NULL)); + +typedef void (CV_CDECL *CvTrackbarCallback2)(int pos, void* userdata); + +CVAPI(int) cvCreateTrackbar2( const char* trackbar_name, const char* window_name, + int* value, int count, CvTrackbarCallback2 on_change, + void* userdata CV_DEFAULT(0)); + +/* retrieve or set trackbar position */ +CVAPI(int) cvGetTrackbarPos( const char* trackbar_name, const char* window_name ); +CVAPI(void) cvSetTrackbarPos( const char* trackbar_name, const char* window_name, int pos ); +CVAPI(void) cvSetTrackbarMax(const char* trackbar_name, const char* window_name, int maxval); +CVAPI(void) cvSetTrackbarMin(const char* trackbar_name, const char* window_name, int minval); + +enum +{ + CV_EVENT_MOUSEMOVE =0, + CV_EVENT_LBUTTONDOWN =1, + CV_EVENT_RBUTTONDOWN =2, + CV_EVENT_MBUTTONDOWN =3, + CV_EVENT_LBUTTONUP =4, + CV_EVENT_RBUTTONUP =5, + CV_EVENT_MBUTTONUP =6, + CV_EVENT_LBUTTONDBLCLK =7, + CV_EVENT_RBUTTONDBLCLK =8, + CV_EVENT_MBUTTONDBLCLK =9, + CV_EVENT_MOUSEWHEEL =10, + CV_EVENT_MOUSEHWHEEL =11 +}; + +enum +{ + CV_EVENT_FLAG_LBUTTON =1, + CV_EVENT_FLAG_RBUTTON =2, + CV_EVENT_FLAG_MBUTTON =4, + CV_EVENT_FLAG_CTRLKEY =8, + CV_EVENT_FLAG_SHIFTKEY =16, + CV_EVENT_FLAG_ALTKEY =32 +}; + + +#define CV_GET_WHEEL_DELTA(flags) ((short)((flags >> 16) & 0xffff)) // upper 16 bits + +typedef void (CV_CDECL *CvMouseCallback )(int event, int x, int y, int flags, void* param); + +/* assign callback for mouse events */ +CVAPI(void) cvSetMouseCallback( const char* window_name, CvMouseCallback on_mouse, + void* param CV_DEFAULT(NULL)); + +/* wait for key event infinitely (delay<=0) or for "delay" milliseconds */ +CVAPI(int) cvWaitKey(int delay CV_DEFAULT(0)); + +// OpenGL support + +typedef void (CV_CDECL *CvOpenGlDrawCallback)(void* userdata); +CVAPI(void) cvSetOpenGlDrawCallback(const char* window_name, CvOpenGlDrawCallback callback, void* userdata CV_DEFAULT(NULL)); + +CVAPI(void) cvSetOpenGlContext(const char* window_name); +CVAPI(void) cvUpdateWindow(const char* window_name); + + +/****************************************************************************************\ + +* Obsolete functions/synonyms * +\****************************************************************************************/ + +#define cvAddSearchPath(path) +#define cvvInitSystem cvInitSystem +#define cvvNamedWindow cvNamedWindow +#define cvvShowImage cvShowImage +#define cvvResizeWindow cvResizeWindow +#define cvvDestroyWindow cvDestroyWindow +#define cvvCreateTrackbar cvCreateTrackbar +#define cvvAddSearchPath cvAddSearchPath +#define cvvWaitKey(name) cvWaitKey(0) +#define cvvWaitKeyEx(name,delay) cvWaitKey(delay) +#define HG_AUTOSIZE CV_WINDOW_AUTOSIZE +#define set_preprocess_func cvSetPreprocessFuncWin32 +#define set_postprocess_func cvSetPostprocessFuncWin32 + +#if defined WIN32 || defined _WIN32 + +CVAPI(void) cvSetPreprocessFuncWin32_(const void* callback); +CVAPI(void) cvSetPostprocessFuncWin32_(const void* callback); +#define cvSetPreprocessFuncWin32(callback) cvSetPreprocessFuncWin32_((const void*)(callback)) +#define cvSetPostprocessFuncWin32(callback) cvSetPostprocessFuncWin32_((const void*)(callback)) + +#endif + +/** @} highgui_c */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/imgcodecs.hpp b/thirdparty/linux/include/opencv2/imgcodecs.hpp new file mode 100644 index 0000000..6359de6 --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgcodecs.hpp @@ -0,0 +1,281 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGCODECS_HPP +#define OPENCV_IMGCODECS_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup imgcodecs Image file reading and writing + @{ + @defgroup imgcodecs_c C API + @defgroup imgcodecs_ios iOS glue + @} +*/ + +//////////////////////////////// image codec //////////////////////////////// +namespace cv +{ + +//! @addtogroup imgcodecs +//! @{ + +//! Imread flags +enum ImreadModes { + IMREAD_UNCHANGED = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped). + IMREAD_GRAYSCALE = 0, //!< If set, always convert image to the single channel grayscale image. + IMREAD_COLOR = 1, //!< If set, always convert image to the 3 channel BGR color image. + IMREAD_ANYDEPTH = 2, //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit. + IMREAD_ANYCOLOR = 4, //!< If set, the image is read in any possible color format. + IMREAD_LOAD_GDAL = 8, //!< If set, use the gdal driver for loading the image. + IMREAD_REDUCED_GRAYSCALE_2 = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2. + IMREAD_REDUCED_COLOR_2 = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2. + IMREAD_REDUCED_GRAYSCALE_4 = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4. + IMREAD_REDUCED_COLOR_4 = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4. + IMREAD_REDUCED_GRAYSCALE_8 = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8. + IMREAD_REDUCED_COLOR_8 = 65, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8. + IMREAD_IGNORE_ORIENTATION = 128 //!< If set, do not rotate the image according to EXIF's orientation flag. + }; + +//! Imwrite flags +enum ImwriteFlags { + IMWRITE_JPEG_QUALITY = 1, //!< For JPEG, it can be a quality from 0 to 100 (the higher is the better). Default value is 95. + IMWRITE_JPEG_PROGRESSIVE = 2, //!< Enable JPEG features, 0 or 1, default is False. + IMWRITE_JPEG_OPTIMIZE = 3, //!< Enable JPEG features, 0 or 1, default is False. + IMWRITE_JPEG_RST_INTERVAL = 4, //!< JPEG restart interval, 0 - 65535, default is 0 - no restart. + IMWRITE_JPEG_LUMA_QUALITY = 5, //!< Separate luma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_JPEG_CHROMA_QUALITY = 6, //!< Separate chroma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_PNG_COMPRESSION = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. Default value is 3. Also strategy is changed to IMWRITE_PNG_STRATEGY_DEFAULT (Z_DEFAULT_STRATEGY). + IMWRITE_PNG_STRATEGY = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_DEFAULT. + IMWRITE_PNG_BILEVEL = 18, //!< Binary level PNG, 0 or 1, default is 0. + IMWRITE_PXM_BINARY = 32, //!< For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1. + IMWRITE_WEBP_QUALITY = 64, //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used. + IMWRITE_PAM_TUPLETYPE = 128,//!< For PAM, sets the TUPLETYPE field to the corresponding string value that is defined for the format + }; + +//! Imwrite PNG specific flags used to tune the compression algorithm. +/** These flags will be modify the way of PNG image compression and will be passed to the underlying zlib processing stage. + +- The effect of IMWRITE_PNG_STRATEGY_FILTERED is to force more Huffman coding and less string matching; it is somewhat intermediate between IMWRITE_PNG_STRATEGY_DEFAULT and IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY. +- IMWRITE_PNG_STRATEGY_RLE is designed to be almost as fast as IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, but give better compression for PNG image data. +- The strategy parameter only affects the compression ratio but not the correctness of the compressed output even if it is not set appropriately. +- IMWRITE_PNG_STRATEGY_FIXED prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. +*/ +enum ImwritePNGFlags { + IMWRITE_PNG_STRATEGY_DEFAULT = 0, //!< Use this value for normal data. + IMWRITE_PNG_STRATEGY_FILTERED = 1, //!< Use this value for data produced by a filter (or predictor).Filtered data consists mostly of small values with a somewhat random distribution. In this case, the compression algorithm is tuned to compress them better. + IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2, //!< Use this value to force Huffman encoding only (no string match). + IMWRITE_PNG_STRATEGY_RLE = 3, //!< Use this value to limit match distances to one (run-length encoding). + IMWRITE_PNG_STRATEGY_FIXED = 4 //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. + }; + +//! Imwrite PAM specific tupletype flags used to define the 'TUPETYPE' field of a PAM file. +enum ImwritePAMFlags { + IMWRITE_PAM_FORMAT_NULL = 0, + IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, + IMWRITE_PAM_FORMAT_RGB = 4, + IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, + }; + +/** @brief Loads an image from a file. + +@anchor imread + +The function imread loads an image from the specified file and returns it. If the image cannot be +read (because of missing file, improper permissions, unsupported or invalid format), the function +returns an empty matrix ( Mat::data==NULL ). + +Currently, the following file formats are supported: + +- Windows bitmaps - \*.bmp, \*.dib (always supported) +- JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Notes* section) +- JPEG 2000 files - \*.jp2 (see the *Notes* section) +- Portable Network Graphics - \*.png (see the *Notes* section) +- WebP - \*.webp (see the *Notes* section) +- Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported) +- Sun rasters - \*.sr, \*.ras (always supported) +- TIFF files - \*.tiff, \*.tif (see the *Notes* section) +- OpenEXR Image files - \*.exr (see the *Notes* section) +- Radiance HDR - \*.hdr, \*.pic (always supported) +- Raster and Vector geospatial data supported by Gdal (see the *Notes* section) + +@note + +- The function determines the type of an image by the content, not by the file extension. +- In the case of color images, the decoded images will have the channels stored in **B G R** order. +- On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg, + libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs, + and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware + that currently these native image loaders give images with different pixel values because of + the color management embedded into MacOSX. +- On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for + codecs supplied with an OS image. Install the relevant packages (do not forget the development + files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn + on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake. +- In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image, + then [GDAL](http://www.gdal.org) driver will be used in order to decode the image by supporting + the following formats: [Raster](http://www.gdal.org/formats_list.html), + [Vector](http://www.gdal.org/ogr_formats.html). +- If EXIF information are embedded in the image file, the EXIF orientation will be taken into account + and thus the image will be rotated accordingly except if the flag @ref IMREAD_IGNORE_ORIENTATION is passed. +@param filename Name of file to be loaded. +@param flags Flag that can take values of cv::ImreadModes +*/ +CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR ); + +/** @brief Loads a multi-page image from a file. + +The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. +@param filename Name of file to be loaded. +@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR. +@param mats A vector of Mat objects holding each page, if more than one. +@sa cv::imread +*/ +CV_EXPORTS_W bool imreadmulti(const String& filename, std::vector& mats, int flags = IMREAD_ANYCOLOR); + +/** @brief Saves an image to a specified file. + +The function imwrite saves the image to the specified file. The image format is chosen based on the +filename extension (see cv::imread for the list of extensions). Only 8-bit (or 16-bit unsigned (CV_16U) +in case of PNG, JPEG 2000, and TIFF) single-channel or 3-channel (with 'BGR' channel order) images +can be saved using this function. If the format, depth or channel order is different, use +Mat::convertTo , and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O +functions to save the image to XML or YAML format. + +It is possible to store PNG images with an alpha channel using this function. To do this, create +8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels +should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535. + +The sample below shows how to create such a BGRA image and store to PNG file. It also demonstrates how to set custom +compression parameters : +@code + #include + + using namespace cv; + using namespace std; + + void createAlphaMat(Mat &mat) + { + CV_Assert(mat.channels() == 4); + for (int i = 0; i < mat.rows; ++i) { + for (int j = 0; j < mat.cols; ++j) { + Vec4b& bgra = mat.at(i, j); + bgra[0] = UCHAR_MAX; // Blue + bgra[1] = saturate_cast((float (mat.cols - j)) / ((float)mat.cols) * UCHAR_MAX); // Green + bgra[2] = saturate_cast((float (mat.rows - i)) / ((float)mat.rows) * UCHAR_MAX); // Red + bgra[3] = saturate_cast(0.5 * (bgra[1] + bgra[2])); // Alpha + } + } + } + + int main(int argv, char **argc) + { + // Create mat with alpha channel + Mat mat(480, 640, CV_8UC4); + createAlphaMat(mat); + + vector compression_params; + compression_params.push_back(IMWRITE_PNG_COMPRESSION); + compression_params.push_back(9); + + try { + imwrite("alpha.png", mat, compression_params); + } + catch (cv::Exception& ex) { + fprintf(stderr, "Exception converting image to PNG format: %s\n", ex.what()); + return 1; + } + + fprintf(stdout, "Saved PNG file with alpha data.\n"); + return 0; + } +@endcode +@param filename Name of the file. +@param img Image to be saved. +@param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags +*/ +CV_EXPORTS_W bool imwrite( const String& filename, InputArray img, + const std::vector& params = std::vector()); + +/** @brief Reads an image from a buffer in memory. + +The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or +contains invalid data, the function returns an empty matrix ( Mat::data==NULL ). + +See cv::imread for the list of supported formats and flags description. + +@note In the case of color images, the decoded images will have the channels stored in **B G R** order. +@param buf Input array or vector of bytes. +@param flags The same flags as in cv::imread, see cv::ImreadModes. +*/ +CV_EXPORTS_W Mat imdecode( InputArray buf, int flags ); + +/** @overload +@param buf +@param flags +@param dst The optional output placeholder for the decoded matrix. It can save the image +reallocations when the function is called repeatedly for images of the same size. +*/ +CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst); + +/** @brief Encodes an image into a memory buffer. + +The function imencode compresses the image and stores it in the memory buffer that is resized to fit the +result. See cv::imwrite for the list of supported formats and flags description. + +@param ext File extension that defines the output format. +@param img Image to be written. +@param buf Output buffer resized to fit the compressed image. +@param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags. +*/ +CV_EXPORTS_W bool imencode( const String& ext, InputArray img, + CV_OUT std::vector& buf, + const std::vector& params = std::vector()); + +//! @} imgcodecs + +} // cv + +#endif //OPENCV_IMGCODECS_HPP diff --git a/thirdparty/linux/include/opencv2/imgcodecs/imgcodecs.hpp b/thirdparty/linux/include/opencv2/imgcodecs/imgcodecs.hpp new file mode 100644 index 0000000..a3cd232 --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgcodecs/imgcodecs.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/imgcodecs.hpp" diff --git a/thirdparty/linux/include/opencv2/imgcodecs/imgcodecs_c.h b/thirdparty/linux/include/opencv2/imgcodecs/imgcodecs_c.h new file mode 100644 index 0000000..3130710 --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgcodecs/imgcodecs_c.h @@ -0,0 +1,148 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGCODECS_H +#define OPENCV_IMGCODECS_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** @addtogroup imgcodecs_c + @{ + */ + +enum +{ +/* 8bit, color or not */ + CV_LOAD_IMAGE_UNCHANGED =-1, +/* 8bit, gray */ + CV_LOAD_IMAGE_GRAYSCALE =0, +/* ?, color */ + CV_LOAD_IMAGE_COLOR =1, +/* any depth, ? */ + CV_LOAD_IMAGE_ANYDEPTH =2, +/* ?, any color */ + CV_LOAD_IMAGE_ANYCOLOR =4, +/* ?, no rotate */ + CV_LOAD_IMAGE_IGNORE_ORIENTATION =128 +}; + +/* load image from file + iscolor can be a combination of above flags where CV_LOAD_IMAGE_UNCHANGED + overrides the other flags + using CV_LOAD_IMAGE_ANYCOLOR alone is equivalent to CV_LOAD_IMAGE_UNCHANGED + unless CV_LOAD_IMAGE_ANYDEPTH is specified images are converted to 8bit +*/ +CVAPI(IplImage*) cvLoadImage( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); +CVAPI(CvMat*) cvLoadImageM( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); + +enum +{ + CV_IMWRITE_JPEG_QUALITY =1, + CV_IMWRITE_JPEG_PROGRESSIVE =2, + CV_IMWRITE_JPEG_OPTIMIZE =3, + CV_IMWRITE_JPEG_RST_INTERVAL =4, + CV_IMWRITE_JPEG_LUMA_QUALITY =5, + CV_IMWRITE_JPEG_CHROMA_QUALITY =6, + CV_IMWRITE_PNG_COMPRESSION =16, + CV_IMWRITE_PNG_STRATEGY =17, + CV_IMWRITE_PNG_BILEVEL =18, + CV_IMWRITE_PNG_STRATEGY_DEFAULT =0, + CV_IMWRITE_PNG_STRATEGY_FILTERED =1, + CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY =2, + CV_IMWRITE_PNG_STRATEGY_RLE =3, + CV_IMWRITE_PNG_STRATEGY_FIXED =4, + CV_IMWRITE_PXM_BINARY =32, + CV_IMWRITE_WEBP_QUALITY =64, + CV_IMWRITE_PAM_TUPLETYPE = 128, + CV_IMWRITE_PAM_FORMAT_NULL = 0, + CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + CV_IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, + CV_IMWRITE_PAM_FORMAT_RGB = 4, + CV_IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, +}; + + + +/* save image to file */ +CVAPI(int) cvSaveImage( const char* filename, const CvArr* image, + const int* params CV_DEFAULT(0) ); + +/* decode image stored in the buffer */ +CVAPI(IplImage*) cvDecodeImage( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); +CVAPI(CvMat*) cvDecodeImageM( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); + +/* encode image and store the result as a byte vector (single-row 8uC1 matrix) */ +CVAPI(CvMat*) cvEncodeImage( const char* ext, const CvArr* image, + const int* params CV_DEFAULT(0) ); + +enum +{ + CV_CVTIMG_FLIP =1, + CV_CVTIMG_SWAP_RB =2 +}; + +/* utility function: convert one image to another with optional vertical flip */ +CVAPI(void) cvConvertImage( const CvArr* src, CvArr* dst, int flags CV_DEFAULT(0)); + +CVAPI(int) cvHaveImageReader(const char* filename); +CVAPI(int) cvHaveImageWriter(const char* filename); + + +/****************************************************************************************\ +* Obsolete functions/synonyms * +\****************************************************************************************/ + +#define cvvLoadImage(name) cvLoadImage((name),1) +#define cvvSaveImage cvSaveImage +#define cvvConvertImage cvConvertImage + +/** @} imgcodecs_c */ + +#ifdef __cplusplus +} +#endif + +#endif // OPENCV_IMGCODECS_H diff --git a/thirdparty/linux/include/opencv2/imgcodecs/ios.h b/thirdparty/linux/include/opencv2/imgcodecs/ios.h new file mode 100644 index 0000000..fbd6371 --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgcodecs/ios.h @@ -0,0 +1,57 @@ + +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#import +#import +#import +#import +#include "opencv2/core/core.hpp" + +//! @addtogroup imgcodecs_ios +//! @{ + +UIImage* MatToUIImage(const cv::Mat& image); +void UIImageToMat(const UIImage* image, + cv::Mat& m, bool alphaExist = false); + +//! @} diff --git a/thirdparty/linux/include/opencv2/imgproc.hpp b/thirdparty/linux/include/opencv2/imgproc.hpp new file mode 100644 index 0000000..243d72b --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgproc.hpp @@ -0,0 +1,4650 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_HPP +#define OPENCV_IMGPROC_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup imgproc Image processing + @{ + @defgroup imgproc_filter Image Filtering + +Functions and classes described in this section are used to perform various linear or non-linear +filtering operations on 2D images (represented as Mat's). It means that for each pixel location +\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to +compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of +morphological operations, it is the minimum or maximum values, and so on. The computed response is +stored in the destination image at the same location \f$(x,y)\f$. It means that the output image +will be of the same size as the input image. Normally, the functions support multi-channel arrays, +in which case every channel is processed independently. Therefore, the output image will also have +the same number of channels as the input one. + +Another common feature of the functions and classes described in this section is that, unlike +simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For +example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when +processing the left-most pixels in each row, you need pixels to the left of them, that is, outside +of the image. You can let these pixels be the same as the left-most image pixels ("replicated +border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant +border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. +For details, see cv::BorderTypes + +@anchor filter_depths +### Depth combinations +Input depth (src.depth()) | Output depth (ddepth) +--------------------------|---------------------- +CV_8U | -1/CV_16S/CV_32F/CV_64F +CV_16U/CV_16S | -1/CV_32F/CV_64F +CV_32F | -1/CV_32F/CV_64F +CV_64F | -1/CV_64F + +@note when ddepth=-1, the output image will have the same depth as the source. + + @defgroup imgproc_transform Geometric Image Transformations + +The functions in this section perform various geometrical transformations of 2D images. They do not +change the image content but deform the pixel grid and map this deformed grid to the destination +image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from +destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the +functions compute coordinates of the corresponding "donor" pixel in the source image and copy the +pixel value: + +\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f] + +In case when you specify the forward mapping \f$\left: \texttt{src} \rightarrow +\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping +\f$\left: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula. + +The actual implementations of the geometrical transformations, from the most generic remap and to +the simplest and the fastest resize, need to solve two main problems with the above formula: + +- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the +previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both +of them may fall outside of the image. In this case, an extrapolation method needs to be used. +OpenCV provides the same selection of extrapolation methods as in the filtering functions. In +addition, it provides the method BORDER_TRANSPARENT. This means that the corresponding pixels in +the destination image will not be modified at all. + +- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point +numbers. This means that \f$\left\f$ can be either an affine or perspective +transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional +coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the +nearest integer coordinates and the corresponding pixel can be used. This is called a +nearest-neighbor interpolation. However, a better result can be achieved by using more +sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) , +where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y), +f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the +interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See +resize for details. + + @defgroup imgproc_misc Miscellaneous Image Transformations + @defgroup imgproc_draw Drawing Functions + +Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be +rendered with antialiasing (implemented only for 8-bit images for now). All the functions include +the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) +for color images and brightness for grayscale images. For color images, the channel ordering is +normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a +color using the Scalar constructor, it should look like: + +\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f] + +If you are using your own image rendering and I/O functions, you can use any channel ordering. The +drawing functions process each channel independently and do not depend on the channel order or even +on the used color space. The whole image can be converted from BGR to RGB or to a different color +space using cvtColor . + +If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, +many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means +that the coordinates can be passed as fixed-point numbers encoded as integers. The number of +fractional bits is specified by the shift parameter and the real point coordinates are calculated as +\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is +especially effective when rendering antialiased shapes. + +@note The functions do not support alpha-transparency when the target image is 4-channel. In this +case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint +semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main +image. + + @defgroup imgproc_colormap ColorMaps in OpenCV + +The human perception isn't built for observing fine changes in grayscale images. Human eyes are more +sensitive to observing changes between colors, so you often need to recolor your grayscale images to +get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your +computer vision application. + +In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample +code reads the path to an image from command line, applies a Jet colormap on it and shows the +result: + +@code +#include +#include +#include +#include +using namespace cv; + +#include +using namespace std; + +int main(int argc, const char *argv[]) +{ + // We need an input image. (can be grayscale or color) + if (argc < 2) + { + cerr << "We need an image to process here. Please run: colorMap [path_to_image]" << endl; + return -1; + } + Mat img_in = imread(argv[1]); + if(img_in.empty()) + { + cerr << "Sample image (" << argv[1] << ") is empty. Please adjust your path, so it points to a valid input image!" << endl; + return -1; + } + // Holds the colormap version of the image: + Mat img_color; + // Apply the colormap: + applyColorMap(img_in, img_color, COLORMAP_JET); + // Show the result: + imshow("colorMap", img_color); + waitKey(0); + return 0; +} +@endcode + +@see cv::ColormapTypes + + @defgroup imgproc_subdiv2d Planar Subdivision + +The Subdiv2D class described in this section is used to perform various planar subdivision on +a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles +using the Delaunay’s algorithm, which corresponds to the dual graph of the Voronoi diagram. +In the figure below, the Delaunay’s triangulation is marked with black lines and the Voronoi +diagram with red lines. + +![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png) + +The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast +location of points on the plane, building special graphs (such as NNG,RNG), and so forth. + + @defgroup imgproc_hist Histograms + @defgroup imgproc_shape Structural Analysis and Shape Descriptors + @defgroup imgproc_motion Motion Analysis and Object Tracking + @defgroup imgproc_feature Feature Detection + @defgroup imgproc_object Object Detection + @defgroup imgproc_c C API + @defgroup imgproc_hal Hardware Acceleration Layer + @{ + @defgroup imgproc_hal_functions Functions + @defgroup imgproc_hal_interface Interface + @} + @} +*/ + +namespace cv +{ + +/** @addtogroup imgproc +@{ +*/ + +//! @addtogroup imgproc_filter +//! @{ + +//! type of morphological operation +enum MorphTypes{ + MORPH_ERODE = 0, //!< see cv::erode + MORPH_DILATE = 1, //!< see cv::dilate + MORPH_OPEN = 2, //!< an opening operation + //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f] + MORPH_CLOSE = 3, //!< a closing operation + //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f] + MORPH_GRADIENT = 4, //!< a morphological gradient + //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f] + MORPH_TOPHAT = 5, //!< "top hat" + //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f] + MORPH_BLACKHAT = 6, //!< "black hat" + //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f] + MORPH_HITMISS = 7 //!< "hit or miss" + //!< .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation +}; + +//! shape of the structuring element +enum MorphShapes { + MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f] + MORPH_CROSS = 1, //!< a cross-shaped structuring element: + //!< \f[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f] + MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed + //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height) +}; + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +//! interpolation algorithm +enum InterpolationFlags{ + /** nearest neighbor interpolation */ + INTER_NEAREST = 0, + /** bilinear interpolation */ + INTER_LINEAR = 1, + /** bicubic interpolation */ + INTER_CUBIC = 2, + /** resampling using pixel area relation. It may be a preferred method for image decimation, as + it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST + method. */ + INTER_AREA = 3, + /** Lanczos interpolation over 8x8 neighborhood */ + INTER_LANCZOS4 = 4, + /** mask for interpolation codes */ + INTER_MAX = 7, + /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the + source image, they are set to zero */ + WARP_FILL_OUTLIERS = 8, + /** flag, inverse transformation + + For example, @ref cv::linearPolar or @ref cv::logPolar transforms: + - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$ + - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$ + */ + WARP_INVERSE_MAP = 16 +}; + +enum InterpolationMasks { + INTER_BITS = 5, + INTER_BITS2 = INTER_BITS * 2, + INTER_TAB_SIZE = 1 << INTER_BITS, + INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE + }; + +//! @} imgproc_transform + +//! @addtogroup imgproc_misc +//! @{ + +//! Distance types for Distance Transform and M-estimators +//! @see cv::distanceTransform, cv::fitLine +enum DistanceTypes { + DIST_USER = -1, //!< User defined distance + DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2| + DIST_L2 = 2, //!< the simple euclidean distance + DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|) + DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) + DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 + DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 + DIST_HUBER = 7 //!< distance = |x| \texttt{thresh}\)}{0}{otherwise}\f] + THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f] + THRESH_TRUNC = 2, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] + THRESH_TOZERO = 3, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f] + THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] + THRESH_MASK = 7, + THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value + THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value +}; + +//! adaptive threshold algorithm +//! see cv::adaptiveThreshold +enum AdaptiveThresholdTypes { + /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times + \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */ + ADAPTIVE_THRESH_MEAN_C = 0, + /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian + window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ + minus C . The default sigma (standard deviation) is used for the specified blockSize . See + cv::getGaussianKernel*/ + ADAPTIVE_THRESH_GAUSSIAN_C = 1 +}; + +//! cv::undistort mode +enum UndistortTypes { + PROJ_SPHERICAL_ORTHO = 0, + PROJ_SPHERICAL_EQRECT = 1 + }; + +//! class of the pixel in GrabCut algorithm +enum GrabCutClasses { + GC_BGD = 0, //!< an obvious background pixels + GC_FGD = 1, //!< an obvious foreground (object) pixel + GC_PR_BGD = 2, //!< a possible background pixel + GC_PR_FGD = 3 //!< a possible foreground pixel +}; + +//! GrabCut algorithm flags +enum GrabCutModes { + /** The function initializes the state and the mask using the provided rectangle. After that it + runs iterCount iterations of the algorithm. */ + GC_INIT_WITH_RECT = 0, + /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT + and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are + automatically initialized with GC_BGD .*/ + GC_INIT_WITH_MASK = 1, + /** The value means that the algorithm should just resume. */ + GC_EVAL = 2 +}; + +//! distanceTransform algorithm flags +enum DistanceTransformLabelTypes { + /** each connected component of zeros in src (as well as all the non-zero pixels closest to the + connected component) will be assigned the same label */ + DIST_LABEL_CCOMP = 0, + /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */ + DIST_LABEL_PIXEL = 1 +}; + +//! floodfill algorithm flags +enum FloodFillFlags { + /** If set, the difference between the current pixel and seed pixel is considered. Otherwise, + the difference between neighbor pixels is considered (that is, the range is floating). */ + FLOODFILL_FIXED_RANGE = 1 << 16, + /** If set, the function does not change the image ( newVal is ignored), and only fills the + mask with the value specified in bits 8-16 of flags as described above. This option only make + sense in function variants that have the mask parameter. */ + FLOODFILL_MASK_ONLY = 1 << 17 +}; + +//! @} imgproc_misc + +//! @addtogroup imgproc_shape +//! @{ + +//! connected components algorithm output formats +enum ConnectedComponentsTypes { + CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding + //!< box in the horizontal direction. + CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding + //!< box in the vertical direction. + CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box + CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box + CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component + CC_STAT_MAX = 5 +}; + +//! connected components algorithm +enum ConnectedComponentsAlgorithmsTypes { + CCL_WU = 0, //!< SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity + CCL_DEFAULT = -1, //!< BBDT algortihm for 8-way connectivity, SAUF algorithm for 4-way connectivity + CCL_GRANA = 1 //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity +}; + +//! mode of the contour retrieval algorithm +enum RetrievalModes { + /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for + all the contours. */ + RETR_EXTERNAL = 0, + /** retrieves all of the contours without establishing any hierarchical relationships. */ + RETR_LIST = 1, + /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top + level, there are external boundaries of the components. At the second level, there are + boundaries of the holes. If there is another contour inside a hole of a connected component, it + is still put at the top level. */ + RETR_CCOMP = 2, + /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/ + RETR_TREE = 3, + RETR_FLOODFILL = 4 //!< +}; + +//! the contour approximation algorithm +enum ContourApproximationModes { + /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and + (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, + max(abs(x1-x2),abs(y2-y1))==1. */ + CHAIN_APPROX_NONE = 1, + /** compresses horizontal, vertical, and diagonal segments and leaves only their end points. + For example, an up-right rectangular contour is encoded with 4 points. */ + CHAIN_APPROX_SIMPLE = 2, + /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ + CHAIN_APPROX_TC89_L1 = 3, + /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ + CHAIN_APPROX_TC89_KCOS = 4 +}; + +//! @} imgproc_shape + +//! Variants of a Hough transform +enum HoughModes { + + /** classical or standard Hough transform. Every line is represented by two floating-point + numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line, + and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must + be (the created sequence will be) of CV_32FC2 type */ + HOUGH_STANDARD = 0, + /** probabilistic Hough transform (more efficient in case if the picture contains a few long + linear segments). It returns line segments rather than the whole line. Each segment is + represented by starting and ending points, and the matrix must be (the created sequence will + be) of the CV_32SC4 type. */ + HOUGH_PROBABILISTIC = 1, + /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as + HOUGH_STANDARD. */ + HOUGH_MULTI_SCALE = 2, + HOUGH_GRADIENT = 3 //!< basically *21HT*, described in @cite Yuen90 +}; + +//! Variants of Line Segment %Detector +//! @ingroup imgproc_feature +enum LineSegmentDetectorModes { + LSD_REFINE_NONE = 0, //!< No refinement applied + LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. + LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are + //!< refined through increase of precision, decrement in size, etc. +}; + +/** Histogram comparison methods + @ingroup imgproc_hist +*/ +enum HistCompMethods { + /** Correlation + \f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f] + where + \f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f] + and \f$N\f$ is a total number of histogram bins. */ + HISTCMP_CORREL = 0, + /** Chi-Square + \f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */ + HISTCMP_CHISQR = 1, + /** Intersection + \f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f] */ + HISTCMP_INTERSECT = 2, + /** Bhattacharyya distance + (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) + \f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */ + HISTCMP_BHATTACHARYYA = 3, + HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA + /** Alternative Chi-Square + \f[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f] + This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */ + HISTCMP_CHISQR_ALT = 4, + /** Kullback-Leibler divergence + \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */ + HISTCMP_KL_DIV = 5 +}; + +/** the color conversion code +@see @ref imgproc_color_conversions +@ingroup imgproc_misc + */ +enum ColorConversionCodes { + COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image + COLOR_RGB2RGBA = COLOR_BGR2BGRA, + + COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image + COLOR_RGBA2RGB = COLOR_BGRA2BGR, + + COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel) + COLOR_RGB2BGRA = COLOR_BGR2RGBA, + + COLOR_RGBA2BGR = 3, + COLOR_BGRA2RGB = COLOR_RGBA2BGR, + + COLOR_BGR2RGB = 4, + COLOR_RGB2BGR = COLOR_BGR2RGB, + + COLOR_BGRA2RGBA = 5, + COLOR_RGBA2BGRA = COLOR_BGRA2RGBA, + + COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions" + COLOR_RGB2GRAY = 7, + COLOR_GRAY2BGR = 8, + COLOR_GRAY2RGB = COLOR_GRAY2BGR, + COLOR_GRAY2BGRA = 9, + COLOR_GRAY2RGBA = COLOR_GRAY2BGRA, + COLOR_BGRA2GRAY = 10, + COLOR_RGBA2GRAY = 11, + + COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images) + COLOR_RGB2BGR565 = 13, + COLOR_BGR5652BGR = 14, + COLOR_BGR5652RGB = 15, + COLOR_BGRA2BGR565 = 16, + COLOR_RGBA2BGR565 = 17, + COLOR_BGR5652BGRA = 18, + COLOR_BGR5652RGBA = 19, + + COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images) + COLOR_BGR5652GRAY = 21, + + COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images) + COLOR_RGB2BGR555 = 23, + COLOR_BGR5552BGR = 24, + COLOR_BGR5552RGB = 25, + COLOR_BGRA2BGR555 = 26, + COLOR_RGBA2BGR555 = 27, + COLOR_BGR5552BGRA = 28, + COLOR_BGR5552RGBA = 29, + + COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images) + COLOR_BGR5552GRAY = 31, + + COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions" + COLOR_RGB2XYZ = 33, + COLOR_XYZ2BGR = 34, + COLOR_XYZ2RGB = 35, + + COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions" + COLOR_RGB2YCrCb = 37, + COLOR_YCrCb2BGR = 38, + COLOR_YCrCb2RGB = 39, + + COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions" + COLOR_RGB2HSV = 41, + + COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions" + COLOR_RGB2Lab = 45, + + COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions" + COLOR_RGB2Luv = 51, + COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions" + COLOR_RGB2HLS = 53, + + COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR + COLOR_HSV2RGB = 55, + + COLOR_Lab2BGR = 56, + COLOR_Lab2RGB = 57, + COLOR_Luv2BGR = 58, + COLOR_Luv2RGB = 59, + COLOR_HLS2BGR = 60, + COLOR_HLS2RGB = 61, + + COLOR_BGR2HSV_FULL = 66, //!< + COLOR_RGB2HSV_FULL = 67, + COLOR_BGR2HLS_FULL = 68, + COLOR_RGB2HLS_FULL = 69, + + COLOR_HSV2BGR_FULL = 70, + COLOR_HSV2RGB_FULL = 71, + COLOR_HLS2BGR_FULL = 72, + COLOR_HLS2RGB_FULL = 73, + + COLOR_LBGR2Lab = 74, + COLOR_LRGB2Lab = 75, + COLOR_LBGR2Luv = 76, + COLOR_LRGB2Luv = 77, + + COLOR_Lab2LBGR = 78, + COLOR_Lab2LRGB = 79, + COLOR_Luv2LBGR = 80, + COLOR_Luv2LRGB = 81, + + COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV + COLOR_RGB2YUV = 83, + COLOR_YUV2BGR = 84, + COLOR_YUV2RGB = 85, + + //! YUV 4:2:0 family to RGB + COLOR_YUV2RGB_NV12 = 90, + COLOR_YUV2BGR_NV12 = 91, + COLOR_YUV2RGB_NV21 = 92, + COLOR_YUV2BGR_NV21 = 93, + COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21, + COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21, + + COLOR_YUV2RGBA_NV12 = 94, + COLOR_YUV2BGRA_NV12 = 95, + COLOR_YUV2RGBA_NV21 = 96, + COLOR_YUV2BGRA_NV21 = 97, + COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21, + COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21, + + COLOR_YUV2RGB_YV12 = 98, + COLOR_YUV2BGR_YV12 = 99, + COLOR_YUV2RGB_IYUV = 100, + COLOR_YUV2BGR_IYUV = 101, + COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV, + COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV, + COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12, + COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12, + + COLOR_YUV2RGBA_YV12 = 102, + COLOR_YUV2BGRA_YV12 = 103, + COLOR_YUV2RGBA_IYUV = 104, + COLOR_YUV2BGRA_IYUV = 105, + COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV, + COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV, + COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12, + COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12, + + COLOR_YUV2GRAY_420 = 106, + COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420, + COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420, + COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420, + + //! YUV 4:2:2 family to RGB + COLOR_YUV2RGB_UYVY = 107, + COLOR_YUV2BGR_UYVY = 108, + //COLOR_YUV2RGB_VYUY = 109, + //COLOR_YUV2BGR_VYUY = 110, + COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY, + COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY, + COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY, + COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY, + + COLOR_YUV2RGBA_UYVY = 111, + COLOR_YUV2BGRA_UYVY = 112, + //COLOR_YUV2RGBA_VYUY = 113, + //COLOR_YUV2BGRA_VYUY = 114, + COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY, + COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY, + COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY, + COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY, + + COLOR_YUV2RGB_YUY2 = 115, + COLOR_YUV2BGR_YUY2 = 116, + COLOR_YUV2RGB_YVYU = 117, + COLOR_YUV2BGR_YVYU = 118, + COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2, + COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2, + COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2, + COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2, + + COLOR_YUV2RGBA_YUY2 = 119, + COLOR_YUV2BGRA_YUY2 = 120, + COLOR_YUV2RGBA_YVYU = 121, + COLOR_YUV2BGRA_YVYU = 122, + COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2, + COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2, + COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2, + COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2, + + COLOR_YUV2GRAY_UYVY = 123, + COLOR_YUV2GRAY_YUY2 = 124, + //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2, + COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2, + COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2, + + //! alpha premultiplication + COLOR_RGBA2mRGBA = 125, + COLOR_mRGBA2RGBA = 126, + + //! RGB to YUV 4:2:0 family + COLOR_RGB2YUV_I420 = 127, + COLOR_BGR2YUV_I420 = 128, + COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420, + COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420, + + COLOR_RGBA2YUV_I420 = 129, + COLOR_BGRA2YUV_I420 = 130, + COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420, + COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420, + COLOR_RGB2YUV_YV12 = 131, + COLOR_BGR2YUV_YV12 = 132, + COLOR_RGBA2YUV_YV12 = 133, + COLOR_BGRA2YUV_YV12 = 134, + + //! Demosaicing + COLOR_BayerBG2BGR = 46, + COLOR_BayerGB2BGR = 47, + COLOR_BayerRG2BGR = 48, + COLOR_BayerGR2BGR = 49, + + COLOR_BayerBG2RGB = COLOR_BayerRG2BGR, + COLOR_BayerGB2RGB = COLOR_BayerGR2BGR, + COLOR_BayerRG2RGB = COLOR_BayerBG2BGR, + COLOR_BayerGR2RGB = COLOR_BayerGB2BGR, + + COLOR_BayerBG2GRAY = 86, + COLOR_BayerGB2GRAY = 87, + COLOR_BayerRG2GRAY = 88, + COLOR_BayerGR2GRAY = 89, + + //! Demosaicing using Variable Number of Gradients + COLOR_BayerBG2BGR_VNG = 62, + COLOR_BayerGB2BGR_VNG = 63, + COLOR_BayerRG2BGR_VNG = 64, + COLOR_BayerGR2BGR_VNG = 65, + + COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG, + COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG, + COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG, + COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG, + + //! Edge-Aware Demosaicing + COLOR_BayerBG2BGR_EA = 135, + COLOR_BayerGB2BGR_EA = 136, + COLOR_BayerRG2BGR_EA = 137, + COLOR_BayerGR2BGR_EA = 138, + + COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA, + COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA, + COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA, + COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA, + + + COLOR_COLORCVT_MAX = 139 +}; + +/** types of intersection between rectangles +@ingroup imgproc_shape +*/ +enum RectanglesIntersectTypes { + INTERSECT_NONE = 0, //!< No intersection + INTERSECT_PARTIAL = 1, //!< There is a partial intersection + INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other +}; + +//! finds arbitrary template in the grayscale image using Generalized Hough Transform +class CV_EXPORTS GeneralizedHough : public Algorithm +{ +public: + //! set template to search + virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0; + virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0; + + //! find template on image + virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0; + virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0; + + //! Canny low threshold. + virtual void setCannyLowThresh(int cannyLowThresh) = 0; + virtual int getCannyLowThresh() const = 0; + + //! Canny high threshold. + virtual void setCannyHighThresh(int cannyHighThresh) = 0; + virtual int getCannyHighThresh() const = 0; + + //! Minimum distance between the centers of the detected objects. + virtual void setMinDist(double minDist) = 0; + virtual double getMinDist() const = 0; + + //! Inverse ratio of the accumulator resolution to the image resolution. + virtual void setDp(double dp) = 0; + virtual double getDp() const = 0; + + //! Maximal size of inner buffers. + virtual void setMaxBufferSize(int maxBufferSize) = 0; + virtual int getMaxBufferSize() const = 0; +}; + +//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122. +//! Detects position only without traslation and rotation +class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough +{ +public: + //! R-Table levels. + virtual void setLevels(int levels) = 0; + virtual int getLevels() const = 0; + + //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected. + virtual void setVotesThreshold(int votesThreshold) = 0; + virtual int getVotesThreshold() const = 0; +}; + +//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038. +//! Detects position, traslation and rotation +class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough +{ +public: + //! Angle difference in degrees between two points in feature. + virtual void setXi(double xi) = 0; + virtual double getXi() const = 0; + + //! Feature table levels. + virtual void setLevels(int levels) = 0; + virtual int getLevels() const = 0; + + //! Maximal difference between angles that treated as equal. + virtual void setAngleEpsilon(double angleEpsilon) = 0; + virtual double getAngleEpsilon() const = 0; + + //! Minimal rotation angle to detect in degrees. + virtual void setMinAngle(double minAngle) = 0; + virtual double getMinAngle() const = 0; + + //! Maximal rotation angle to detect in degrees. + virtual void setMaxAngle(double maxAngle) = 0; + virtual double getMaxAngle() const = 0; + + //! Angle step in degrees. + virtual void setAngleStep(double angleStep) = 0; + virtual double getAngleStep() const = 0; + + //! Angle votes threshold. + virtual void setAngleThresh(int angleThresh) = 0; + virtual int getAngleThresh() const = 0; + + //! Minimal scale to detect. + virtual void setMinScale(double minScale) = 0; + virtual double getMinScale() const = 0; + + //! Maximal scale to detect. + virtual void setMaxScale(double maxScale) = 0; + virtual double getMaxScale() const = 0; + + //! Scale step. + virtual void setScaleStep(double scaleStep) = 0; + virtual double getScaleStep() const = 0; + + //! Scale votes threshold. + virtual void setScaleThresh(int scaleThresh) = 0; + virtual int getScaleThresh() const = 0; + + //! Position votes threshold. + virtual void setPosThresh(int posThresh) = 0; + virtual int getPosThresh() const = 0; +}; + + +class CV_EXPORTS_W CLAHE : public Algorithm +{ +public: + CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0; + + CV_WRAP virtual void setClipLimit(double clipLimit) = 0; + CV_WRAP virtual double getClipLimit() const = 0; + + CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0; + CV_WRAP virtual Size getTilesGridSize() const = 0; + + CV_WRAP virtual void collectGarbage() = 0; +}; + + +//! @addtogroup imgproc_subdiv2d +//! @{ + +class CV_EXPORTS_W Subdiv2D +{ +public: + /** Subdiv2D point location cases */ + enum { PTLOC_ERROR = -2, //!< Point location error + PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect + PTLOC_INSIDE = 0, //!< Point inside some facet + PTLOC_VERTEX = 1, //!< Point coincides with one of the subdivision vertices + PTLOC_ON_EDGE = 2 //!< Point on some edge + }; + + /** Subdiv2D edge type navigation (see: getEdge()) */ + enum { NEXT_AROUND_ORG = 0x00, + NEXT_AROUND_DST = 0x22, + PREV_AROUND_ORG = 0x11, + PREV_AROUND_DST = 0x33, + NEXT_AROUND_LEFT = 0x13, + NEXT_AROUND_RIGHT = 0x31, + PREV_AROUND_LEFT = 0x20, + PREV_AROUND_RIGHT = 0x02 + }; + + /** creates an empty Subdiv2D object. + To create a new empty Delaunay subdivision you need to use the initDelaunay() function. + */ + CV_WRAP Subdiv2D(); + + /** @overload + + @param rect – Rectangle that includes all of the 2D points that are to be added to the subdivision. + + The function creates an empty Delaunay subdivision where 2D points can be added using the function + insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime + error is raised. + */ + CV_WRAP Subdiv2D(Rect rect); + + /** @brief Creates a new empty Delaunay subdivision + + @param rect – Rectangle that includes all of the 2D points that are to be added to the subdivision. + + */ + CV_WRAP void initDelaunay(Rect rect); + + /** @brief Insert a single point into a Delaunay triangulation. + + @param pt – Point to insert. + + The function inserts a single point into a subdivision and modifies the subdivision topology + appropriately. If a point with the same coordinates exists already, no new point is added. + @returns the ID of the point. + + @note If the point is outside of the triangulation specified rect a runtime error is raised. + */ + CV_WRAP int insert(Point2f pt); + + /** @brief Insert multiple points into a Delaunay triangulation. + + @param ptvec – Points to insert. + + The function inserts a vector of points into a subdivision and modifies the subdivision topology + appropriately. + */ + CV_WRAP void insert(const std::vector& ptvec); + + /** @brief Returns the location of a point within a Delaunay triangulation. + + @param pt – Point to locate. + @param edge – Output edge that the point belongs to or is located to the right of it. + @param vertex – Optional output vertex the input point coincides with. + + The function locates the input point within the subdivision and gives one of the triangle edges + or vertices. + + @returns an integer which specify one of the following five cases for point location: + - The point falls into some facet. The function returns PTLOC_INSIDE and edge will contain one of + edges of the facet. + - The point falls onto the edge. The function returns PTLOC_ON_EDGE and edge will contain this edge. + - The point coincides with one of the subdivision vertices. The function returns PTLOC_VERTEX and + vertex will contain a pointer to the vertex. + - The point is outside the subdivision reference rectangle. The function returns PTLOC_OUTSIDE_RECT + and no pointers are filled. + - One of input arguments is invalid. A runtime error is raised or, if silent or “parent†error + processing mode is selected, CV_PTLOC_ERROR is returnd. + */ + CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex); + + /** @brief Finds the subdivision vertex closest to the given point. + + @param pt – Input point. + @param nearestPt – Output subdivision vertex point. + + The function is another function that locates the input point within the subdivision. It finds the + subdivision vertex that is the closest to the input point. It is not necessarily one of vertices + of the facet containing the input point, though the facet (located using locate() ) is used as a + starting point. + + @returns vertex ID. + */ + CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0); + + /** @brief Returns a list of all edges. + + @param edgeList – Output vector. + + The function gives each edge as a 4 numbers vector, where each two are one of the edge + vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3]. + */ + CV_WRAP void getEdgeList(CV_OUT std::vector& edgeList) const; + + /** @brief Returns a list of the leading edge ID connected to each triangle. + + @param leadingEdgeList – Output vector. + + The function gives one edge ID for each triangle. + */ + CV_WRAP void getLeadingEdgeList(CV_OUT std::vector& leadingEdgeList) const; + + /** @brief Returns a list of all triangles. + + @param triangleList – Output vector. + + The function gives each triangle as a 6 numbers vector, where each two are one of the triangle + vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5]. + */ + CV_WRAP void getTriangleList(CV_OUT std::vector& triangleList) const; + + /** @brief Returns a list of all Voroni facets. + + @param idx – Vector of vertices IDs to consider. For all vertices you can pass empty vector. + @param facetList – Output vector of the Voroni facets. + @param facetCenters – Output vector of the Voroni facets center points. + + */ + CV_WRAP void getVoronoiFacetList(const std::vector& idx, CV_OUT std::vector >& facetList, + CV_OUT std::vector& facetCenters); + + /** @brief Returns vertex location from vertex ID. + + @param vertex – vertex ID. + @param firstEdge – Optional. The first edge ID which is connected to the vertex. + @returns vertex (x,y) + + */ + CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const; + + /** @brief Returns one of the edges related to the given edge. + + @param edge – Subdivision edge ID. + @param nextEdgeType - Parameter specifying which of the related edges to return. + The following values are possible: + - NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge) + - NEXT_AROUND_DST next around the edge vertex ( eDnext ) + - PREV_AROUND_ORG previous around the edge origin (reversed eRnext ) + - PREV_AROUND_DST previous around the edge destination (reversed eLnext ) + - NEXT_AROUND_LEFT next around the left facet ( eLnext ) + - NEXT_AROUND_RIGHT next around the right facet ( eRnext ) + - PREV_AROUND_LEFT previous around the left facet (reversed eOnext ) + - PREV_AROUND_RIGHT previous around the right facet (reversed eDnext ) + + ![sample output](pics/quadedge.png) + + @returns edge ID related to the input edge. + */ + CV_WRAP int getEdge( int edge, int nextEdgeType ) const; + + /** @brief Returns next edge around the edge origin. + + @param edge – Subdivision edge ID. + + @returns an integer which is next edge ID around the edge origin: eOnext on the + picture above if e is the input edge). + */ + CV_WRAP int nextEdge(int edge) const; + + /** @brief Returns another edge of the same quad-edge. + + @param edge – Subdivision edge ID. + @param rotate - Parameter specifying which of the edges of the same quad-edge as the input + one to return. The following values are possible: + - 0 - the input edge ( e on the picture below if e is the input edge) + - 1 - the rotated edge ( eRot ) + - 2 - the reversed edge (reversed e (in green)) + - 3 - the reversed rotated edge (reversed eRot (in green)) + + @returns one of the edges ID of the same quad-edge as the input edge. + */ + CV_WRAP int rotateEdge(int edge, int rotate) const; + CV_WRAP int symEdge(int edge) const; + + /** @brief Returns the edge origin. + + @param edge – Subdivision edge ID. + @param orgpt – Output vertex location. + + @returns vertex ID. + */ + CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const; + + /** @brief Returns the edge destination. + + @param edge – Subdivision edge ID. + @param dstpt – Output vertex location. + + @returns vertex ID. + */ + CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const; + +protected: + int newEdge(); + void deleteEdge(int edge); + int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0); + void deletePoint(int vtx); + void setEdgePoints( int edge, int orgPt, int dstPt ); + void splice( int edgeA, int edgeB ); + int connectEdges( int edgeA, int edgeB ); + void swapEdges( int edge ); + int isRightOf(Point2f pt, int edge) const; + void calcVoronoi(); + void clearVoronoi(); + void checkSubdiv() const; + + struct CV_EXPORTS Vertex + { + Vertex(); + Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0); + bool isvirtual() const; + bool isfree() const; + + int firstEdge; + int type; + Point2f pt; + }; + + struct CV_EXPORTS QuadEdge + { + QuadEdge(); + QuadEdge(int edgeidx); + bool isfree() const; + + int next[4]; + int pt[4]; + }; + + //! All of the vertices + std::vector vtx; + //! All of the edges + std::vector qedges; + int freeQEdge; + int freePoint; + bool validGeometry; + + int recentEdge; + //! Top left corner of the bounding rect + Point2f topLeft; + //! Bottom right corner of the bounding rect + Point2f bottomRight; +}; + +//! @} imgproc_subdiv2d + +//! @addtogroup imgproc_feature +//! @{ + +/** @example lsd_lines.cpp +An example using the LineSegmentDetector +*/ + +/** @brief Line segment detector class + +following the algorithm described at @cite Rafael12 . +*/ +class CV_EXPORTS_W LineSegmentDetector : public Algorithm +{ +public: + + /** @brief Finds lines in the input image. + + This is the output of the default parameters of the algorithm on the above shown image. + + ![image](pics/building_lsd.png) + + @param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: + `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` + @param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where + Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly + oriented depending on the gradient. + @param width Vector of widths of the regions, where the lines are found. E.g. Width of line. + @param prec Vector of precisions with which the lines are found. + @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The + bigger the value, logarithmically better the detection. + - -1 corresponds to 10 mean false alarms + - 0 corresponds to 1 mean false alarm + - 1 corresponds to 0.1 mean false alarms + This vector will be calculated only when the objects type is LSD_REFINE_ADV. + */ + CV_WRAP virtual void detect(InputArray _image, OutputArray _lines, + OutputArray width = noArray(), OutputArray prec = noArray(), + OutputArray nfa = noArray()) = 0; + + /** @brief Draws the line segments on a given image. + @param _image The image, where the liens will be drawn. Should be bigger or equal to the image, + where the lines were found. + @param lines A vector of the lines that needed to be drawn. + */ + CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0; + + /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. + + @param size The size of the image, where lines1 and lines2 were found. + @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color. + @param lines2 The second group of lines. They visualized in red color. + @param _image Optional image, where the lines will be drawn. The image should be color(3-channel) + in order for lines1 and lines2 to be drawn in the above mentioned colors. + */ + CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0; + + virtual ~LineSegmentDetector() { } +}; + +/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it. + +The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want +to edit those, as to tailor it for their own application. + +@param _refine The way found lines will be refined, see cv::LineSegmentDetectorModes +@param _scale The scale of the image that will be used to find the lines. Range (0..1]. +@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale. +@param _quant Bound to the quantization error on the gradient norm. +@param _ang_th Gradient angle tolerance in degrees. +@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advancent refinement +is chosen. +@param _density_th Minimal density of aligned region points in the enclosing rectangle. +@param _n_bins Number of bins in pseudo-ordering of gradient modulus. + */ +CV_EXPORTS_W Ptr createLineSegmentDetector( + int _refine = LSD_REFINE_STD, double _scale = 0.8, + double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, + double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024); + +//! @} imgproc_feature + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Returns Gaussian filter coefficients. + +The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter +coefficients: + +\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f] + +where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$. + +Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize +smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. +You may also use the higher-level GaussianBlur. +@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive. +@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as +`sigma = 0.3\*((ksize-1)\*0.5 - 1) + 0.8`. +@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . +@sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur + */ +CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F ); + +/** @brief Returns filter coefficients for computing spatial image derivatives. + +The function computes and returns the filter coefficients for spatial image derivatives. When +`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see cv::Scharr). Otherwise, Sobel +kernels are generated (see cv::Sobel). The filters are normally passed to sepFilter2D or to + +@param kx Output matrix of row filter coefficients. It has the type ktype . +@param ky Output matrix of column filter coefficients. It has the type ktype . +@param dx Derivative order in respect of x. +@param dy Derivative order in respect of y. +@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7. +@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. +Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are +going to filter floating-point images, you are likely to use the normalized kernels. But if you +compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve +all the fractional bits, you may want to set normalize=false . +@param ktype Type of filter coefficients. It can be CV_32f or CV_64F . + */ +CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky, + int dx, int dy, int ksize, + bool normalize = false, int ktype = CV_32F ); + +/** @brief Returns Gabor filter coefficients. + +For more details about gabor filter equations and parameters, see: [Gabor +Filter](http://en.wikipedia.org/wiki/Gabor_filter). + +@param ksize Size of the filter returned. +@param sigma Standard deviation of the gaussian envelope. +@param theta Orientation of the normal to the parallel stripes of a Gabor function. +@param lambd Wavelength of the sinusoidal factor. +@param gamma Spatial aspect ratio. +@param psi Phase offset. +@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . + */ +CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd, + double gamma, double psi = CV_PI*0.5, int ktype = CV_64F ); + +//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. +static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); } + +/** @brief Returns a structuring element of the specified size and shape for morphological operations. + +The function constructs and returns the structuring element that can be further passed to cv::erode, +cv::dilate or cv::morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as +the structuring element. + +@param shape Element shape that could be one of cv::MorphShapes +@param ksize Size of the structuring element. +@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the +anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor +position. In other cases the anchor just regulates how much the result of the morphological +operation is shifted. + */ +CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1)); + +/** @brief Blurs an image using the median filter. + +The function smoothes an image using the median filter with the \f$\texttt{ksize} \times +\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently. +In-place operation is supported. + +@note The median filter uses BORDER_REPLICATE internally to cope with border pixels, see cv::BorderTypes + +@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be +CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. +@param dst destination array of the same size and type as src. +@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... +@sa bilateralFilter, blur, boxFilter, GaussianBlur + */ +CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize ); + +/** @brief Blurs an image using a Gaussian filter. + +The function convolves the source image with the specified Gaussian kernel. In-place filtering is +supported. + +@param src input image; the image can have any number of channels, which are processed +independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be +positive and odd. Or, they can be zero's and then they are computed from sigma. +@param sigmaX Gaussian kernel standard deviation in X direction. +@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be +equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, +respectively (see cv::getGaussianKernel for details); to fully control the result regardless of +possible future modifications of all this semantics, it is recommended to specify all of ksize, +sigmaX, and sigmaY. +@param borderType pixel extrapolation method, see cv::BorderTypes + +@sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur + */ +CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize, + double sigmaX, double sigmaY = 0, + int borderType = BORDER_DEFAULT ); + +/** @brief Applies the bilateral filter to an image. + +The function applies bilateral filtering to the input image, as described in +http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html +bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is +very slow compared to most filters. + +_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< +10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very +strong effect, making the image look "cartoonish". + +_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time +applications, and perhaps d=9 for offline applications that need heavy noise filtering. + +This filter does not work inplace. +@param src Source 8-bit or floating-point, 1-channel or 3-channel image. +@param dst Destination image of the same size and type as src . +@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, +it is computed from sigmaSpace. +@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that +farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting +in larger areas of semi-equal color. +@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that +farther pixels will influence each other as long as their colors are close enough (see sigmaColor +). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is +proportional to sigmaSpace. +@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes + */ +CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d, + double sigmaColor, double sigmaSpace, + int borderType = BORDER_DEFAULT ); + +/** @brief Blurs an image using the box filter. + +The function smoothes an image using the kernel: + +\f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f] + +where + +\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f] + +Unnormalized box filter is useful for computing various integral characteristics over each pixel +neighborhood, such as covariance matrices of image derivatives (used in dense optical flow +algorithms, and so on). If you need to compute pixel sums over variable-size windows, use cv::integral. + +@param src input image. +@param dst output image of the same size and type as src. +@param ddepth the output image depth (-1 to use src.depth()). +@param ksize blurring kernel size. +@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel +center. +@param normalize flag, specifying whether the kernel is normalized by its area or not. +@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes +@sa blur, bilateralFilter, GaussianBlur, medianBlur, integral + */ +CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth, + Size ksize, Point anchor = Point(-1,-1), + bool normalize = true, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter. + +For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring +pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$. + +The unnormalized square box filter can be useful in computing local image statistics such as the the local +variance and standard deviation around the neighborhood of a pixel. + +@param _src input image +@param _dst output image of the same size and type as _src +@param ddepth the output image depth (-1 to use src.depth()) +@param ksize kernel size +@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel +center. +@param normalize flag, specifying whether the kernel is to be normalized by it's area or not. +@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes +@sa boxFilter +*/ +CV_EXPORTS_W void sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth, + Size ksize, Point anchor = Point(-1, -1), + bool normalize = true, + int borderType = BORDER_DEFAULT ); + +/** @brief Blurs an image using the normalized box filter. + +The function smoothes an image using the kernel: + +\f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f] + +The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), +anchor, true, borderType)`. + +@param src input image; it can have any number of channels, which are processed independently, but +the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param ksize blurring kernel size. +@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel +center. +@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes +@sa boxFilter, bilateralFilter, GaussianBlur, medianBlur + */ +CV_EXPORTS_W void blur( InputArray src, OutputArray dst, + Size ksize, Point anchor = Point(-1,-1), + int borderType = BORDER_DEFAULT ); + +/** @brief Convolves an image with the kernel. + +The function applies an arbitrary linear filter to an image. In-place operation is supported. When +the aperture is partially outside the image, the function interpolates outlier pixel values +according to the specified border mode. + +The function does actually compute correlation, not the convolution: + +\f[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] + +That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip +the kernel using cv::flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - +anchor.y - 1)`. + +The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or +larger) and the direct algorithm for small kernels. + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param ddepth desired depth of the destination image, see @ref filter_depths "combinations" +@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point +matrix; if you want to apply different kernels to different channels, split the image into +separate color planes using split and process them individually. +@param anchor anchor of the kernel that indicates the relative position of a filtered point within +the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor +is at the kernel center. +@param delta optional value added to the filtered pixels before storing them in dst. +@param borderType pixel extrapolation method, see cv::BorderTypes +@sa sepFilter2D, dft, matchTemplate + */ +CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth, + InputArray kernel, Point anchor = Point(-1,-1), + double delta = 0, int borderType = BORDER_DEFAULT ); + +/** @brief Applies a separable linear filter to an image. + +The function applies a separable linear filter to the image. That is, first, every row of src is +filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D +kernel kernelY. The final result shifted by delta is stored in dst . + +@param src Source image. +@param dst Destination image of the same size and the same number of channels as src . +@param ddepth Destination image depth, see @ref filter_depths "combinations" +@param kernelX Coefficients for filtering each row. +@param kernelY Coefficients for filtering each column. +@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor +is at the kernel center. +@param delta Value added to the filtered results before storing them. +@param borderType Pixel extrapolation method, see cv::BorderTypes +@sa filter2D, Sobel, GaussianBlur, boxFilter, blur + */ +CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth, + InputArray kernelX, InputArray kernelY, + Point anchor = Point(-1,-1), + double delta = 0, int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. + +In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to +calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$ +kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first +or the second x- or y- derivatives. + +There is also the special value `ksize = CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr +filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is + +\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f] + +for the x-derivative, or transposed for the y-derivative. + +The function calculates an image derivative by convolving the image with the appropriate kernel: + +\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f] + +The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less +resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) +or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first +case corresponds to a kernel of: + +\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f] + +The second case corresponds to a kernel of: + +\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f] + +@param src input image. +@param dst output image of the same size and the same number of channels as src . +@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of + 8-bit input images it will result in truncated derivatives. +@param dx order of the derivative x. +@param dy order of the derivative y. +@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. +@param scale optional scale factor for the computed derivative values; by default, no scaling is +applied (see cv::getDerivKernels for details). +@param delta optional delta value that is added to the results prior to storing them in dst. +@param borderType pixel extrapolation method, see cv::BorderTypes +@sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar + */ +CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth, + int dx, int dy, int ksize = 3, + double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first order image derivative in both x and y using a Sobel operator + +Equivalent to calling: + +@code +Sobel( src, dx, CV_16SC1, 1, 0, 3 ); +Sobel( src, dy, CV_16SC1, 0, 1, 3 ); +@endcode + +@param src input image. +@param dx output image with first-order derivative in x. +@param dy output image with first-order derivative in y. +@param ksize size of Sobel kernel. It must be 3. +@param borderType pixel extrapolation method, see cv::BorderTypes + +@sa Sobel + */ + +CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx, + OutputArray dy, int ksize = 3, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first x- or y- image derivative using Scharr operator. + +The function computes the first x- or y- spatial image derivative using the Scharr operator. The +call + +\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f] + +is equivalent to + +\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV\_SCHARR, scale, delta, borderType)} .\f] + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param ddepth output image depth, see @ref filter_depths "combinations" +@param dx order of the derivative x. +@param dy order of the derivative y. +@param scale optional scale factor for the computed derivative values; by default, no scaling is +applied (see getDerivKernels for details). +@param delta optional delta value that is added to the results prior to storing them in dst. +@param borderType pixel extrapolation method, see cv::BorderTypes +@sa cartToPolar + */ +CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth, + int dx, int dy, double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +/** @example laplace.cpp + An example using Laplace transformations for edge detection +*/ + +/** @brief Calculates the Laplacian of an image. + +The function calculates the Laplacian of the source image by adding up the second x and y +derivatives calculated using the Sobel operator: + +\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f] + +This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image +with the following \f$3 \times 3\f$ aperture: + +\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f] + +@param src Source image. +@param dst Destination image of the same size and the same number of channels as src . +@param ddepth Desired depth of the destination image. +@param ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for +details. The size must be positive and odd. +@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is +applied. See getDerivKernels for details. +@param delta Optional delta value that is added to the results prior to storing them in dst . +@param borderType Pixel extrapolation method, see cv::BorderTypes +@sa Sobel, Scharr + */ +CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth, + int ksize = 1, double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_feature +//! @{ + +/** @example edge.cpp + An example on using the canny edge detector +*/ + +/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 . + +The function finds edges in the input image image and marks them in the output map edges using the +Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The +largest value is used to find initial segments of strong edges. See + + +@param image 8-bit input image. +@param edges output edge map; single channels 8-bit image, which has the same size as image . +@param threshold1 first threshold for the hysteresis procedure. +@param threshold2 second threshold for the hysteresis procedure. +@param apertureSize aperture size for the Sobel operator. +@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm +\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( +L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( +L2gradient=false ). + */ +CV_EXPORTS_W void Canny( InputArray image, OutputArray edges, + double threshold1, double threshold2, + int apertureSize = 3, bool L2gradient = false ); + +/** \overload + +Finds edges in an image using the Canny algorithm with custom image gradient. + +@param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). +@param dy 16-bit y derivative of input image (same type as dx). +@param edges,threshold1,threshold2,L2gradient See cv::Canny + */ +CV_EXPORTS_W void Canny( InputArray dx, InputArray dy, + OutputArray edges, + double threshold1, double threshold2, + bool L2gradient = false ); + +/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection. + +The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal +eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms +of the formulae in the cornerEigenValsAndVecs description. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as +src . +@param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). +@param ksize Aperture parameter for the Sobel operator. +@param borderType Pixel extrapolation method. See cv::BorderTypes. + */ +CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst, + int blockSize, int ksize = 3, + int borderType = BORDER_DEFAULT ); + +/** @brief Harris corner detector. + +The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and +cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance +matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it +computes the following characteristic: + +\f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f] + +Corners in the image can be found as the local maxima of this response map. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same +size as src . +@param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). +@param ksize Aperture parameter for the Sobel operator. +@param k Harris detector free parameter. See the formula below. +@param borderType Pixel extrapolation method. See cv::BorderTypes. + */ +CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize, + int ksize, double k, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection. + +For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize +neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as: + +\f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f] + +where the derivatives are computed using the Sobel operator. + +After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as +\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where + +- \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$ +- \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$ +- \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$ + +The output of the function can be used for robust edge or corner detection. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) . +@param blockSize Neighborhood size (see details below). +@param ksize Aperture parameter for the Sobel operator. +@param borderType Pixel extrapolation method. See cv::BorderTypes. + +@sa cornerMinEigenVal, cornerHarris, preCornerDetect + */ +CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst, + int blockSize, int ksize, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates a feature map for corner detection. + +The function calculates the complex spatial derivative-based function of the source image + +\f[\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\f] + +where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image +derivatives, and \f$D_{xy}\f$ is the mixed derivative. + +The corners can be found as local maximums of the functions, as shown below: +@code + Mat corners, dilated_corners; + preCornerDetect(image, corners, 3); + // dilation with 3x3 rectangular structuring element + dilate(corners, dilated_corners, Mat(), 1); + Mat corner_mask = corners == dilated_corners; +@endcode + +@param src Source single-channel 8-bit of floating-point image. +@param dst Output image that has the type CV_32F and the same size as src . +@param ksize %Aperture size of the Sobel . +@param borderType Pixel extrapolation method. See cv::BorderTypes. + */ +CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize, + int borderType = BORDER_DEFAULT ); + +/** @brief Refines the corner locations. + +The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as +shown on the figure below. + +![image](pics/cornersubpix.png) + +Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$ +to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$ +subject to image and measurement noise. Consider the expression: + +\f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f] + +where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The +value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up +with \f$\epsilon_i\f$ set to zero: + +\f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f] + +where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first +gradient term \f$G\f$ and the second gradient term \f$b\f$ gives: + +\f[q = G^{-1} \cdot b\f] + +The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates +until the center stays within a set threshold. + +@param image Input image. +@param corners Initial coordinates of the input corners and refined coordinates provided for +output. +@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) , +then a \f$5*2+1 \times 5*2+1 = 11 \times 11\f$ search window is used. +@param zeroZone Half of the size of the dead region in the middle of the search zone over which +the summation in the formula below is not done. It is used sometimes to avoid possible +singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such +a size. +@param criteria Criteria for termination of the iterative process of corner refinement. That is, +the process of corner position refinement stops either after criteria.maxCount iterations or when +the corner position moves by less than criteria.epsilon on some iteration. + */ +CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners, + Size winSize, Size zeroZone, + TermCriteria criteria ); + +/** @brief Determines strong corners on an image. + +The function finds the most prominent corners in the image or in the specified image region, as +described in @cite Shi94 + +- Function calculates the corner quality measure at every source image pixel using the + cornerMinEigenVal or cornerHarris . +- Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are + retained). +- The corners with the minimal eigenvalue less than + \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected. +- The remaining corners are sorted by the quality measure in the descending order. +- Function throws away each corner for which there is a stronger corner at a distance less than + maxDistance. + +The function can be used to initialize a point-based tracker of an object. + +@note If the function is called with different values A and B of the parameter qualityLevel , and +A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector +with qualityLevel=B . + +@param image Input 8-bit or floating-point 32-bit, single-channel image. +@param corners Output vector of detected corners. +@param maxCorners Maximum number of corners to return. If there are more corners than are found, +the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set +and all detected corners are returned. +@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The +parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue +(see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the +quality measure less than the product are rejected. For example, if the best corner has the +quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure +less than 15 are rejected. +@param minDistance Minimum possible Euclidean distance between the returned corners. +@param mask Optional region of interest. If the image is not empty (it needs to have the type +CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. +@param blockSize Size of an average block for computing a derivative covariation matrix over each +pixel neighborhood. See cornerEigenValsAndVecs . +@param useHarrisDetector Parameter indicating whether to use a Harris detector (see cornerHarris) +or cornerMinEigenVal. +@param k Free parameter of the Harris detector. + +@sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, + */ +CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, + int maxCorners, double qualityLevel, double minDistance, + InputArray mask = noArray(), int blockSize = 3, + bool useHarrisDetector = false, double k = 0.04 ); + +/** @example houghlines.cpp +An example using the Hough line detector +*/ + +/** @brief Finds lines in a binary image using the standard Hough transform. + +The function implements the standard or standard multi-scale Hough transform algorithm for line +detection. See for a good explanation of Hough +transform. + +@param image 8-bit, single-channel binary source image. The image may be modified by the function. +@param lines Output vector of lines. Each line is represented by a two-element vector +\f$(\rho, \theta)\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of +the image). \f$\theta\f$ is the line rotation angle in radians ( +\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ). +@param rho Distance resolution of the accumulator in pixels. +@param theta Angle resolution of the accumulator in radians. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ). +@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . +The coarse accumulator distance resolution is rho and the accurate accumulator resolution is +rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these +parameters should be positive. +@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. +@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines. +Must fall between 0 and max_theta. +@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines. +Must fall between min_theta and CV_PI. + */ +CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines, + double rho, double theta, int threshold, + double srn = 0, double stn = 0, + double min_theta = 0, double max_theta = CV_PI ); + +/** @brief Finds line segments in a binary image using the probabilistic Hough transform. + +The function implements the probabilistic Hough transform algorithm for line detection, described +in @cite Matas00 + +See the line detection example below: + +@code + #include + #include + + using namespace cv; + using namespace std; + + int main(int argc, char** argv) + { + Mat src, dst, color_dst; + if( argc != 2 || !(src=imread(argv[1], 0)).data) + return -1; + + Canny( src, dst, 50, 200, 3 ); + cvtColor( dst, color_dst, COLOR_GRAY2BGR ); + + #if 0 + vector lines; + HoughLines( dst, lines, 1, CV_PI/180, 100 ); + + for( size_t i = 0; i < lines.size(); i++ ) + { + float rho = lines[i][0]; + float theta = lines[i][1]; + double a = cos(theta), b = sin(theta); + double x0 = a*rho, y0 = b*rho; + Point pt1(cvRound(x0 + 1000*(-b)), + cvRound(y0 + 1000*(a))); + Point pt2(cvRound(x0 - 1000*(-b)), + cvRound(y0 - 1000*(a))); + line( color_dst, pt1, pt2, Scalar(0,0,255), 3, 8 ); + } + #else + vector lines; + HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 ); + for( size_t i = 0; i < lines.size(); i++ ) + { + line( color_dst, Point(lines[i][0], lines[i][1]), + Point(lines[i][2], lines[i][3]), Scalar(0,0,255), 3, 8 ); + } + #endif + namedWindow( "Source", 1 ); + imshow( "Source", src ); + + namedWindow( "Detected Lines", 1 ); + imshow( "Detected Lines", color_dst ); + + waitKey(0); + return 0; + } +@endcode +This is a sample picture the function parameters have been tuned for: + +![image](pics/building.jpg) + +And this is the output of the above program in case of the probabilistic Hough transform: + +![image](pics/houghp.png) + +@param image 8-bit, single-channel binary source image. The image may be modified by the function. +@param lines Output vector of lines. Each line is represented by a 4-element vector +\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected +line segment. +@param rho Distance resolution of the accumulator in pixels. +@param theta Angle resolution of the accumulator in radians. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ). +@param minLineLength Minimum line length. Line segments shorter than that are rejected. +@param maxLineGap Maximum allowed gap between points on the same line to link them. + +@sa LineSegmentDetector + */ +CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines, + double rho, double theta, int threshold, + double minLineLength = 0, double maxLineGap = 0 ); + +/** @example houghcircles.cpp +An example using the Hough circle detector +*/ + +/** @brief Finds circles in a grayscale image using the Hough transform. + +The function finds circles in a grayscale image using a modification of the Hough transform. + +Example: : +@code + #include + #include + #include + + using namespace cv; + using namespace std; + + int main(int argc, char** argv) + { + Mat img, gray; + if( argc != 2 || !(img=imread(argv[1], 1)).data) + return -1; + cvtColor(img, gray, COLOR_BGR2GRAY); + // smooth it, otherwise a lot of false circles may be detected + GaussianBlur( gray, gray, Size(9, 9), 2, 2 ); + vector circles; + HoughCircles(gray, circles, HOUGH_GRADIENT, + 2, gray.rows/4, 200, 100 ); + for( size_t i = 0; i < circles.size(); i++ ) + { + Point center(cvRound(circles[i][0]), cvRound(circles[i][1])); + int radius = cvRound(circles[i][2]); + // draw the circle center + circle( img, center, 3, Scalar(0,255,0), -1, 8, 0 ); + // draw the circle outline + circle( img, center, radius, Scalar(0,0,255), 3, 8, 0 ); + } + namedWindow( "circles", 1 ); + imshow( "circles", img ); + + waitKey(0); + return 0; + } +@endcode + +@note Usually the function detects the centers of circles well. However, it may fail to find correct +radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if +you know it. Or, you may ignore the returned radius, use only the center, and find the correct +radius using an additional procedure. + +@param image 8-bit, single-channel, grayscale input image. +@param circles Output vector of found circles. Each vector is encoded as a 3-element +floating-point vector \f$(x, y, radius)\f$ . +@param method Detection method, see cv::HoughModes. Currently, the only implemented method is HOUGH_GRADIENT +@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if +dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has +half as big width and height. +@param minDist Minimum distance between the centers of the detected circles. If the parameter is +too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is +too large, some circles may be missed. +@param param1 First method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the higher +threshold of the two passed to the Canny edge detector (the lower one is twice smaller). +@param param2 Second method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the +accumulator threshold for the circle centers at the detection stage. The smaller it is, the more +false circles may be detected. Circles, corresponding to the larger accumulator values, will be +returned first. +@param minRadius Minimum circle radius. +@param maxRadius Maximum circle radius. + +@sa fitEllipse, minEnclosingCircle + */ +CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles, + int method, double dp, double minDist, + double param1 = 100, double param2 = 100, + int minRadius = 0, int maxRadius = 0 ); + +//! @} imgproc_feature + +//! @addtogroup imgproc_filter +//! @{ + +/** @example morphology2.cpp + An example using the morphological operations +*/ + +/** @brief Erodes an image by using a specific structuring element. + +The function erodes the source image using the specified structuring element that determines the +shape of a pixel neighborhood over which the minimum is taken: + +\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] + +The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In +case of multi-channel images, each channel is processed independently. + +@param src input image; the number of channels can be arbitrary, but the depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular +structuring element is used. Kernel can be created using getStructuringElement. +@param anchor position of the anchor within the element; default value (-1, -1) means that the +anchor is at the element center. +@param iterations number of times erosion is applied. +@param borderType pixel extrapolation method, see cv::BorderTypes +@param borderValue border value in case of a constant border +@sa dilate, morphologyEx, getStructuringElement + */ +CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +/** @brief Dilates an image by using a specific structuring element. + +The function dilates the source image using the specified structuring element that determines the +shape of a pixel neighborhood over which the maximum is taken: +\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] + +The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In +case of multi-channel images, each channel is processed independently. + +@param src input image; the number of channels can be arbitrary, but the depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src\`. +@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular +structuring element is used. Kernel can be created using getStructuringElement +@param anchor position of the anchor within the element; default value (-1, -1) means that the +anchor is at the element center. +@param iterations number of times dilation is applied. +@param borderType pixel extrapolation method, see cv::BorderTypes +@param borderValue border value in case of a constant border +@sa erode, morphologyEx, getStructuringElement + */ +CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +/** @brief Performs advanced morphological transformations. + +The function morphologyEx can perform advanced morphological transformations using an erosion and dilation as +basic operations. + +Any of the operations can be done in-place. In case of multi-channel images, each channel is +processed independently. + +@param src Source image. The number of channels can be arbitrary. The depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst Destination image of the same size and type as source image. +@param op Type of a morphological operation, see cv::MorphTypes +@param kernel Structuring element. It can be created using cv::getStructuringElement. +@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the +kernel center. +@param iterations Number of times erosion and dilation are applied. +@param borderType Pixel extrapolation method, see cv::BorderTypes +@param borderValue Border value in case of a constant border. The default value has a special +meaning. +@sa dilate, erode, getStructuringElement + */ +CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst, + int op, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +/** @brief Resizes an image. + +The function resize resizes the image src down to or up to the specified size. Note that the +initial dst type or size are not taken into account. Instead, the size and type are derived from +the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, +you may call the function as follows: +@code + // explicitly specify dsize=dst.size(); fx and fy will be computed from that. + resize(src, dst, dst.size(), 0, 0, interpolation); +@endcode +If you want to decimate the image by factor of 2 in each direction, you can call the function this +way: +@code + // specify fx and fy and let the function compute the destination image size. + resize(src, dst, Size(), 0.5, 0.5, interpolation); +@endcode +To shrink an image, it will generally look best with cv::INTER_AREA interpolation, whereas to +enlarge an image, it will generally look best with cv::INTER_CUBIC (slow) or cv::INTER_LINEAR +(faster but still looks OK). + +@param src input image. +@param dst output image; it has the size dsize (when it is non-zero) or the size computed from +src.size(), fx, and fy; the type of dst is the same as of src. +@param dsize output image size; if it equals zero, it is computed as: + \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f] + Either dsize or both fx and fy must be non-zero. +@param fx scale factor along the horizontal axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.width/src.cols}\f] +@param fy scale factor along the vertical axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.height/src.rows}\f] +@param interpolation interpolation method, see cv::InterpolationFlags + +@sa warpAffine, warpPerspective, remap + */ +CV_EXPORTS_W void resize( InputArray src, OutputArray dst, + Size dsize, double fx = 0, double fy = 0, + int interpolation = INTER_LINEAR ); + +/** @brief Applies an affine transformation to an image. + +The function warpAffine transforms the source image using the specified matrix: + +\f[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\f] + +when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted +with cv::invertAffineTransform and then put in the formula above instead of M. The function cannot +operate in-place. + +@param src input image. +@param dst output image that has the size dsize and the same type as src . +@param M \f$2\times 3\f$ transformation matrix. +@param dsize size of the output image. +@param flags combination of interpolation methods (see cv::InterpolationFlags) and the optional +flag WARP_INVERSE_MAP that means that M is the inverse transformation ( +\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). +@param borderMode pixel extrapolation method (see cv::BorderTypes); when +borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to +the "outliers" in the source image are not modified by the function. +@param borderValue value used in case of a constant border; by default, it is 0. + +@sa warpPerspective, resize, remap, getRectSubPix, transform + */ +CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst, + InputArray M, Size dsize, + int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Applies a perspective transformation to an image. + +The function warpPerspective transforms the source image using the specified matrix: + +\f[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , + \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f] + +when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert +and then put in the formula above instead of M. The function cannot operate in-place. + +@param src input image. +@param dst output image that has the size dsize and the same type as src . +@param M \f$3\times 3\f$ transformation matrix. +@param dsize size of the output image. +@param flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the +optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( +\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). +@param borderMode pixel extrapolation method (BORDER_CONSTANT or BORDER_REPLICATE). +@param borderValue value used in case of a constant border; by default, it equals 0. + +@sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform + */ +CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst, + InputArray M, Size dsize, + int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Applies a generic geometrical transformation to an image. + +The function remap transforms the source image using the specified map: + +\f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f] + +where values of pixels with non-integer coordinates are computed using one of available +interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps +in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in +\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to +convert from floating to fixed-point representations of a map is that they can yield much faster +(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x), +cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients. + +This function cannot operate in-place. + +@param src Source image. +@param dst Destination image. It has the same size as map1 and the same type as src . +@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , +CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point +representation to fixed-point for speed. +@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map +if map1 is (x,y) points), respectively. +@param interpolation Interpolation method (see cv::InterpolationFlags). The method INTER_AREA is +not supported by this function. +@param borderMode Pixel extrapolation method (see cv::BorderTypes). When +borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image that +corresponds to the "outliers" in the source image are not modified by the function. +@param borderValue Value used in case of a constant border. By default, it is 0. +@note +Due to current implementaion limitations the size of an input and output images should be less than 32767x32767. + */ +CV_EXPORTS_W void remap( InputArray src, OutputArray dst, + InputArray map1, InputArray map2, + int interpolation, int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Converts image transformation maps from one representation to another. + +The function converts a pair of maps for remap from one representation to another. The following +options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are +supported: + +- \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the +most frequently used conversion operation, in which the original floating-point maps (see remap ) +are converted to a more compact and much faster fixed-point representation. The first output array +contains the rounded coordinates and the second array (created only when nninterpolation=false ) +contains indices in the interpolation tables. + +- \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but +the original maps are stored in one 2-channel matrix. + +- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same +as the originals. + +@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . +@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), +respectively. +@param dstmap1 The first output map that has the type dstmap1type and the same size as src . +@param dstmap2 The second output map. +@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or +CV_32FC2 . +@param nninterpolation Flag indicating whether the fixed-point maps are used for the +nearest-neighbor or for a more complex interpolation. + +@sa remap, undistort, initUndistortRectifyMap + */ +CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2, + OutputArray dstmap1, OutputArray dstmap2, + int dstmap1type, bool nninterpolation = false ); + +/** @brief Calculates an affine matrix of 2D rotation. + +The function calculates the following matrix: + +\f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f] + +where + +\f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f] + +The transformation maps the rotation center to itself. If this is not the target, adjust the shift. + +@param center Center of the rotation in the source image. +@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the +coordinate origin is assumed to be the top-left corner). +@param scale Isotropic scale factor. + +@sa getAffineTransform, warpAffine, transform + */ +CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale ); + +//! returns 3x3 perspective transformation for the corresponding 4 point pairs. +CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] ); + +/** @brief Calculates an affine transform from three pairs of the corresponding points. + +The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that: + +\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] + +where + +\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f] + +@param src Coordinates of triangle vertices in the source image. +@param dst Coordinates of the corresponding triangle vertices in the destination image. + +@sa warpAffine, transform + */ +CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] ); + +/** @brief Inverts an affine transformation. + +The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M: + +\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f] + +The result is also a \f$2 \times 3\f$ matrix of the same type as M. + +@param M Original affine transformation. +@param iM Output reverse affine transformation. + */ +CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM ); + +/** @brief Calculates a perspective transform from four pairs of the corresponding points. + +The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that: + +\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] + +where + +\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f] + +@param src Coordinates of quadrangle vertices in the source image. +@param dst Coordinates of the corresponding quadrangle vertices in the destination image. + +@sa findHomography, warpPerspective, perspectiveTransform + */ +CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst ); + +CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst ); + +/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy. + +The function getRectSubPix extracts pixels from src: + +\f[dst(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] + +where the values of the pixels at non-integer coordinates are retrieved using bilinear +interpolation. Every channel of multi-channel images is processed independently. While the center of +the rectangle must be inside the image, parts of the rectangle may be outside. In this case, the +replication border mode (see cv::BorderTypes) is used to extrapolate the pixel values outside of +the image. + +@param image Source image. +@param patchSize Size of the extracted patch. +@param center Floating point coordinates of the center of the extracted rectangle within the +source image. The center must be inside the image. +@param patch Extracted patch that has the size patchSize and the same number of channels as src . +@param patchType Depth of the extracted pixels. By default, they have the same depth as src . + +@sa warpAffine, warpPerspective + */ +CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize, + Point2f center, OutputArray patch, int patchType = -1 ); + +/** @example polar_transforms.cpp +An example using the cv::linearPolar and cv::logPolar operations +*/ + +/** @brief Remaps an image to semilog-polar coordinates space. + +Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image"): +\f[\begin{array}{l} + dst( \rho , \phi ) = src(x,y) \\ + dst.size() \leftarrow src.size() +\end{array}\f] + +where +\f[\begin{array}{l} + I = (dx,dy) = (x - center.x,y - center.y) \\ + \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ + \phi = Ky \cdot \texttt{angle} (I)_{0..360 deg} \\ +\end{array}\f] + +and +\f[\begin{array}{l} + M = src.cols / log_e(maxRadius) \\ + Ky = src.rows / 360 \\ +\end{array}\f] + +The function emulates the human "foveal" vision and can be used for fast scale and +rotation-invariant template matching, for object tracking and so forth. +@param src Source image +@param dst Destination image. It will have same size and type as src. +@param center The transformation center; where the output precision is maximal +@param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too. +@param flags A combination of interpolation methods, see cv::InterpolationFlags + +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees @ref cv::cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. +*/ +CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst, + Point2f center, double M, int flags ); + +/** @brief Remaps an image to polar coordinates space. + +@anchor polar_remaps_reference_image +![Polar remaps reference](pics/polar_remap_doc.png) + +Transform the source image using the following transformation: +\f[\begin{array}{l} + dst( \rho , \phi ) = src(x,y) \\ + dst.size() \leftarrow src.size() +\end{array}\f] + +where +\f[\begin{array}{l} + I = (dx,dy) = (x - center.x,y - center.y) \\ + \rho = Kx \cdot \texttt{magnitude} (I) ,\\ + \phi = Ky \cdot \texttt{angle} (I)_{0..360 deg} +\end{array}\f] + +and +\f[\begin{array}{l} + Kx = src.cols / maxRadius \\ + Ky = src.rows / 360 +\end{array}\f] + + +@param src Source image +@param dst Destination image. It will have same size and type as src. +@param center The transformation center; +@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. +@param flags A combination of interpolation methods, see cv::InterpolationFlags + +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees @ref cv::cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + +*/ +CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst, + Point2f center, double maxRadius, int flags ); + +//! @} imgproc_transform + +//! @addtogroup imgproc_misc +//! @{ + +/** @overload */ +CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 ); + +/** @overload */ +CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum, + OutputArray sqsum, int sdepth = -1, int sqdepth = -1 ); + +/** @brief Calculates the integral of an image. + +The function calculates one or more integral images for the source image as follows: + +\f[\texttt{sum} (X,Y) = \sum _{x + +Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed +with getOptimalDFTSize. + +The function performs the following equations: +- First it applies a Hanning window (see ) to each +image to remove possible edge effects. This window is cached until the array size changes to speed +up processing time. +- Next it computes the forward DFTs of each source array: +\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f] +where \f$\mathcal{F}\f$ is the forward DFT. +- It then computes the cross-power spectrum of each frequency domain array: +\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f] +- Next the cross-correlation is converted back into the time domain via the inverse DFT: +\f[r = \mathcal{F}^{-1}\{R\}\f] +- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to +achieve sub-pixel accuracy. +\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f] +- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 +centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single +peak) and will be smaller when there are multiple peaks. + +@param src1 Source floating point array (CV_32FC1 or CV_64FC1) +@param src2 Source floating point array (CV_32FC1 or CV_64FC1) +@param window Floating point array with windowing coefficients to reduce edge effects (optional). +@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). +@returns detected phase shift (sub-pixel) between the two arrays. + +@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow + */ +CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2, + InputArray window = noArray(), CV_OUT double* response = 0); + +/** @brief This function computes a Hanning window coefficients in two dimensions. + +See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) +for more information. + +An example is shown below: +@code + // create hanning window of size 100x100 and type CV_32F + Mat hann; + createHanningWindow(hann, Size(100, 100), CV_32F); +@endcode +@param dst Destination array to place Hann coefficients in +@param winSize The window size specifications +@param type Created array type + */ +CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type); + +//! @} imgproc_motion + +//! @addtogroup imgproc_misc +//! @{ + +/** @brief Applies a fixed-level threshold to each array element. + +The function applies fixed-level thresholding to a single-channel array. The function is typically +used to get a bi-level (binary) image out of a grayscale image ( cv::compare could be also used for +this purpose) or for removing a noise, that is, filtering out pixels with too small or too large +values. There are several types of thresholding supported by the function. They are determined by +type parameter. + +Also, the special values cv::THRESH_OTSU or cv::THRESH_TRIANGLE may be combined with one of the +above values. In these cases, the function determines the optimal threshold value using the Otsu's +or Triangle algorithm and uses it instead of the specified thresh . The function returns the +computed threshold value. Currently, the Otsu's and Triangle methods are implemented only for 8-bit +images. + +@param src input array (single-channel, 8-bit or 32-bit floating point). +@param dst output array of the same size and type as src. +@param thresh threshold value. +@param maxval maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding +types. +@param type thresholding type (see the cv::ThresholdTypes). + +@sa adaptiveThreshold, findContours, compare, min, max + */ +CV_EXPORTS_W double threshold( InputArray src, OutputArray dst, + double thresh, double maxval, int type ); + + +/** @brief Applies an adaptive threshold to an array. + +The function transforms a grayscale image to a binary image according to the formulae: +- **THRESH_BINARY** + \f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f] +- **THRESH_BINARY_INV** + \f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f] +where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter). + +The function can process the image in-place. + +@param src Source 8-bit single-channel image. +@param dst Destination image of the same size and the same type as src. +@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied +@param adaptiveMethod Adaptive thresholding algorithm to use, see cv::AdaptiveThresholdTypes +@param thresholdType Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV, +see cv::ThresholdTypes. +@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the +pixel: 3, 5, 7, and so on. +@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it +is positive but may be zero or negative as well. + +@sa threshold, blur, GaussianBlur + */ +CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst, + double maxValue, int adaptiveMethod, + int thresholdType, int blockSize, double C ); + +//! @} imgproc_misc + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Blurs an image and downsamples it. + +By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in +any case, the following conditions should be satisfied: + +\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f] + +The function performs the downsampling step of the Gaussian pyramid construction. First, it +convolves the source image with the kernel: + +\f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f] + +Then, it downsamples the image by rejecting even rows and columns. + +@param src input image. +@param dst output image; it has the specified size and the same type as src. +@param dstsize size of the output image. +@param borderType Pixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported) + */ +CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst, + const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); + +/** @brief Upsamples an image and then blurs it. + +By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any +case, the following conditions should be satisfied: + +\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\f] + +The function performs the upsampling step of the Gaussian pyramid construction, though it can +actually be used to construct the Laplacian pyramid. First, it upsamples the source image by +injecting even zero rows and columns and then convolves the result with the same kernel as in +pyrDown multiplied by 4. + +@param src input image. +@param dst output image. It has the specified size and the same type as src . +@param dstsize size of the output image. +@param borderType Pixel extrapolation method, see cv::BorderTypes (only BORDER_DEFAULT is supported) + */ +CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst, + const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); + +/** @brief Constructs the Gaussian pyramid for an image. + +The function constructs a vector of images and builds the Gaussian pyramid by recursively applying +pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. + +@param src Source image. Check pyrDown for the list of supported types. +@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the +same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. +@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative. +@param borderType Pixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported) + */ +CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst, + int maxlevel, int borderType = BORDER_DEFAULT ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +/** @brief Transforms an image to compensate for lens distortion. + +The function transforms an image to compensate radial and tangential lens distortion. + +The function is simply a combination of cv::initUndistortRectifyMap (with unity R ) and cv::remap +(with bilinear interpolation). See the former function for details of the transformation being +performed. + +Those pixels in the destination image, for which there is no correspondent pixels in the source +image, are filled with zeros (black color). + +A particular subset of the source image that will be visible in the corrected image can be regulated +by newCameraMatrix. You can use cv::getOptimalNewCameraMatrix to compute the appropriate +newCameraMatrix depending on your requirements. + +The camera matrix and the distortion parameters can be determined using cv::calibrateCamera. If +the resolution of images is different from the resolution used at the calibration stage, \f$f_x, +f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain +the same. + +@param src Input (distorted) image. +@param dst Output (corrected) image that has the same size and type as src . +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as +cameraMatrix but you may additionally scale and shift the result by using a different matrix. + */ +CV_EXPORTS_W void undistort( InputArray src, OutputArray dst, + InputArray cameraMatrix, + InputArray distCoeffs, + InputArray newCameraMatrix = noArray() ); + +/** @brief Computes the undistortion and rectification transformation map. + +The function computes the joint undistortion and rectification transformation and represents the +result in the form of maps for remap. The undistorted image looks like original, as if it is +captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a +monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by +cv::getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, +newCameraMatrix is normally set to P1 or P2 computed by cv::stereoRectify . + +Also, this new camera is oriented differently in the coordinate space, according to R. That, for +example, helps to align two heads of a stereo camera so that the epipolar lines on both images +become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). + +The function actually builds the maps for the inverse mapping algorithm that is used by remap. That +is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function +computes the corresponding coordinates in the source image (that is, in the original image from +camera). The following process is applied: +\f[ +\begin{array}{l} +x \leftarrow (u - {c'}_x)/{f'}_x \\ +y \leftarrow (v - {c'}_y)/{f'}_y \\ +{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ +x' \leftarrow X/W \\ +y' \leftarrow Y/W \\ +r^2 \leftarrow x'^2 + y'^2 \\ +x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} ++ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ +y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} ++ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ +s\vecthree{x'''}{y'''}{1} = +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ +map_x(u,v) \leftarrow x''' f_x + c_x \\ +map_y(u,v) \leftarrow y''' f_y + c_y +\end{array} +\f] +where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +are the distortion coefficients. + +In case of a stereo camera, this function is called twice: once for each camera head, after +stereoRectify, which in its turn is called after cv::stereoCalibrate. But if the stereo camera +was not calibrated, it is still possible to compute the rectification transformations directly from +the fundamental matrix using cv::stereoRectifyUncalibrated. For each camera, the function computes +homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D +space. R can be computed from H as +\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] +where cameraMatrix can be chosen arbitrarily. + +@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , +computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation +is assumed. In cvInitUndistortMap R assumed to be an identity matrix. +@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$. +@param size Undistorted image size. +@param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2, see cv::convertMaps +@param map1 The first output map. +@param map2 The second output map. + */ +CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs, + InputArray R, InputArray newCameraMatrix, + Size size, int m1type, OutputArray map1, OutputArray map2 ); + +//! initializes maps for cv::remap() for wide-angle +CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs, + Size imageSize, int destImageWidth, + int m1type, OutputArray map1, OutputArray map2, + int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0); + +/** @brief Returns the default new camera matrix. + +The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when +centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true). + +In the latter case, the new camera matrix will be: + +\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f] + +where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively. + +By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not +move the principal point. However, when you work with stereo, it is important to move the principal +points in both views to the same y-coordinate (which is required by most of stereo correspondence +algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for +each view where the principal points are located at the center. + +@param cameraMatrix Input camera matrix. +@param imgsize Camera view image size in pixels. +@param centerPrincipalPoint Location of the principal point in the new camera matrix. The +parameter indicates whether this location should be at the image center or not. + */ +CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(), + bool centerPrincipalPoint = false ); + +/** @brief Computes the ideal point coordinates from the observed point coordinates. + +The function is similar to cv::undistort and cv::initUndistortRectifyMap but it operates on a +sparse set of points instead of a raster image. Also the function performs a reverse transformation +to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a +planar object, it does, up to a translation vector, if the proper R is specified. + +For each observed point coordinate \f$(u, v)\f$ the function computes: +\f[ +\begin{array}{l} +x^{"} \leftarrow (u - c_x)/f_x \\ +y^{"} \leftarrow (v - c_y)/f_y \\ +(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ +{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ +x \leftarrow X/W \\ +y \leftarrow Y/W \\ +\text{only performed if P is specified:} \\ +u' \leftarrow x {f'}_x + {c'}_x \\ +v' \leftarrow y {f'}_y + {c'}_y +\end{array} +\f] + +where *undistort* is an approximate iterative algorithm that estimates the normalized original +point coordinates out of the normalized distorted point coordinates ("normalized" means that the +coordinates do not depend on the camera matrix). + +The function can be used for both a stereo camera head or a monocular camera (when R is empty). + +@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2). +@param dst Output ideal point coordinates after undistortion and reverse perspective +transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates. +@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by +cv::stereoRectify can be passed here. If the matrix is empty, the identity transformation is used. +@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by +cv::stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used. + */ +CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst, + InputArray cameraMatrix, InputArray distCoeffs, + InputArray R = noArray(), InputArray P = noArray()); + +//! @} imgproc_transform + +//! @addtogroup imgproc_hist +//! @{ + +/** @example demhist.cpp +An example for creating histograms of an image +*/ + +/** @brief Calculates a histogram of a set of arrays. + +The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used +to increment a histogram bin are taken from the corresponding input arrays at the same location. The +sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : +@code + #include + #include + + using namespace cv; + + int main( int argc, char** argv ) + { + Mat src, hsv; + if( argc != 2 || !(src=imread(argv[1], 1)).data ) + return -1; + + cvtColor(src, hsv, COLOR_BGR2HSV); + + // Quantize the hue to 30 levels + // and the saturation to 32 levels + int hbins = 30, sbins = 32; + int histSize[] = {hbins, sbins}; + // hue varies from 0 to 179, see cvtColor + float hranges[] = { 0, 180 }; + // saturation varies from 0 (black-gray-white) to + // 255 (pure spectrum color) + float sranges[] = { 0, 256 }; + const float* ranges[] = { hranges, sranges }; + MatND hist; + // we compute the histogram from the 0-th and 1-st channels + int channels[] = {0, 1}; + + calcHist( &hsv, 1, channels, Mat(), // do not use mask + hist, 2, histSize, ranges, + true, // the histogram is uniform + false ); + double maxVal=0; + minMaxLoc(hist, 0, &maxVal, 0, 0); + + int scale = 10; + Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3); + + for( int h = 0; h < hbins; h++ ) + for( int s = 0; s < sbins; s++ ) + { + float binVal = hist.at(h, s); + int intensity = cvRound(binVal*255/maxVal); + rectangle( histImg, Point(h*scale, s*scale), + Point( (h+1)*scale - 1, (s+1)*scale - 1), + Scalar::all(intensity), + CV_FILLED ); + } + + namedWindow( "Source", 1 ); + imshow( "Source", src ); + + namedWindow( "H-S Histogram", 1 ); + imshow( "H-S Histogram", histImg ); + waitKey(); + } +@endcode + +@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same +size. Each of them can have an arbitrary number of channels. +@param nimages Number of source images. +@param channels List of the dims channels used to compute the histogram. The first array channels +are numerated from 0 to images[0].channels()-1 , the second array channels are counted from +images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. +@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size +as images[i] . The non-zero mask elements mark the array elements counted in the histogram. +@param hist Output histogram, which is a dense or sparse dims -dimensional array. +@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS +(equal to 32 in the current OpenCV version). +@param histSize Array of histogram sizes in each dimension. +@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the +histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower +(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary +\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a +uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( +uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: +\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$ +. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not +counted in the histogram. +@param uniform Flag indicating whether the histogram is uniform or not (see above). +@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning +when it is allocated. This feature enables you to compute a single histogram from several sets of +arrays, or to update the histogram in time. +*/ +CV_EXPORTS void calcHist( const Mat* images, int nimages, + const int* channels, InputArray mask, + OutputArray hist, int dims, const int* histSize, + const float** ranges, bool uniform = true, bool accumulate = false ); + +/** @overload + +this variant uses cv::SparseMat for output +*/ +CV_EXPORTS void calcHist( const Mat* images, int nimages, + const int* channels, InputArray mask, + SparseMat& hist, int dims, + const int* histSize, const float** ranges, + bool uniform = true, bool accumulate = false ); + +/** @overload */ +CV_EXPORTS_W void calcHist( InputArrayOfArrays images, + const std::vector& channels, + InputArray mask, OutputArray hist, + const std::vector& histSize, + const std::vector& ranges, + bool accumulate = false ); + +/** @brief Calculates the back projection of a histogram. + +The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to +cv::calcHist , at each location (x, y) the function collects the values from the selected channels +in the input images and finds the corresponding histogram bin. But instead of incrementing it, the +function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of +statistics, the function computes probability of each element value in respect with the empirical +probability distribution represented by the histogram. See how, for example, you can find and track +a bright-colored object in a scene: + +- Before tracking, show the object to the camera so that it covers almost the whole frame. +Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant +colors in the object. + +- When tracking, calculate a back projection of a hue plane of each input video frame using that +pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make +sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels. + +- Find connected components in the resulting picture and choose, for example, the largest +component. + +This is an approximate algorithm of the CamShift color object tracker. + +@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same +size. Each of them can have an arbitrary number of channels. +@param nimages Number of source images. +@param channels The list of channels used to compute the back projection. The number of channels +must match the histogram dimensionality. The first array channels are numerated from 0 to +images[0].channels()-1 , the second array channels are counted from images[0].channels() to +images[0].channels() + images[1].channels()-1, and so on. +@param hist Input histogram that can be dense or sparse. +@param backProject Destination back projection array that is a single-channel array of the same +size and depth as images[0] . +@param ranges Array of arrays of the histogram bin boundaries in each dimension. See cv::calcHist . +@param scale Optional scale factor for the output back projection. +@param uniform Flag indicating whether the histogram is uniform or not (see above). + +@sa cv::calcHist, cv::compareHist + */ +CV_EXPORTS void calcBackProject( const Mat* images, int nimages, + const int* channels, InputArray hist, + OutputArray backProject, const float** ranges, + double scale = 1, bool uniform = true ); + +/** @overload */ +CV_EXPORTS void calcBackProject( const Mat* images, int nimages, + const int* channels, const SparseMat& hist, + OutputArray backProject, const float** ranges, + double scale = 1, bool uniform = true ); + +/** @overload */ +CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector& channels, + InputArray hist, OutputArray dst, + const std::vector& ranges, + double scale ); + +/** @brief Compares two histograms. + +The function cv::compareHist compares two dense or two sparse histograms using the specified method. + +The function returns \f$d(H_1, H_2)\f$ . + +While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable +for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling +problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms +or more general sparse configurations of weighted points, consider using the cv::EMD function. + +@param H1 First compared histogram. +@param H2 Second compared histogram of the same size as H1 . +@param method Comparison method, see cv::HistCompMethods + */ +CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method ); + +/** @overload */ +CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method ); + +/** @brief Equalizes the histogram of a grayscale image. + +The function equalizes the histogram of the input image using the following algorithm: + +- Calculate the histogram \f$H\f$ for src . +- Normalize the histogram so that the sum of histogram bins is 255. +- Compute the integral of the histogram: +\f[H'_i = \sum _{0 \le j < i} H(j)\f] +- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$ + +The algorithm normalizes the brightness and increases the contrast of the image. + +@param src Source 8-bit single channel image. +@param dst Destination image of the same size and type as src . + */ +CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst ); + +/** @brief Computes the "minimal work" distance between two weighted point configurations. + +The function computes the earth mover distance and/or a lower boundary of the distance between the +two weighted point configurations. One of the applications described in @cite RubnerSept98, +@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation +problem that is solved using some modification of a simplex algorithm, thus the complexity is +exponential in the worst case, though, on average it is much faster. In the case of a real metric +the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used +to determine roughly whether the two signatures are far enough so that they cannot relate to the +same object. + +@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix. +Each row stores the point weight followed by the point coordinates. The matrix is allowed to have +a single column (weights only) if the user-defined cost matrix is used. The weights must be +non-negative and have at least one non-zero value. +@param signature2 Second signature of the same format as signature1 , though the number of rows +may be different. The total weights may be different. In this case an extra "dummy" point is added +to either signature1 or signature2. The weights must be non-negative and have at least one non-zero +value. +@param distType Used metric. See cv::DistanceTypes. +@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix +is used, lower boundary lowerBound cannot be calculated because it needs a metric function. +@param lowerBound Optional input/output parameter: lower boundary of a distance between the two +signatures that is a distance between mass centers. The lower boundary may not be calculated if +the user-defined cost matrix is used, the total weights of point configurations are not equal, or +if the signatures consist of weights only (the signature matrices have a single column). You +**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or +equal to \*lowerBound (it means that the signatures are far enough), the function does not +calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on +return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound +should be set to 0. +@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is +a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 . + */ +CV_EXPORTS float EMD( InputArray signature1, InputArray signature2, + int distType, InputArray cost=noArray(), + float* lowerBound = 0, OutputArray flow = noArray() ); + +//! @} imgproc_hist + +/** @example watershed.cpp +An example using the watershed algorithm + */ + +/** @brief Performs a marker-based image segmentation using the watershed algorithm. + +The function implements one of the variants of watershed, non-parametric marker-based segmentation +algorithm, described in @cite Meyer92 . + +Before passing the image to the function, you have to roughly outline the desired regions in the +image markers with positive (\>0) indices. So, every region is represented as one or more connected +components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary +mask using findContours and drawContours (see the watershed.cpp demo). The markers are "seeds" of +the future image regions. All the other pixels in markers , whose relation to the outlined regions +is not known and should be defined by the algorithm, should be set to 0's. In the function output, +each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the +regions. + +@note Any two neighbor connected components are not necessarily separated by a watershed boundary +(-1's pixels); for example, they can touch each other in the initial marker image passed to the +function. + +@param image Input 8-bit 3-channel image. +@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same +size as image . + +@sa findContours + +@ingroup imgproc_misc + */ +CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers ); + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Performs initial step of meanshift segmentation of an image. + +The function implements the filtering stage of meanshift segmentation, that is, the output of the +function is the filtered "posterized" image with color gradients and fine-grain texture flattened. +At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes +meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is +considered: + +\f[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\f] + +where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively +(though, the algorithm does not depend on the color space used, so any 3-component color space can +be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector +(R',G',B') are found and they act as the neighborhood center on the next iteration: + +\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f] + +After the iterations over, the color components of the initial pixel (that is, the pixel from where +the iterations started) are set to the final value (average color at the last iteration): + +\f[I(X,Y) <- (R*,G*,B*)\f] + +When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is +run on the smallest layer first. After that, the results are propagated to the larger layer and the +iterations are run again only on those pixels where the layer colors differ by more than sr from the +lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the +results will be actually different from the ones obtained by running the meanshift procedure on the +whole original image (i.e. when maxLevel==0). + +@param src The source 8-bit, 3-channel image. +@param dst The destination image of the same format and the same size as the source. +@param sp The spatial window radius. +@param sr The color window radius. +@param maxLevel Maximum level of the pyramid for the segmentation. +@param termcrit Termination criteria: when to stop meanshift iterations. + */ +CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst, + double sp, double sr, int maxLevel = 1, + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) ); + +//! @} + +//! @addtogroup imgproc_misc +//! @{ + +/** @example grabcut.cpp +An example using the GrabCut algorithm + */ + +/** @brief Runs the GrabCut algorithm. + +The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). + +@param img Input 8-bit 3-channel image. +@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when +mode is set to GC_INIT_WITH_RECT. Its elements may have one of the cv::GrabCutClasses. +@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as +"obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT . +@param bgdModel Temporary array for the background model. Do not modify it while you are +processing the same image. +@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are +processing the same image. +@param iterCount Number of iterations the algorithm should make before returning the result. Note +that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or +mode==GC_EVAL . +@param mode Operation mode that could be one of the cv::GrabCutModes + */ +CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect, + InputOutputArray bgdModel, InputOutputArray fgdModel, + int iterCount, int mode = GC_EVAL ); + +/** @example distrans.cpp +An example on using the distance transform\ +*/ + + +/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image. + +The function cv::distanceTransform calculates the approximate or precise distance from every binary +image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. + +When maskSize == DIST_MASK_PRECISE and distanceType == DIST_L2 , the function runs the +algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library. + +In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function +finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, +diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall +distance is calculated as a sum of these basic distances. Since the distance function should be +symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all +the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the +same cost (denoted as `c`). For the cv::DIST_C and cv::DIST_L1 types, the distance is calculated +precisely, whereas for cv::DIST_L2 (Euclidean distance) the distance can be calculated only with a +relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV +uses the values suggested in the original paper: +- DIST_L1: `a = 1, b = 2` +- DIST_L2: + - `3 x 3`: `a=0.955, b=1.3693` + - `5 x 5`: `a=1, b=1.4, c=2.1969` +- DIST_C: `a = 1, b = 1` + +Typically, for a fast, coarse distance estimation DIST_L2, a \f$3\times 3\f$ mask is used. For a +more accurate distance estimation DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used. +Note that both the precise and the approximate algorithms are linear on the number of pixels. + +This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$ +but also identifies the nearest connected component consisting of zero pixels +(labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the +component/pixel is stored in `labels(x, y)`. When labelType==DIST_LABEL_CCOMP, the function +automatically finds connected components of zero pixels in the input image and marks them with +distinct labels. When labelType==DIST_LABEL_CCOMP, the function scans through the input image and +marks all the zero pixels with distinct labels. + +In this mode, the complexity is still linear. That is, the function provides a very fast way to +compute the Voronoi diagram for a binary image. Currently, the second variant can use only the +approximate distance transform algorithm, i.e. maskSize=DIST_MASK_PRECISE is not supported +yet. + +@param src 8-bit, single-channel (binary) source image. +@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, +single-channel image of the same size as src. +@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type +CV_32SC1 and the same size as src. +@param distanceType Type of distance, see cv::DistanceTypes +@param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks. +DIST_MASK_PRECISE is not supported by this variant. In case of the DIST_L1 or DIST_C distance type, +the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times +5\f$ or any larger aperture. +@param labelType Type of the label array to build, see cv::DistanceTransformLabelTypes. + */ +CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst, + OutputArray labels, int distanceType, int maskSize, + int labelType = DIST_LABEL_CCOMP ); + +/** @overload +@param src 8-bit, single-channel (binary) source image. +@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, +single-channel image of the same size as src . +@param distanceType Type of distance, see cv::DistanceTypes +@param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks. In case of the +DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives +the same result as \f$5\times 5\f$ or any larger aperture. +@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for +the first variant of the function and distanceType == DIST_L1. +*/ +CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst, + int distanceType, int maskSize, int dstType=CV_32F); + +/** @example ffilldemo.cpp + An example using the FloodFill technique +*/ + +/** @overload + +variant without `mask` parameter +*/ +CV_EXPORTS int floodFill( InputOutputArray image, + Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0, + Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), + int flags = 4 ); + +/** @brief Fills a connected component with the given color. + +The function cv::floodFill fills a connected component starting from the seed point with the specified +color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The +pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if: + +- in case of a grayscale image and floating range +\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\f] + + +- in case of a grayscale image and fixed range +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f] + + +- in case of a color image and floating range +\f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f] +\f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f] +and +\f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f] + + +- in case of a color image and fixed range +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f] +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f] +and +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f] + + +where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the +component. That is, to be added to the connected component, a color/brightness of the pixel should +be close enough to: +- Color/brightness of one of its neighbors that already belong to the connected component in case +of a floating range. +- Color/brightness of the seed point in case of a fixed range. + +Use these functions to either mark a connected component with the specified color in-place, or build +a mask and then extract the contour, or copy the region to another image, and so on. + +@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the +function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See +the details below. +@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels +taller than image. Since this is both an input and output parameter, you must take responsibility +of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, +an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the +mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags +as described below. It is therefore possible to use the same mask in multiple calls to the function +to make sure the filled areas do not overlap. +@param seedPoint Starting point. +@param newVal New value of the repainted domain pixels. +@param loDiff Maximal lower brightness/color difference between the currently observed pixel and +one of its neighbors belonging to the component, or a seed pixel being added to the component. +@param upDiff Maximal upper brightness/color difference between the currently observed pixel and +one of its neighbors belonging to the component, or a seed pixel being added to the component. +@param rect Optional output parameter set by the function to the minimum bounding rectangle of the +repainted domain. +@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of +4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A +connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) +will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill +the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest +neighbours and fill the mask with a value of 255. The following additional options occupy higher +bits and therefore may be further combined with the connectivity and mask fill values using +bit-wise or (|), see cv::FloodFillFlags. + +@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the +pixel \f$(x+1, y+1)\f$ in the mask . + +@sa findContours + */ +CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask, + Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0, + Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), + int flags = 4 ); + +/** @brief Converts an image from one color space to another. + +The function converts an input image from one color space to another. In case of a transformation +to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note +that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the +bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue +component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and +sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. + +The conventional ranges for R, G, and B channel values are: +- 0 to 255 for CV_8U images +- 0 to 65535 for CV_16U images +- 0 to 1 for CV_32F images + +In case of linear transformations, the range does not matter. But in case of a non-linear +transformation, an input RGB image should be normalized to the proper value range to get the correct +results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a +32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will +have the 0..255 value range instead of 0..1 assumed by the function. So, before calling cvtColor , +you need first to scale the image down: +@code + img *= 1./255; + cvtColor(img, img, COLOR_BGR2Luv); +@endcode +If you use cvtColor with 8-bit images, the conversion will have some information lost. For many +applications, this will not be noticeable but it is recommended to use 32-bit images in applications +that need the full range of colors or that convert an image before an operation and then convert +back. + +If conversion adds the alpha channel, its value will set to the maximum of corresponding channel +range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. + +@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision +floating-point. +@param dst output image of the same size and depth as src. +@param code color space conversion code (see cv::ColorConversionCodes). +@param dstCn number of channels in the destination image; if the parameter is 0, the number of the +channels is derived automatically from src and code. + +@see @ref imgproc_color_conversions + */ +CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 ); + +//! @} imgproc_misc + +// main function for all demosaicing procceses +CV_EXPORTS_W void demosaicing(InputArray _src, OutputArray _dst, int code, int dcn = 0); + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape. + +The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The +results are returned in the structure cv::Moments. + +@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( +\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ). +@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is +used for images only. +@returns moments. + +@note Only applicable to contour moments calculations from Python bindings: Note that the numpy +type for the input array should be either np.int32 or np.float32. + +@sa contourArea, arcLength + */ +CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false ); + +/** @brief Calculates seven Hu invariants. + +The function calculates seven Hu invariants (introduced in @cite Hu62; see also +) defined as: + +\f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f] + +where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ . + +These values are proved to be invariants to the image scale, rotation, and reflection except the +seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of +infinite image resolution. In case of raster images, the computed Hu invariants for the original and +transformed images are a bit different. + +@param moments Input moments computed with moments . +@param hu Output Hu invariants. + +@sa matchShapes + */ +CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] ); + +/** @overload */ +CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu ); + +//! @} imgproc_shape + +//! @addtogroup imgproc_object +//! @{ + +//! type of the template matching operation +enum TemplateMatchModes { + TM_SQDIFF = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f] + TM_SQDIFF_NORMED = 1, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] + TM_CCORR = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f] + TM_CCORR_NORMED = 3, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] + TM_CCOEFF = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f] + //!< where + //!< \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f] + TM_CCOEFF_NORMED = 5 //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f] +}; + +/** @brief Compares a template against overlapped image regions. + +The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against +templ using the specified method and stores the comparison results in result . Here are the formulae +for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation +is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$ + +After the function finishes the comparison, the best matches can be found as global minimums (when +TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the +minMaxLoc function. In case of a color image, template summation in the numerator and each sum in +the denominator is done over all of the channels and separate mean values are used for each channel. +That is, the function can take a color template and a color image. The result will still be a +single-channel image, which is easier to analyze. + +@param image Image where the search is running. It must be 8-bit or 32-bit floating-point. +@param templ Searched template. It must be not greater than the source image and have the same +data type. +@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image +is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ . +@param method Parameter specifying the comparison method, see cv::TemplateMatchModes +@param mask Mask of searched template. It must have the same datatype and size with templ. It is +not set by default. + */ +CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ, + OutputArray result, int method, InputArray mask = noArray() ); + +//! @} + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief computes the connected components labeled image of boolean image + +image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 +represents the background label. ltype specifies the output label image type, an important +consideration based on the total number of labels or alternatively the total number of pixels in +the source image. ccltype specifies the connected components labeling algorithm to use, currently +Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the cv::ConnectedComponentsAlgorithmsTypes +for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +@param ccltype connected components algorithm type (see the cv::ConnectedComponentsAlgorithmsTypes). +*/ +CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels, + int connectivity, int ltype, int ccltype); + + +/** @overload + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +*/ +CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels, + int connectivity = 8, int ltype = CV_32S); + + +/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label + +image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 +represents the background label. ltype specifies the output label image type, an important +consideration based on the total number of labels or alternatively the total number of pixels in +the source image. ccltype specifies the connected components labeling algorithm to use, currently +Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the cv::ConnectedComponentsAlgorithmsTypes +for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. + + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param stats statistics output for each label, including the background label, see below for +available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of +cv::ConnectedComponentsTypes. The data type is CV_32S. +@param centroids centroid output for each label, including the background label. Centroids are +accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +@param ccltype connected components algorithm type (see the cv::ConnectedComponentsAlgorithmsTypes). +*/ +CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels, + OutputArray stats, OutputArray centroids, + int connectivity, int ltype, int ccltype); + +/** @overload +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param stats statistics output for each label, including the background label, see below for +available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of +cv::ConnectedComponentsTypes. The data type is CV_32S. +@param centroids centroid output for each label, including the background label. Centroids are +accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +*/ +CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels, + OutputArray stats, OutputArray centroids, + int connectivity = 8, int ltype = CV_32S); + + +/** @brief Finds contours in a binary image. + +The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours +are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the +OpenCV sample directory. +@note Since opencv 3.2 source image is not modified by this function. + +@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero +pixels remain 0's, so the image is treated as binary . You can use cv::compare, cv::inRange, cv::threshold , +cv::adaptiveThreshold, cv::Canny, and others to create a binary image out of a grayscale or color one. +If mode equals to cv::RETR_CCOMP or cv::RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). +@param contours Detected contours. Each contour is stored as a vector of points (e.g. +std::vector >). +@param hierarchy Optional output vector (e.g. std::vector), containing information about the image topology. It has +as many elements as the number of contours. For each i-th contour contours[i], the elements +hierarchy[i][0] , hiearchy[i][1] , hiearchy[i][2] , and hiearchy[i][3] are set to 0-based indices +in contours of the next and previous contours at the same hierarchical level, the first child +contour and the parent contour, respectively. If for the contour i there are no next, previous, +parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. +@param mode Contour retrieval mode, see cv::RetrievalModes +@param method Contour approximation method, see cv::ContourApproximationModes +@param offset Optional offset by which every contour point is shifted. This is useful if the +contours are extracted from the image ROI and then they should be analyzed in the whole image +context. + */ +CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours, + OutputArray hierarchy, int mode, + int method, Point offset = Point()); + +/** @overload */ +CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours, + int mode, int method, Point offset = Point()); + +/** @brief Approximates a polygonal curve(s) with the specified precision. + +The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less +vertices so that the distance between them is less or equal to the specified precision. It uses the +Douglas-Peucker algorithm + +@param curve Input vector of a 2D point stored in std::vector or Mat +@param approxCurve Result of the approximation. The type should match the type of the input curve. +@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance +between the original curve and its approximation. +@param closed If true, the approximated curve is closed (its first and last vertices are +connected). Otherwise, it is not closed. + */ +CV_EXPORTS_W void approxPolyDP( InputArray curve, + OutputArray approxCurve, + double epsilon, bool closed ); + +/** @brief Calculates a contour perimeter or a curve length. + +The function computes a curve length or a closed contour perimeter. + +@param curve Input vector of 2D points, stored in std::vector or Mat. +@param closed Flag indicating whether the curve is closed or not. + */ +CV_EXPORTS_W double arcLength( InputArray curve, bool closed ); + +/** @brief Calculates the up-right bounding rectangle of a point set. + +The function calculates and returns the minimal up-right bounding rectangle for the specified point set. + +@param points Input 2D point set, stored in std::vector or Mat. + */ +CV_EXPORTS_W Rect boundingRect( InputArray points ); + +/** @brief Calculates a contour area. + +The function computes a contour area. Similarly to moments , the area is computed using the Green +formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using +drawContours or fillPoly , can be different. Also, the function will most certainly give a wrong +results for contours with self-intersections. + +Example: +@code + vector contour; + contour.push_back(Point2f(0, 0)); + contour.push_back(Point2f(10, 0)); + contour.push_back(Point2f(10, 10)); + contour.push_back(Point2f(5, 4)); + + double area0 = contourArea(contour); + vector approx; + approxPolyDP(contour, approx, 5, true); + double area1 = contourArea(approx); + + cout << "area0 =" << area0 << endl << + "area1 =" << area1 << endl << + "approx poly vertices" << approx.size() << endl; +@endcode +@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. +@param oriented Oriented area flag. If it is true, the function returns a signed area value, +depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can +determine orientation of a contour by taking the sign of an area. By default, the parameter is +false, which means that the absolute value is returned. + */ +CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false ); + +/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set. + +The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a +specified point set. See the OpenCV sample minarea.cpp . Developer should keep in mind that the +returned rotatedRect can contain negative indices when data is close to the containing Mat element +boundary. + +@param points Input vector of 2D points, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect minAreaRect( InputArray points ); + +/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. + +The function finds the four vertices of a rotated rectangle. This function is useful to draw the +rectangle. In C++, instead of using this function, you can directly use box.points() method. Please +visit the [tutorial on bounding +rectangle](http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html#bounding-rects-circles) +for more information. + +@param box The input rotated rectangle. It may be the output of +@param points The output array of four vertices of rectangles. + */ +CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points); + +/** @brief Finds a circle of the minimum area enclosing a 2D point set. + +The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. See +the OpenCV sample minarea.cpp . + +@param points Input vector of 2D points, stored in std::vector\<\> or Mat +@param center Output center of the circle. +@param radius Output radius of the circle. + */ +CV_EXPORTS_W void minEnclosingCircle( InputArray points, + CV_OUT Point2f& center, CV_OUT float& radius ); + +/** @example minarea.cpp + */ + +/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area. + +The function finds a triangle of minimum area enclosing the given set of 2D points and returns its +area. The output for a given 2D point set is shown in the image below. 2D points are depicted in +*red* and the enclosing triangle in *yellow*. + +![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png) + +The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's +@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal +enclosing triangle of a 2D convex polygon with n vertices. Since the minEnclosingTriangle function +takes a 2D point set as input an additional preprocessing step of computing the convex hull of the +2D point set is required. The complexity of the convexHull function is \f$O(n log(n))\f$ which is higher +than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$. + +@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat +@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth +of the OutputArray must be CV_32F. + */ +CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle ); + +/** @brief Compares two shapes. + +The function compares two shapes. All three implemented methods use the Hu invariants (see cv::HuMoments) + +@param contour1 First contour or grayscale image. +@param contour2 Second contour or grayscale image. +@param method Comparison method, see ::ShapeMatchModes +@param parameter Method-specific parameter (not supported now). + */ +CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2, + int method, double parameter ); + +/** @example convexhull.cpp +An example using the convexHull functionality +*/ + +/** @brief Finds the convex hull of a point set. + +The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82 +that has *O(N logN)* complexity in the current implementation. See the OpenCV sample convexhull.cpp +that demonstrates the usage of different function variants. + +@param points Input 2D point set, stored in std::vector or Mat. +@param hull Output convex hull. It is either an integer vector of indices or vector of points. In +the first case, the hull elements are 0-based indices of the convex hull points in the original +array (since the set of convex hull points is a subset of the original point set). In the second +case, hull elements are the convex hull points themselves. +@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. +Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing +to the right, and its Y axis pointing upwards. +@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function +returns convex hull points. Otherwise, it returns indices of the convex hull points. When the +output array is std::vector, the flag is ignored, and the output depends on the type of the +vector: std::vector\ implies returnPoints=false, std::vector\ implies +returnPoints=true. + */ +CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull, + bool clockwise = false, bool returnPoints = true ); + +/** @brief Finds the convexity defects of a contour. + +The figure below displays convexity defects of a hand contour: + +![image](pics/defects.png) + +@param contour Input contour. +@param convexhull Convex hull obtained using convexHull that should contain indices of the contour +points that make the hull. +@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java +interface each convexity defect is represented as 4-element integer vector (a.k.a. cv::Vec4i): +(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices +in the original contour of the convexity defect beginning, end and the farthest point, and +fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the +farthest contour point and the hull. That is, to get the floating-point value of the depth will be +fixpt_depth/256.0. + */ +CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects ); + +/** @brief Tests a contour convexity. + +The function tests whether the input contour is convex or not. The contour must be simple, that is, +without self-intersections. Otherwise, the function output is undefined. + +@param contour Input vector of 2D points, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W bool isContourConvex( InputArray contour ); + +//! finds intersection of two convex polygons +CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2, + OutputArray _p12, bool handleNested = true ); + +/** @example fitellipse.cpp + An example using the fitEllipse technique +*/ + +/** @brief Fits an ellipse around a set of 2D points. + +The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of +all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95 +is used. Developer should keep in mind that it is possible that the returned +ellipse/rotatedRect data contains negative indices, due to the data points being close to the +border of the containing Mat element. + +@param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipse( InputArray points ); + +/** @brief Fits a line to a 2D or 3D point set. + +The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where +\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one +of the following: +- DIST_L2 +\f[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\f] +- DIST_L1 +\f[\rho (r) = r\f] +- DIST_L12 +\f[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f] +- DIST_FAIR +\f[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\f] +- DIST_WELSCH +\f[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\f] +- DIST_HUBER +\f[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f] + +The algorithm is based on the M-estimator ( ) technique +that iteratively fits the line using the weighted least-squares algorithm. After each iteration the +weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ . + +@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. +@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements +(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and +(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like +Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line +and (x0, y0, z0) is a point on the line. +@param distType Distance used by the M-estimator, see cv::DistanceTypes +@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value +is chosen. +@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line). +@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. + */ +CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType, + double param, double reps, double aeps ); + +/** @brief Performs a point-in-contour test. + +The function determines whether the point is inside a contour, outside, or lies on an edge (or +coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) +value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. +Otherwise, the return value is a signed distance between the point and the nearest contour edge. + +See below a sample output of the function where each image pixel is tested against the contour: + +![sample output](pics/pointpolygon.png) + +@param contour Input contour. +@param pt Point tested against the contour. +@param measureDist If true, the function estimates the signed distance from the point to the +nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. + */ +CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist ); + +/** @brief Finds out if there is any intersection between two rotated rectangles. + +If there is then the vertices of the interesecting region are returned as well. + +Below are some examples of intersection configurations. The hatched pattern indicates the +intersecting region and the red vertices are returned by the function. + +![intersection examples](pics/intersection.png) + +@param rect1 First rectangle +@param rect2 Second rectangle +@param intersectingRegion The output array of the verticies of the intersecting region. It returns +at most 8 vertices. Stored as std::vector\ or cv::Mat as Mx1 of type CV_32FC2. +@returns One of cv::RectanglesIntersectTypes + */ +CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion ); + +//! @} imgproc_shape + +CV_EXPORTS_W Ptr createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8)); + +//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122. +//! Detects position only without traslation and rotation +CV_EXPORTS Ptr createGeneralizedHoughBallard(); + +//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038. +//! Detects position, traslation and rotation +CV_EXPORTS Ptr createGeneralizedHoughGuil(); + +//! Performs linear blending of two images +CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst); + +//! @addtogroup imgproc_colormap +//! @{ + +//! GNU Octave/MATLAB equivalent colormaps +enum ColormapTypes +{ + COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg) + COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg) + COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg) + COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg) + COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg) + COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg) + COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg) + COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg) + COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg) + COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg) + COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg) + COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg) + COLORMAP_PARULA = 12 //!< ![parula](pics/colormaps/colorscale_parula.jpg) +}; + +/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image. + +@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. +@param dst The result is the colormapped source image. Note: Mat::create is called on dst. +@param colormap The colormap to apply, see cv::ColormapTypes + */ +CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap); + +//! @} imgproc_colormap + +//! @addtogroup imgproc_draw +//! @{ + +/** @brief Draws a line segment connecting two points. + +The function line draws the line segment between pt1 and pt2 points in the image. The line is +clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected +or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased +lines are drawn using Gaussian filtering. + +@param img Image. +@param pt1 First point of the line segment. +@param pt2 Second point of the line segment. +@param color Line color. +@param thickness Line thickness. +@param lineType Type of the line, see cv::LineTypes. +@param shift Number of fractional bits in the point coordinates. + */ +CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0); + +/** @brief Draws a arrow segment pointing from the first point to the second one. + +The function arrowedLine draws an arrow between pt1 and pt2 points in the image. See also cv::line. + +@param img Image. +@param pt1 The point the arrow starts from. +@param pt2 The point the arrow points to. +@param color Line color. +@param thickness Line thickness. +@param line_type Type of the line, see cv::LineTypes +@param shift Number of fractional bits in the point coordinates. +@param tipLength The length of the arrow tip in relation to the arrow length + */ +CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, + int thickness=1, int line_type=8, int shift=0, double tipLength=0.1); + +/** @brief Draws a simple, thick, or filled up-right rectangle. + +The function rectangle draws a rectangle outline or a filled rectangle whose two opposite corners +are pt1 and pt2. + +@param img Image. +@param pt1 Vertex of the rectangle. +@param pt2 Vertex of the rectangle opposite to pt1 . +@param color Rectangle color or brightness (grayscale image). +@param thickness Thickness of lines that make up the rectangle. Negative values, like CV_FILLED , +mean that the function has to draw a filled rectangle. +@param lineType Type of the line. See the line description. +@param shift Number of fractional bits in the point coordinates. + */ +CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @overload + +use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and +r.br()-Point(1,1)` are opposite corners +*/ +CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @brief Draws a circle. + +The function circle draws a simple or filled circle with a given center and radius. +@param img Image where the circle is drawn. +@param center Center of the circle. +@param radius Radius of the circle. +@param color Circle color. +@param thickness Thickness of the circle outline, if positive. Negative thickness means that a +filled circle is to be drawn. +@param lineType Type of the circle boundary. See the line description. +@param shift Number of fractional bits in the coordinates of the center and in the radius value. + */ +CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector. + +The function cv::ellipse with less parameters draws an ellipse outline, a filled ellipse, an elliptic +arc, or a filled ellipse sector. A piecewise-linear curve is used to approximate the elliptic arc +boundary. If you need more control of the ellipse rendering, you can retrieve the curve using +ellipse2Poly and then render it with polylines or fill it with fillPoly . If you use the first +variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and +endAngle=360 . The figure below explains the meaning of the parameters. + +![Parameters of Elliptic Arc](pics/ellipse.png) + +@param img Image. +@param center Center of the ellipse. +@param axes Half of the size of the ellipse main axes. +@param angle Ellipse rotation angle in degrees. +@param startAngle Starting angle of the elliptic arc in degrees. +@param endAngle Ending angle of the elliptic arc in degrees. +@param color Ellipse color. +@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that +a filled ellipse sector is to be drawn. +@param lineType Type of the ellipse boundary. See the line description. +@param shift Number of fractional bits in the coordinates of the center and values of axes. + */ +CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes, + double angle, double startAngle, double endAngle, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @overload +@param img Image. +@param box Alternative ellipse representation via RotatedRect. This means that the function draws +an ellipse inscribed in the rotated rectangle. +@param color Ellipse color. +@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that +a filled ellipse sector is to be drawn. +@param lineType Type of the ellipse boundary. See the line description. +*/ +CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color, + int thickness = 1, int lineType = LINE_8); + +/* ----------------------------------------------------------------------------------------- */ +/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */ +/* ----------------------------------------------------------------------------------------- */ + +//! Possible set of marker types used for the cv::drawMarker function +enum MarkerTypes +{ + MARKER_CROSS = 0, //!< A crosshair marker shape + MARKER_TILTED_CROSS = 1, //!< A 45 degree tilted crosshair marker shape + MARKER_STAR = 2, //!< A star marker shape, combination of cross and tilted cross + MARKER_DIAMOND = 3, //!< A diamond marker shape + MARKER_SQUARE = 4, //!< A square marker shape + MARKER_TRIANGLE_UP = 5, //!< An upwards pointing triangle marker shape + MARKER_TRIANGLE_DOWN = 6 //!< A downwards pointing triangle marker shape +}; + +/** @brief Draws a marker on a predefined position in an image. + +The function drawMarker draws a marker on a given position in the image. For the moment several +marker types are supported, see cv::MarkerTypes for more information. + +@param img Image. +@param position The point where the crosshair is positioned. +@param color Line color. +@param markerType The specific type of marker you want to use, see cv::MarkerTypes +@param thickness Line thickness. +@param line_type Type of the line, see cv::LineTypes +@param markerSize The length of the marker axis [default = 20 pixels] + */ +CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color, + int markerType = MARKER_CROSS, int markerSize=20, int thickness=1, + int line_type=8); + +/* ----------------------------------------------------------------------------------------- */ +/* END OF MARKER SECTION */ +/* ----------------------------------------------------------------------------------------- */ + +/** @overload */ +CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts, + const Scalar& color, int lineType = LINE_8, + int shift = 0); + +/** @brief Fills a convex polygon. + +The function fillConvexPoly draws a filled convex polygon. This function is much faster than the +function cv::fillPoly . It can fill not only convex polygons but any monotonic polygon without +self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) +twice at the most (though, its top-most and/or the bottom edge could be horizontal). + +@param img Image. +@param points Polygon vertices. +@param color Polygon color. +@param lineType Type of the polygon boundaries. See the line description. +@param shift Number of fractional bits in the vertex coordinates. + */ +CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points, + const Scalar& color, int lineType = LINE_8, + int shift = 0); + +/** @overload */ +CV_EXPORTS void fillPoly(Mat& img, const Point** pts, + const int* npts, int ncontours, + const Scalar& color, int lineType = LINE_8, int shift = 0, + Point offset = Point() ); + +/** @brief Fills the area bounded by one or more polygons. + +The function fillPoly fills an area bounded by several polygonal contours. The function can fill +complex areas, for example, areas with holes, contours with self-intersections (some of their +parts), and so forth. + +@param img Image. +@param pts Array of polygons where each polygon is represented as an array of points. +@param color Polygon color. +@param lineType Type of the polygon boundaries. See the line description. +@param shift Number of fractional bits in the vertex coordinates. +@param offset Optional offset of all points of the contours. + */ +CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts, + const Scalar& color, int lineType = LINE_8, int shift = 0, + Point offset = Point() ); + +/** @overload */ +CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts, + int ncontours, bool isClosed, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0 ); + +/** @brief Draws several polygonal curves. + +@param img Image. +@param pts Array of polygonal curves. +@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, +the function draws a line from the last vertex of each curve to its first vertex. +@param color Polyline color. +@param thickness Thickness of the polyline edges. +@param lineType Type of the line segments. See the line description. +@param shift Number of fractional bits in the vertex coordinates. + +The function polylines draws one or more polygonal curves. + */ +CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts, + bool isClosed, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0 ); + +/** @example contours2.cpp + An example using the drawContour functionality +*/ + +/** @example segment_objects.cpp +An example using drawContours to clean up a background segmentation result + */ + +/** @brief Draws contours outlines or filled contours. + +The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area +bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve +connected components from the binary image and label them: : +@code + #include "opencv2/imgproc.hpp" + #include "opencv2/highgui.hpp" + + using namespace cv; + using namespace std; + + int main( int argc, char** argv ) + { + Mat src; + // the first command-line parameter must be a filename of the binary + // (black-n-white) image + if( argc != 2 || !(src=imread(argv[1], 0)).data) + return -1; + + Mat dst = Mat::zeros(src.rows, src.cols, CV_8UC3); + + src = src > 1; + namedWindow( "Source", 1 ); + imshow( "Source", src ); + + vector > contours; + vector hierarchy; + + findContours( src, contours, hierarchy, + RETR_CCOMP, CHAIN_APPROX_SIMPLE ); + + // iterate through all the top-level contours, + // draw each connected component with its own random color + int idx = 0; + for( ; idx >= 0; idx = hierarchy[idx][0] ) + { + Scalar color( rand()&255, rand()&255, rand()&255 ); + drawContours( dst, contours, idx, color, FILLED, 8, hierarchy ); + } + + namedWindow( "Components", 1 ); + imshow( "Components", dst ); + waitKey(0); + } +@endcode + +@param image Destination image. +@param contours All the input contours. Each contour is stored as a point vector. +@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. +@param color Color of the contours. +@param thickness Thickness of lines the contours are drawn with. If it is negative (for example, +thickness=CV_FILLED ), the contour interiors are drawn. +@param lineType Line connectivity. See cv::LineTypes. +@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only +some of the contours (see maxLevel ). +@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. +If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function +draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This +parameter is only taken into account when there is hierarchy available. +@param offset Optional contour shift parameter. Shift all the drawn contours by the specified +\f$\texttt{offset}=(dx,dy)\f$ . + */ +CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours, + int contourIdx, const Scalar& color, + int thickness = 1, int lineType = LINE_8, + InputArray hierarchy = noArray(), + int maxLevel = INT_MAX, Point offset = Point() ); + +/** @brief Clips the line against the image rectangle. + +The function cv::clipLine calculates a part of the line segment that is entirely within the specified +rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise, +it returns true . +@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . +@param pt1 First line point. +@param pt2 Second line point. + */ +CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2); + +/** @overload +@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . +@param pt1 First line point. +@param pt2 Second line point. +*/ +CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2); + +/** @overload +@param imgRect Image rectangle. +@param pt1 First line point. +@param pt2 Second line point. +*/ +CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2); + +/** @brief Approximates an elliptic arc with a polyline. + +The function ellipse2Poly computes the vertices of a polyline that approximates the specified +elliptic arc. It is used by cv::ellipse. + +@param center Center of the arc. +@param axes Half of the size of the ellipse main axes. See the ellipse for details. +@param angle Rotation angle of the ellipse in degrees. See the ellipse for details. +@param arcStart Starting angle of the elliptic arc in degrees. +@param arcEnd Ending angle of the elliptic arc in degrees. +@param delta Angle between the subsequent polyline vertices. It defines the approximation +accuracy. +@param pts Output vector of polyline vertices. + */ +CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle, + int arcStart, int arcEnd, int delta, + CV_OUT std::vector& pts ); + +/** @overload +@param center Center of the arc. +@param axes Half of the size of the ellipse main axes. See the ellipse for details. +@param angle Rotation angle of the ellipse in degrees. See the ellipse for details. +@param arcStart Starting angle of the elliptic arc in degrees. +@param arcEnd Ending angle of the elliptic arc in degrees. +@param delta Angle between the subsequent polyline vertices. It defines the approximation +accuracy. +@param pts Output vector of polyline vertices. +*/ +CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle, + int arcStart, int arcEnd, int delta, + CV_OUT std::vector& pts); + +/** @brief Draws a text string. + +The function putText renders the specified text string in the image. Symbols that cannot be rendered +using the specified font are replaced by question marks. See getTextSize for a text rendering code +example. + +@param img Image. +@param text Text string to be drawn. +@param org Bottom-left corner of the text string in the image. +@param fontFace Font type, see cv::HersheyFonts. +@param fontScale Font scale factor that is multiplied by the font-specific base size. +@param color Text color. +@param thickness Thickness of the lines used to draw a text. +@param lineType Line type. See the line for details. +@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, +it is at the top-left corner. + */ +CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org, + int fontFace, double fontScale, Scalar color, + int thickness = 1, int lineType = LINE_8, + bool bottomLeftOrigin = false ); + +/** @brief Calculates the width and height of a text string. + +The function getTextSize calculates and returns the size of a box that contains the specified text. +That is, the following code renders some text, the tight box surrounding it, and the baseline: : +@code + String text = "Funny text inside the box"; + int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + double fontScale = 2; + int thickness = 3; + + Mat img(600, 800, CV_8UC3, Scalar::all(0)); + + int baseline=0; + Size textSize = getTextSize(text, fontFace, + fontScale, thickness, &baseline); + baseline += thickness; + + // center the text + Point textOrg((img.cols - textSize.width)/2, + (img.rows + textSize.height)/2); + + // draw the box + rectangle(img, textOrg + Point(0, baseline), + textOrg + Point(textSize.width, -textSize.height), + Scalar(0,0,255)); + // ... and the baseline first + line(img, textOrg + Point(0, thickness), + textOrg + Point(textSize.width, thickness), + Scalar(0, 0, 255)); + + // then put the text itself + putText(img, text, textOrg, fontFace, fontScale, + Scalar::all(255), thickness, 8); +@endcode + +@param text Input text string. +@param fontFace Font to use, see cv::HersheyFonts. +@param fontScale Font scale factor that is multiplied by the font-specific base size. +@param thickness Thickness of lines used to render the text. See putText for details. +@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text +point. +@return The size of a box that contains the specified text. + +@see cv::putText + */ +CV_EXPORTS_W Size getTextSize(const String& text, int fontFace, + double fontScale, int thickness, + CV_OUT int* baseLine); + +/** @brief Line iterator + +The class is used to iterate over all the pixels on the raster line +segment connecting two specified points. + +The class LineIterator is used to get each pixel of a raster line. It +can be treated as versatile implementation of the Bresenham algorithm +where you can stop at each pixel and do some extra processing, for +example, grab pixel values along the line or draw a line with an effect +(for example, with XOR operation). + +The number of pixels along the line is stored in LineIterator::count. +The method LineIterator::pos returns the current position in the image: + +@code{.cpp} +// grabs pixels along the line (pt1, pt2) +// from 8-bit 3-channel image to the buffer +LineIterator it(img, pt1, pt2, 8); +LineIterator it2 = it; +vector buf(it.count); + +for(int i = 0; i < it.count; i++, ++it) + buf[i] = *(const Vec3b)*it; + +// alternative way of iterating through the line +for(int i = 0; i < it2.count; i++, ++it2) +{ + Vec3b val = img.at(it2.pos()); + CV_Assert(buf[i] == val); +} +@endcode +*/ +class CV_EXPORTS LineIterator +{ +public: + /** @brief intializes the iterator + + creates iterators for the line connecting pt1 and pt2 + the line will be clipped on the image boundaries + the line is 8-connected or 4-connected + If leftToRight=true, then the iteration is always done + from the left-most point to the right most, + not to depend on the ordering of pt1 and pt2 parameters + */ + LineIterator( const Mat& img, Point pt1, Point pt2, + int connectivity = 8, bool leftToRight = false ); + /** @brief returns pointer to the current pixel + */ + uchar* operator *(); + /** @brief prefix increment operator (++it). shifts iterator to the next pixel + */ + LineIterator& operator ++(); + /** @brief postfix increment operator (it++). shifts iterator to the next pixel + */ + LineIterator operator ++(int); + /** @brief returns coordinates of the current pixel + */ + Point pos() const; + + uchar* ptr; + const uchar* ptr0; + int step, elemSize; + int err, count; + int minusDelta, plusDelta; + int minusStep, plusStep; +}; + +//! @cond IGNORED + +// === LineIterator implementation === + +inline +uchar* LineIterator::operator *() +{ + return ptr; +} + +inline +LineIterator& LineIterator::operator ++() +{ + int mask = err < 0 ? -1 : 0; + err += minusDelta + (plusDelta & mask); + ptr += minusStep + (plusStep & mask); + return *this; +} + +inline +LineIterator LineIterator::operator ++(int) +{ + LineIterator it = *this; + ++(*this); + return it; +} + +inline +Point LineIterator::pos() const +{ + Point p; + p.y = (int)((ptr - ptr0)/step); + p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize); + return p; +} + +//! @endcond + +//! @} imgproc_draw + +//! @} imgproc + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/imgproc/imgproc_c.h" +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/imgproc/detail/distortion_model.hpp b/thirdparty/linux/include/opencv2/imgproc/detail/distortion_model.hpp new file mode 100644 index 0000000..a9c3dde --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgproc/detail/distortion_model.hpp @@ -0,0 +1,123 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP +#define OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP + +//! @cond IGNORED + +namespace cv { namespace detail { +/** +Computes the matrix for the projection onto a tilted image sensor +\param tauX angular parameter rotation around x-axis +\param tauY angular parameter rotation around y-axis +\param matTilt if not NULL returns the matrix +\f[ +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) +\f] +where +\f[ +R(\tau_x, \tau_y) = +\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} +\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = +\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} +{0}{\cos(\tau_x)}{\sin(\tau_x)} +{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. +\f] +\param dMatTiltdTauX if not NULL it returns the derivative of matTilt with +respect to \f$\tau_x\f$. +\param dMatTiltdTauY if not NULL it returns the derivative of matTilt with +respect to \f$\tau_y\f$. +\param invMatTilt if not NULL it returns the inverse of matTilt +**/ +template +void computeTiltProjectionMatrix(FLOAT tauX, + FLOAT tauY, + Matx* matTilt = 0, + Matx* dMatTiltdTauX = 0, + Matx* dMatTiltdTauY = 0, + Matx* invMatTilt = 0) +{ + FLOAT cTauX = cos(tauX); + FLOAT sTauX = sin(tauX); + FLOAT cTauY = cos(tauY); + FLOAT sTauY = sin(tauY); + Matx matRotX = Matx(1,0,0,0,cTauX,sTauX,0,-sTauX,cTauX); + Matx matRotY = Matx(cTauY,0,-sTauY,0,1,0,sTauY,0,cTauY); + Matx matRotXY = matRotY * matRotX; + Matx matProjZ = Matx(matRotXY(2,2),0,-matRotXY(0,2),0,matRotXY(2,2),-matRotXY(1,2),0,0,1); + if (matTilt) + { + // Matrix for trapezoidal distortion of tilted image sensor + *matTilt = matProjZ * matRotXY; + } + if (dMatTiltdTauX) + { + // Derivative with respect to tauX + Matx dMatRotXYdTauX = matRotY * Matx(0,0,0,0,-sTauX,cTauX,0,-cTauX,-sTauX); + Matx dMatProjZdTauX = Matx(dMatRotXYdTauX(2,2),0,-dMatRotXYdTauX(0,2), + 0,dMatRotXYdTauX(2,2),-dMatRotXYdTauX(1,2),0,0,0); + *dMatTiltdTauX = (matProjZ * dMatRotXYdTauX) + (dMatProjZdTauX * matRotXY); + } + if (dMatTiltdTauY) + { + // Derivative with respect to tauY + Matx dMatRotXYdTauY = Matx(-sTauY,0,-cTauY,0,0,0,cTauY,0,-sTauY) * matRotX; + Matx dMatProjZdTauY = Matx(dMatRotXYdTauY(2,2),0,-dMatRotXYdTauY(0,2), + 0,dMatRotXYdTauY(2,2),-dMatRotXYdTauY(1,2),0,0,0); + *dMatTiltdTauY = (matProjZ * dMatRotXYdTauY) + (dMatProjZdTauY * matRotXY); + } + if (invMatTilt) + { + FLOAT inv = 1./matRotXY(2,2); + Matx invMatProjZ = Matx(inv,0,inv*matRotXY(0,2),0,inv,inv*matRotXY(1,2),0,0,1); + *invMatTilt = matRotXY.t()*invMatProjZ; + } +} +}} // namespace detail, cv + + +//! @endcond + +#endif // OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP diff --git a/thirdparty/linux/include/opencv2/imgproc/hal/hal.hpp b/thirdparty/linux/include/opencv2/imgproc/hal/hal.hpp new file mode 100644 index 0000000..fc6b9d8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgproc/hal/hal.hpp @@ -0,0 +1,189 @@ +#ifndef CV_IMGPROC_HAL_HPP +#define CV_IMGPROC_HAL_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/hal/interface.h" + +namespace cv { namespace hal { + +//! @addtogroup imgproc_hal_functions +//! @{ + +struct CV_EXPORTS Filter2D +{ + static Ptr create(uchar * kernel_data, size_t kernel_step, int kernel_type, + int kernel_width, int kernel_height, + int max_width, int max_height, + int stype, int dtype, + int borderType, double delta, + int anchor_x, int anchor_y, + bool isSubmatrix, bool isInplace); + virtual void apply(uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int full_width, int full_height, + int offset_x, int offset_y) = 0; + virtual ~Filter2D() {} +}; + +struct CV_EXPORTS SepFilter2D +{ + static Ptr create(int stype, int dtype, int ktype, + uchar * kernelx_data, int kernelx_len, + uchar * kernely_data, int kernely_len, + int anchor_x, int anchor_y, + double delta, int borderType); + virtual void apply(uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int full_width, int full_height, + int offset_x, int offset_y) = 0; + virtual ~SepFilter2D() {} +}; + + +struct CV_EXPORTS Morph +{ + static Ptr create(int op, int src_type, int dst_type, int max_width, int max_height, + int kernel_type, uchar * kernel_data, size_t kernel_step, + int kernel_width, int kernel_height, + int anchor_x, int anchor_y, + int borderType, const double borderValue[4], + int iterations, bool isSubmatrix, bool allowInplace); + virtual void apply(uchar * src_data, size_t src_step, uchar * dst_data, size_t dst_step, int width, int height, + int roi_width, int roi_height, int roi_x, int roi_y, + int roi_width2, int roi_height2, int roi_x2, int roi_y2) = 0; + virtual ~Morph() {} +}; + + +CV_EXPORTS void resize(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + double inv_scale_x, double inv_scale_y, int interpolation); + +CV_EXPORTS void warpAffine(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + const double M[6], int interpolation, int borderType, const double borderValue[4]); + +CV_EXPORTS void warpPerspectve(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + const double M[9], int interpolation, int borderType, const double borderValue[4]); + +CV_EXPORTS void cvtBGRtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, int dcn, bool swapBlue); + +CV_EXPORTS void cvtBGRtoBGR5x5(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int greenBits); + +CV_EXPORTS void cvtBGR5x5toBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int dcn, bool swapBlue, int greenBits); + +CV_EXPORTS void cvtBGRtoGray(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue); + +CV_EXPORTS void cvtGraytoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn); + +CV_EXPORTS void cvtBGR5x5toGray(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int greenBits); + +CV_EXPORTS void cvtGraytoBGR5x5(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int greenBits); +CV_EXPORTS void cvtBGRtoYUV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isCbCr); + +CV_EXPORTS void cvtYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isCbCr); + +CV_EXPORTS void cvtBGRtoXYZ(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue); + +CV_EXPORTS void cvtXYZtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue); + +CV_EXPORTS void cvtBGRtoHSV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isFullRange, bool isHSV); + +CV_EXPORTS void cvtHSVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isFullRange, bool isHSV); + +CV_EXPORTS void cvtBGRtoLab(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isLab, bool srgb); + +CV_EXPORTS void cvtLabtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isLab, bool srgb); + +CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtThreePlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtBGRtoThreePlaneYUV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtOnePlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int dcn, bool swapBlue, int uIdx, int ycn); + +CV_EXPORTS void cvtRGBAtoMultipliedRGBA(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height); + +CV_EXPORTS void cvtMultipliedRGBAtoRGBA(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height); + +CV_EXPORTS void integral(int depth, int sdepth, int sqdepth, + const uchar* src, size_t srcstep, + uchar* sum, size_t sumstep, + uchar* sqsum, size_t sqsumstep, + uchar* tilted, size_t tstep, + int width, int height, int cn); + +//! @} + +}} + +#endif // CV_IMGPROC_HAL_HPP diff --git a/thirdparty/linux/include/opencv2/imgproc/hal/interface.h b/thirdparty/linux/include/opencv2/imgproc/hal/interface.h new file mode 100644 index 0000000..9d2a3e5 --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgproc/hal/interface.h @@ -0,0 +1,26 @@ +#ifndef OPENCV_IMGPROC_HAL_INTERFACE_H +#define OPENCV_IMGPROC_HAL_INTERFACE_H + +//! @addtogroup imgproc_hal_interface +//! @{ + +//! @name Interpolation modes +//! @sa cv::InterpolationFlags +//! @{ +#define CV_HAL_INTER_NEAREST 0 +#define CV_HAL_INTER_LINEAR 1 +#define CV_HAL_INTER_CUBIC 2 +#define CV_HAL_INTER_AREA 3 +#define CV_HAL_INTER_LANCZOS4 4 +//! @} + +//! @name Morphology operations +//! @sa cv::MorphTypes +//! @{ +#define MORPH_ERODE 0 +#define MORPH_DILATE 1 +//! @} + +//! @} + +#endif diff --git a/thirdparty/linux/include/opencv2/imgproc/imgproc.hpp b/thirdparty/linux/include/opencv2/imgproc/imgproc.hpp new file mode 100644 index 0000000..4175bd0 --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgproc/imgproc.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/imgproc.hpp" diff --git a/thirdparty/linux/include/opencv2/imgproc/imgproc_c.h b/thirdparty/linux/include/opencv2/imgproc/imgproc_c.h new file mode 100644 index 0000000..d11db4b --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgproc/imgproc_c.h @@ -0,0 +1,1210 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_IMGPROC_C_H +#define OPENCV_IMGPROC_IMGPROC_C_H + +#include "opencv2/imgproc/types_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup imgproc_c +@{ +*/ + +/*********************** Background statistics accumulation *****************************/ + +/** @brief Adds image to accumulator +@see cv::accumulate +*/ +CVAPI(void) cvAcc( const CvArr* image, CvArr* sum, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds squared image to accumulator +@see cv::accumulateSquare +*/ +CVAPI(void) cvSquareAcc( const CvArr* image, CvArr* sqsum, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds a product of two images to accumulator +@see cv::accumulateProduct +*/ +CVAPI(void) cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha +@see cv::accumulateWeighted +*/ +CVAPI(void) cvRunningAvg( const CvArr* image, CvArr* acc, double alpha, + const CvArr* mask CV_DEFAULT(NULL) ); + +/****************************************************************************************\ +* Image Processing * +\****************************************************************************************/ + +/** Copies source 2D array inside of the larger destination array and + makes a border of the specified type (IPL_BORDER_*) around the copied area. */ +CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset, + int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0))); + +/** @brief Smooths the image in one of several ways. + +@param src The source image +@param dst The destination image +@param smoothtype Type of the smoothing, see SmoothMethod_c +@param size1 The first parameter of the smoothing operation, the aperture width. Must be a +positive odd number (1, 3, 5, ...) +@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by +CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if +size2 is zero, it is set to size1. Otherwise it must be a positive odd number. +@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$ +(standard deviation). If it is zero, it is calculated from the kernel size: +\f[\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f] +Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If +sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the +sigma (to provide accurate enough operation). +@param sigma2 additional parameter for bilateral filtering + +@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter. + */ +CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst, + int smoothtype CV_DEFAULT(CV_GAUSSIAN), + int size1 CV_DEFAULT(3), + int size2 CV_DEFAULT(0), + double sigma1 CV_DEFAULT(0), + double sigma2 CV_DEFAULT(0)); + +/** @brief Convolves an image with the kernel. + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point +matrix; if you want to apply different kernels to different channels, split the image into +separate color planes using split and process them individually. +@param anchor anchor of the kernel that indicates the relative position of a filtered point within +the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor +is at the kernel center. + +@see cv::filter2D + */ +CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel, + CvPoint anchor CV_DEFAULT(cvPoint(-1,-1))); + +/** @brief Finds integral image: SUM(X,Y) = sum(x \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f] + +@param hist1 First histogram (the divisor). +@param hist2 Second histogram. +@param dst_hist Destination histogram. +@param scale Scale factor for the destination histogram. + */ +CVAPI(void) cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2, + CvHistogram* dst_hist, double scale CV_DEFAULT(255) ); + +/** @brief equalizes histogram of 8-bit single-channel image +@see cv::equalizeHist +*/ +CVAPI(void) cvEqualizeHist( const CvArr* src, CvArr* dst ); + + +/** @brief Applies distance transform to binary image +@see cv::distanceTransform +*/ +CVAPI(void) cvDistTransform( const CvArr* src, CvArr* dst, + int distance_type CV_DEFAULT(CV_DIST_L2), + int mask_size CV_DEFAULT(3), + const float* mask CV_DEFAULT(NULL), + CvArr* labels CV_DEFAULT(NULL), + int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP)); + + +/** @brief Applies fixed-level threshold to grayscale image. + + This is a basic operation applied before retrieving contours +@see cv::threshold +*/ +CVAPI(double) cvThreshold( const CvArr* src, CvArr* dst, + double threshold, double max_value, + int threshold_type ); + +/** @brief Applies adaptive threshold to grayscale image. + + The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and + CV_ADAPTIVE_THRESH_GAUSSIAN_C are: + neighborhood size (3, 5, 7 etc.), + and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...) +@see cv::adaptiveThreshold +*/ +CVAPI(void) cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value, + int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C), + int threshold_type CV_DEFAULT(CV_THRESH_BINARY), + int block_size CV_DEFAULT(3), + double param1 CV_DEFAULT(5)); + +/** @brief Fills the connected component until the color difference gets large enough +@see cv::floodFill +*/ +CVAPI(void) cvFloodFill( CvArr* image, CvPoint seed_point, + CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)), + CvScalar up_diff CV_DEFAULT(cvScalarAll(0)), + CvConnectedComp* comp CV_DEFAULT(NULL), + int flags CV_DEFAULT(4), + CvArr* mask CV_DEFAULT(NULL)); + +/****************************************************************************************\ +* Feature detection * +\****************************************************************************************/ + +/** @brief Runs canny edge detector +@see cv::Canny +*/ +CVAPI(void) cvCanny( const CvArr* image, CvArr* edges, double threshold1, + double threshold2, int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates constraint image for corner detection + + Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy. + Applying threshold to the result gives coordinates of corners +@see cv::preCornerDetect +*/ +CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners, + int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates eigen values and vectors of 2x2 + gradient covariation matrix at every image pixel +@see cv::cornerEigenValsAndVecs +*/ +CVAPI(void) cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv, + int block_size, int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at + every image pixel +@see cv::cornerMinEigenVal +*/ +CVAPI(void) cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, + int block_size, int aperture_size CV_DEFAULT(3) ); + +/** @brief Harris corner detector: + + Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel +@see cv::cornerHarris +*/ +CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_response, + int block_size, int aperture_size CV_DEFAULT(3), + double k CV_DEFAULT(0.04) ); + +/** @brief Adjust corner position using some sort of gradient search +@see cv::cornerSubPix +*/ +CVAPI(void) cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners, + int count, CvSize win, CvSize zero_zone, + CvTermCriteria criteria ); + +/** @brief Finds a sparse set of points within the selected region + that seem to be easy to track +@see cv::goodFeaturesToTrack +*/ +CVAPI(void) cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image, + CvArr* temp_image, CvPoint2D32f* corners, + int* corner_count, double quality_level, + double min_distance, + const CvArr* mask CV_DEFAULT(NULL), + int block_size CV_DEFAULT(3), + int use_harris CV_DEFAULT(0), + double k CV_DEFAULT(0.04) ); + +/** @brief Finds lines on binary image using one of several methods. + + line_storage is either memory storage or 1 x _max number of lines_ CvMat, its + number of columns is changed by the function. + method is one of CV_HOUGH_*; + rho, theta and threshold are used for each of those methods; + param1 ~ line length, param2 ~ line gap - for probabilistic, + param1 ~ srn, param2 ~ stn - for multi-scale +@see cv::HoughLines +*/ +CVAPI(CvSeq*) cvHoughLines2( CvArr* image, void* line_storage, int method, + double rho, double theta, int threshold, + double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0), + double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI)); + +/** @brief Finds circles in the image +@see cv::HoughCircles +*/ +CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage, + int method, double dp, double min_dist, + double param1 CV_DEFAULT(100), + double param2 CV_DEFAULT(100), + int min_radius CV_DEFAULT(0), + int max_radius CV_DEFAULT(0)); + +/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique) +@see cv::fitLine +*/ +CVAPI(void) cvFitLine( const CvArr* points, int dist_type, double param, + double reps, double aeps, float* line ); + +/****************************************************************************************\ +* Drawing * +\****************************************************************************************/ + +/****************************************************************************************\ +* Drawing functions work with images/matrices of arbitrary type. * +* For color images the channel order is BGR[A] * +* Antialiasing is supported only for 8-bit image now. * +* All the functions include parameter color that means rgb value (that may be * +* constructed with CV_RGB macro) for color images and brightness * +* for grayscale images. * +* If a drawn figure is partially or completely outside of the image, it is clipped.* +\****************************************************************************************/ + +#define CV_RGB( r, g, b ) cvScalar( (b), (g), (r), 0 ) +#define CV_FILLED -1 + +#define CV_AA 16 + +/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points +@see cv::line +*/ +CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2) + + if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn +@see cv::rectangle +*/ +CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + int shift CV_DEFAULT(0)); + +/** @brief Draws a rectangle specified by a CvRect structure +@see cv::rectangle +*/ +CVAPI(void) cvRectangleR( CvArr* img, CvRect r, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + int shift CV_DEFAULT(0)); + + +/** @brief Draws a circle with specified center and radius. + + Thickness works in the same way as with cvRectangle +@see cv::circle +*/ +CVAPI(void) cvCircle( CvArr* img, CvPoint center, int radius, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector + + depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure + is rotated by _angle_. All the angles are in degrees +@see cv::ellipse +*/ +CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes, + double angle, double start_angle, double end_angle, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color, + int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ) +{ + CvSize axes; + axes.width = cvRound(box.size.width*0.5); + axes.height = cvRound(box.size.height*0.5); + + cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle, + 0, 360, color, thickness, line_type, shift ); +} + +/** @brief Fills convex or monotonous polygon. +@see cv::fillConvexPoly +*/ +CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color, + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +/** @brief Fills an area bounded by one or more arbitrary polygons +@see cv::fillPoly +*/ +CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts, + int contours, CvScalar color, + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +/** @brief Draws one or more polygonal curves +@see cv::polylines +*/ +CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours, + int is_closed, CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +#define cvDrawRect cvRectangle +#define cvDrawLine cvLine +#define cvDrawCircle cvCircle +#define cvDrawEllipse cvEllipse +#define cvDrawPolyLine cvPolyLine + +/** @brief Clips the line segment connecting *pt1 and *pt2 + by the rectangular window + + (0<=xptr will point to pt1 (or pt2, see left_to_right description) location in +the image. Returns the number of pixels on the line between the ending points. +@see cv::LineIterator +*/ +CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2, + CvLineIterator* line_iterator, + int connectivity CV_DEFAULT(8), + int left_to_right CV_DEFAULT(0)); + +#define CV_NEXT_LINE_POINT( line_iterator ) \ +{ \ + int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \ + (line_iterator).err += (line_iterator).minus_delta + \ + ((line_iterator).plus_delta & _line_iterator_mask); \ + (line_iterator).ptr += (line_iterator).minus_step + \ + ((line_iterator).plus_step & _line_iterator_mask); \ +} + + +#define CV_FONT_HERSHEY_SIMPLEX 0 +#define CV_FONT_HERSHEY_PLAIN 1 +#define CV_FONT_HERSHEY_DUPLEX 2 +#define CV_FONT_HERSHEY_COMPLEX 3 +#define CV_FONT_HERSHEY_TRIPLEX 4 +#define CV_FONT_HERSHEY_COMPLEX_SMALL 5 +#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6 +#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7 + +#define CV_FONT_ITALIC 16 + +#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX + + +/** Font structure */ +typedef struct CvFont +{ + const char* nameFont; //Qt:nameFont + CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component]) + int font_face; //Qt: bool italic /** =CV_FONT_* */ + const int* ascii; //!< font data and metrics + const int* greek; + const int* cyrillic; + float hscale, vscale; + float shear; //!< slope coefficient: 0 - normal, >0 - italic + int thickness; //!< Qt: weight /** letters thickness */ + float dx; //!< horizontal interval between letters + int line_type; //!< Qt: PointSize +} +CvFont; + +/** @brief Initializes font structure (OpenCV 1.x API). + +The function initializes the font structure that can be passed to text rendering functions. + +@param font Pointer to the font structure initialized by the function +@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers. +@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width +depending on the font type. If equal to 0.5f , the characters are of half the original width. +@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending +on the font type. If equal to 0.5f , the characters are of half the original height. +@param shear Approximate tangent of the character slope relative to the vertical line. A zero +value means a non-italic font, 1.0f means about a 45 degree slope, etc. +@param thickness Thickness of the text strokes +@param line_type Type of the strokes, see line description + +@sa cvPutText + */ +CVAPI(void) cvInitFont( CvFont* font, int font_face, + double hscale, double vscale, + double shear CV_DEFAULT(0), + int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8)); + +CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) ) +{ + CvFont font; + cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA ); + return font; +} + +/** @brief Renders text stroke with specified font and color at specified location. + CvFont should be initialized with cvInitFont +@see cvInitFont, cvGetTextSize, cvFont, cv::putText +*/ +CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org, + const CvFont* font, CvScalar color ); + +/** @brief Calculates bounding box of text stroke (useful for alignment) +@see cv::getTextSize +*/ +CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font, + CvSize* text_size, int* baseline ); + +/** @brief Unpacks color value + +if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels +(depending on arrtype) of destination scalar are set to the same value = _color_ +*/ +CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype ); + +/** @brief Returns the polygon points which make up the given ellipse. + +The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial +sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than +0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total +number of points stored into 'pts' is returned by this function. +@see cv::ellipse2Poly +*/ +CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes, + int angle, int arc_start, int arc_end, CvPoint * pts, int delta ); + +/** @brief Draws contour outlines or filled interiors on the image +@see cv::drawContours +*/ +CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour, + CvScalar external_color, CvScalar hole_color, + int max_level, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + CvPoint offset CV_DEFAULT(cvPoint(0,0))); + +/** @} */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/imgproc/types_c.h b/thirdparty/linux/include/opencv2/imgproc/types_c.h new file mode 100644 index 0000000..eacba02 --- /dev/null +++ b/thirdparty/linux/include/opencv2/imgproc/types_c.h @@ -0,0 +1,626 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_TYPES_C_H +#define OPENCV_IMGPROC_TYPES_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup imgproc_c + @{ +*/ + +/** Connected component structure */ +typedef struct CvConnectedComp +{ + double area; /** DBL_EPSILON ? 1./std::sqrt(am00) : 0; + } + operator cv::Moments() const + { + return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03); + } +#endif +} +CvMoments; + +/** Hu invariants */ +typedef struct CvHuMoments +{ + double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */ +} +CvHuMoments; + +/** Template matching methods */ +enum +{ + CV_TM_SQDIFF =0, + CV_TM_SQDIFF_NORMED =1, + CV_TM_CCORR =2, + CV_TM_CCORR_NORMED =3, + CV_TM_CCOEFF =4, + CV_TM_CCOEFF_NORMED =5 +}; + +typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param ); + +/** Contour retrieval modes */ +enum +{ + CV_RETR_EXTERNAL=0, + CV_RETR_LIST=1, + CV_RETR_CCOMP=2, + CV_RETR_TREE=3, + CV_RETR_FLOODFILL=4 +}; + +/** Contour approximation methods */ +enum +{ + CV_CHAIN_CODE=0, + CV_CHAIN_APPROX_NONE=1, + CV_CHAIN_APPROX_SIMPLE=2, + CV_CHAIN_APPROX_TC89_L1=3, + CV_CHAIN_APPROX_TC89_KCOS=4, + CV_LINK_RUNS=5 +}; + +/* +Internal structure that is used for sequential retrieving contours from the image. +It supports both hierarchical and plane variants of Suzuki algorithm. +*/ +typedef struct _CvContourScanner* CvContourScanner; + +/** Freeman chain reader state */ +typedef struct CvChainPtReader +{ + CV_SEQ_READER_FIELDS() + char code; + CvPoint pt; + schar deltas[8][2]; +} +CvChainPtReader; + +/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */ +#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \ + ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \ + (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \ + (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \ + (deltas)[6] = (step), (deltas)[7] = (step) + (nch)) + + +/** Contour approximation algorithms */ +enum +{ + CV_POLY_APPROX_DP = 0 +}; + +/** @brief Shape matching methods + +\f$A\f$ denotes object1,\f$B\f$ denotes object2 + +\f$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}\f$ + +and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively. +*/ +enum ShapeMatchModes +{ + CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] + CV_CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] + CV_CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] +}; + +/** Shape orientation */ +enum +{ + CV_CLOCKWISE =1, + CV_COUNTER_CLOCKWISE =2 +}; + + +/** Convexity defect */ +typedef struct CvConvexityDefect +{ + CvPoint* start; /**< point of the contour where the defect begins */ + CvPoint* end; /**< point of the contour where the defect ends */ + CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */ + float depth; /**< distance between the farthest point and the convex hull */ +} CvConvexityDefect; + + +/** Histogram comparison methods */ +enum +{ + CV_COMP_CORREL =0, + CV_COMP_CHISQR =1, + CV_COMP_INTERSECT =2, + CV_COMP_BHATTACHARYYA =3, + CV_COMP_HELLINGER =CV_COMP_BHATTACHARYYA, + CV_COMP_CHISQR_ALT =4, + CV_COMP_KL_DIV =5 +}; + +/** Mask size for distance transform */ +enum +{ + CV_DIST_MASK_3 =3, + CV_DIST_MASK_5 =5, + CV_DIST_MASK_PRECISE =0 +}; + +/** Content of output label array: connected components or pixels */ +enum +{ + CV_DIST_LABEL_CCOMP = 0, + CV_DIST_LABEL_PIXEL = 1 +}; + +/** Distance types for Distance Transform and M-estimators */ +enum +{ + CV_DIST_USER =-1, /**< User defined distance */ + CV_DIST_L1 =1, /**< distance = |x1-x2| + |y1-y2| */ + CV_DIST_L2 =2, /**< the simple euclidean distance */ + CV_DIST_C =3, /**< distance = max(|x1-x2|,|y1-y2|) */ + CV_DIST_L12 =4, /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */ + CV_DIST_FAIR =5, /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */ + CV_DIST_WELSCH =6, /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */ + CV_DIST_HUBER =7 /**< distance = |x| threshold ? max_value : 0 */ + CV_THRESH_BINARY_INV =1, /**< value = value > threshold ? 0 : max_value */ + CV_THRESH_TRUNC =2, /**< value = value > threshold ? threshold : value */ + CV_THRESH_TOZERO =3, /**< value = value > threshold ? value : 0 */ + CV_THRESH_TOZERO_INV =4, /**< value = value > threshold ? 0 : value */ + CV_THRESH_MASK =7, + CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value; + combine the flag with one of the above CV_THRESH_* values */ + CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value; + combine the flag with one of the above CV_THRESH_* values, but not + with CV_THRESH_OTSU */ +}; + +/** Adaptive threshold methods */ +enum +{ + CV_ADAPTIVE_THRESH_MEAN_C =0, + CV_ADAPTIVE_THRESH_GAUSSIAN_C =1 +}; + +/** FloodFill flags */ +enum +{ + CV_FLOODFILL_FIXED_RANGE =(1 << 16), + CV_FLOODFILL_MASK_ONLY =(1 << 17) +}; + + +/** Canny edge detector flags */ +enum +{ + CV_CANNY_L2_GRADIENT =(1 << 31) +}; + +/** Variants of a Hough transform */ +enum +{ + CV_HOUGH_STANDARD =0, + CV_HOUGH_PROBABILISTIC =1, + CV_HOUGH_MULTI_SCALE =2, + CV_HOUGH_GRADIENT =3 +}; + + +/* Fast search data structures */ +struct CvFeatureTree; +struct CvLSH; +struct CvLSHOperations; + +/** @} */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/line_descriptor.hpp b/thirdparty/linux/include/opencv2/line_descriptor.hpp new file mode 100644 index 0000000..cb2969f --- /dev/null +++ b/thirdparty/linux/include/opencv2/line_descriptor.hpp @@ -0,0 +1,119 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// + // + // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + // + // By downloading, copying, installing or using the software you agree to this license. + // If you do not agree to this license, do not download, install, + // copy or use the software. + // + // + // License Agreement + // For Open Source Computer Vision Library + // + // Copyright (C) 2013, OpenCV Foundation, all rights reserved. + // Third party copyrights are property of their respective owners. + // + // Redistribution and use in source and binary forms, with or without modification, + // are permitted provided that the following conditions are met: + // + // * Redistribution's of source code must retain the above copyright notice, + // this list of conditions and the following disclaimer. + // + // * Redistribution's in binary form must reproduce the above copyright notice, + // this list of conditions and the following disclaimer in the documentation + // and/or other materials provided with the distribution. + // + // * The name of the copyright holders may not be used to endorse or promote products + // derived from this software without specific prior written permission. + // + // This software is provided by the copyright holders and contributors "as is" and + // any express or implied warranties, including, but not limited to, the implied + // warranties of merchantability and fitness for a particular purpose are disclaimed. + // In no event shall the Intel Corporation or contributors be liable for any direct, + // indirect, incidental, special, exemplary, or consequential damages + // (including, but not limited to, procurement of substitute goods or services; + // loss of use, data, or profits; or business interruption) however caused + // and on any theory of liability, whether in contract, strict liability, + // or tort (including negligence or otherwise) arising in any way out of + // the use of this software, even if advised of the possibility of such damage. + // + //M*/ + +#ifndef __OPENCV_LINE_DESCRIPTOR_HPP__ +#define __OPENCV_LINE_DESCRIPTOR_HPP__ + +#include "opencv2/line_descriptor/descriptor.hpp" + +/** @defgroup line_descriptor Binary descriptors for lines extracted from an image + +Introduction +------------ + +One of the most challenging activities in computer vision is the extraction of useful information +from a given image. Such information, usually comes in the form of points that preserve some kind of +property (for instance, they are scale-invariant) and are actually representative of input image. + +The goal of this module is seeking a new kind of representative information inside an image and +providing the functionalities for its extraction and representation. In particular, differently from +previous methods for detection of relevant elements inside an image, lines are extracted in place of +points; a new class is defined ad hoc to summarize a line's properties, for reuse and plotting +purposes. + +Computation of binary descriptors +--------------------------------- + +To obtatin a binary descriptor representing a certain line detected from a certain octave of an +image, we first compute a non-binary descriptor as described in @cite LBD . Such algorithm works on +lines extracted using EDLine detector, as explained in @cite EDL . Given a line, we consider a +rectangular region centered at it and called *line support region (LSR)*. Such region is divided +into a set of bands \f$\{B_1, B_2, ..., B_m\}\f$, whose length equals the one of line. + +If we indicate with \f$\bf{d}_L\f$ the direction of line, the orthogonal and clockwise direction to line +\f$\bf{d}_{\perp}\f$ can be determined; these two directions, are used to construct a reference frame +centered in the middle point of line. The gradients of pixels \f$\bf{g'}\f$ inside LSR can be projected +to the newly determined frame, obtaining their local equivalent +\f$\bf{g'} = (\bf{g}^T \cdot \bf{d}_{\perp}, \bf{g}^T \cdot \bf{d}_L)^T \triangleq (\bf{g'}_{d_{\perp}}, \bf{g'}_{d_L})^T\f$. + +Later on, a Gaussian function is applied to all LSR's pixels along \f$\bf{d}_\perp\f$ direction; first, +we assign a global weighting coefficient \f$f_g(i) = (1/\sqrt{2\pi}\sigma_g)e^{-d^2_i/2\sigma^2_g}\f$ to +*i*-th row in LSR, where \f$d_i\f$ is the distance of *i*-th row from the center row in LSR, +\f$\sigma_g = 0.5(m \cdot w - 1)\f$ and \f$w\f$ is the width of bands (the same for every band). Secondly, +considering a band \f$B_j\f$ and its neighbor bands \f$B_{j-1}, B_{j+1}\f$, we assign a local weighting +\f$F_l(k) = (1/\sqrt{2\pi}\sigma_l)e^{-d'^2_k/2\sigma_l^2}\f$, where \f$d'_k\f$ is the distance of *k*-th +row from the center row in \f$B_j\f$ and \f$\sigma_l = w\f$. Using the global and local weights, we obtain, +at the same time, the reduction of role played by gradients far from line and of boundary effect, +respectively. + +Each band \f$B_j\f$ in LSR has an associated *band descriptor(BD)* which is computed considering +previous and next band (top and bottom bands are ignored when computing descriptor for first and +last band). Once each band has been assignen its BD, the LBD descriptor of line is simply given by + +\f[LBD = (BD_1^T, BD_2^T, ... , BD^T_m)^T.\f] + +To compute a band descriptor \f$B_j\f$, each *k*-th row in it is considered and the gradients in such +row are accumulated: + +\f[\begin{matrix} \bf{V1}^k_j = \lambda \sum\limits_{\bf{g}'_{d_\perp}>0}\bf{g}'_{d_\perp}, & \bf{V2}^k_j = \lambda \sum\limits_{\bf{g}'_{d_\perp}<0} -\bf{g}'_{d_\perp}, \\ \bf{V3}^k_j = \lambda \sum\limits_{\bf{g}'_{d_L}>0}\bf{g}'_{d_L}, & \bf{V4}^k_j = \lambda \sum\limits_{\bf{g}'_{d_L}<0} -\bf{g}'_{d_L}\end{matrix}.\f] + +with \f$\lambda = f_g(k)f_l(k)\f$. + +By stacking previous results, we obtain the *band description matrix (BDM)* + +\f[BDM_j = \left(\begin{matrix} \bf{V1}_j^1 & \bf{V1}_j^2 & \ldots & \bf{V1}_j^n \\ \bf{V2}_j^1 & \bf{V2}_j^2 & \ldots & \bf{V2}_j^n \\ \bf{V3}_j^1 & \bf{V3}_j^2 & \ldots & \bf{V3}_j^n \\ \bf{V4}_j^1 & \bf{V4}_j^2 & \ldots & \bf{V4}_j^n \end{matrix} \right) \in \mathbb{R}^{4\times n},\f] + +with \f$n\f$ the number of rows in band \f$B_j\f$: + +\f[n = \begin{cases} 2w, & j = 1||m; \\ 3w, & \mbox{else}. \end{cases}\f] + +Each \f$BD_j\f$ can be obtained using the standard deviation vector \f$S_j\f$ and mean vector \f$M_j\f$ of +\f$BDM_J\f$. Thus, finally: + +\f[LBD = (M_1^T, S_1^T, M_2^T, S_2^T, \ldots, M_m^T, S_m^T)^T \in \mathbb{R}^{8m}\f] + +Once the LBD has been obtained, it must be converted into a binary form. For such purpose, we +consider 32 possible pairs of BD inside it; each couple of BD is compared bit by bit and comparison +generates an 8 bit string. Concatenating 32 comparison strings, we get the 256-bit final binary +representation of a single LBD. +*/ + +#endif diff --git a/thirdparty/linux/include/opencv2/line_descriptor/descriptor.hpp b/thirdparty/linux/include/opencv2/line_descriptor/descriptor.hpp new file mode 100644 index 0000000..9f2c639 --- /dev/null +++ b/thirdparty/linux/include/opencv2/line_descriptor/descriptor.hpp @@ -0,0 +1,1369 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// + // + // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + // + // By downloading, copying, installing or using the software you agree to this license. + // If you do not agree to this license, do not download, install, + // copy or use the software. + // + // + // License Agreement + // For Open Source Computer Vision Library + // + // Copyright (C) 2014, Biagio Montesano, all rights reserved. + // Third party copyrights are property of their respective owners. + // + // Redistribution and use in source and binary forms, with or without modification, + // are permitted provided that the following conditions are met: + // + // * Redistribution's of source code must retain the above copyright notice, + // this list of conditions and the following disclaimer. + // + // * Redistribution's in binary form must reproduce the above copyright notice, + // this list of conditions and the following disclaimer in the documentation + // and/or other materials provided with the distribution. + // + // * The name of the copyright holders may not be used to endorse or promote products + // derived from this software without specific prior written permission. + // + // This software is provided by the copyright holders and contributors "as is" and + // any express or implied warranties, including, but not limited to, the implied + // warranties of merchantability and fitness for a particular purpose are disclaimed. + // In no event shall the Intel Corporation or contributors be liable for any direct, + // indirect, incidental, special, exemplary, or consequential damages + // (including, but not limited to, procurement of substitute goods or services; + // loss of use, data, or profits; or business interruption) however caused + // and on any theory of liability, whether in contract, strict liability, + // or tort (including negligence or otherwise) arising in any way out of + // the use of this software, even if advised of the possibility of such damage. + // + //M*/ + +#ifndef __OPENCV_DESCRIPTOR_HPP__ +#define __OPENCV_DESCRIPTOR_HPP__ + +#include +#include +#include + +#if defined _MSC_VER && _MSC_VER <= 1700 +#include +#else +#include +#endif + +#include +#include + +#include "opencv2/core/utility.hpp" +//#include "opencv2/core/private.hpp" +#include +#include +#include +#include "opencv2/core.hpp" + +/* define data types */ +typedef uint64_t UINT64; +typedef uint32_t UINT32; +typedef uint16_t UINT16; +typedef uint8_t UINT8; + +/* define constants */ +#define UINT64_1 ((UINT64)0x01) +#define UINT32_1 ((UINT32)0x01) + +namespace cv +{ +namespace line_descriptor +{ + +//! @addtogroup line_descriptor +//! @{ + +/** @brief A class to represent a line + +As aformentioned, it is been necessary to design a class that fully stores the information needed to +characterize completely a line and plot it on image it was extracted from, when required. + +*KeyLine* class has been created for such goal; it is mainly inspired to Feature2d's KeyPoint class, +since KeyLine shares some of *KeyPoint*'s fields, even if a part of them assumes a different +meaning, when speaking about lines. In particular: + +- the *class_id* field is used to gather lines extracted from different octaves which refer to + same line inside original image (such lines and the one they represent in original image share + the same *class_id* value) +- the *angle* field represents line's slope with respect to (positive) X axis +- the *pt* field represents line's midpoint +- the *response* field is computed as the ratio between the line's length and maximum between + image's width and height +- the *size* field is the area of the smallest rectangle containing line + +Apart from fields inspired to KeyPoint class, KeyLines stores information about extremes of line in +original image and in octave it was extracted from, about line's length and number of pixels it +covers. + */ +struct CV_EXPORTS KeyLine +{ + public: + /** orientation of the line */ + float angle; + + /** object ID, that can be used to cluster keylines by the line they represent */ + int class_id; + + /** octave (pyramid layer), from which the keyline has been extracted */ + int octave; + + /** coordinates of the middlepoint */ + Point2f pt; + + /** the response, by which the strongest keylines have been selected. + It's represented by the ratio between line's length and maximum between + image's width and height */ + float response; + + /** minimum area containing line */ + float size; + + /** lines's extremes in original image */ + float startPointX; + float startPointY; + float endPointX; + float endPointY; + + /** line's extremes in image it was extracted from */ + float sPointInOctaveX; + float sPointInOctaveY; + float ePointInOctaveX; + float ePointInOctaveY; + + /** the length of line */ + float lineLength; + + /** number of pixels covered by the line */ + int numOfPixels; + + /** Returns the start point of the line in the original image */ + Point2f getStartPoint() const + { + return Point2f(startPointX, startPointY); + } + + /** Returns the end point of the line in the original image */ + Point2f getEndPoint() const + { + return Point2f(endPointX, endPointY); + } + + /** Returns the start point of the line in the octave it was extracted from */ + Point2f getStartPointInOctave() const + { + return Point2f(sPointInOctaveX, sPointInOctaveY); + } + + /** Returns the end point of the line in the octave it was extracted from */ + Point2f getEndPointInOctave() const + { + return Point2f(ePointInOctaveX, ePointInOctaveY); + } + + /** constructor */ + KeyLine() + { + } +}; + +/** @brief Class implements both functionalities for detection of lines and computation of their +binary descriptor. + +Class' interface is mainly based on the ones of classical detectors and extractors, such as +Feature2d's @ref features2d_main and @ref features2d_match. Retrieved information about lines is +stored in line_descriptor::KeyLine objects. + */ +class CV_EXPORTS BinaryDescriptor : public Algorithm +{ + + public: + /** @brief List of BinaryDescriptor parameters: + */ + struct CV_EXPORTS Params + { + /*CV_WRAP*/ + Params(); + + /** the number of image octaves (default = 1) */ + + int numOfOctave_; + + /** the width of band; (default: 7) */ + + int widthOfBand_; + + /** image's reduction ratio in construction of Gaussian pyramids */ + int reductionRatio; + + int ksize_; + + /** read parameters from a FileNode object and store them (struct function) */ + void read( const FileNode& fn ); + + /** store parameters to a FileStorage object (struct function) */ + void write( FileStorage& fs ) const; + + }; + + /** @brief Constructor + + @param parameters configuration parameters BinaryDescriptor::Params + + If no argument is provided, constructor sets default values (see comments in the code snippet in + previous section). Default values are strongly reccomended. + */ + BinaryDescriptor( const BinaryDescriptor::Params ¶meters = BinaryDescriptor::Params() ); + + /** @brief Create a BinaryDescriptor object with default parameters (or with the ones provided) + and return a smart pointer to it + */ + static Ptr createBinaryDescriptor(); + static Ptr createBinaryDescriptor( Params parameters ); + + /** destructor */ + ~BinaryDescriptor(); + + /** @brief Get current number of octaves + */ + int getNumOfOctaves();/*CV_WRAP*/ + /** @brief Set number of octaves + @param octaves number of octaves + */ + void setNumOfOctaves( int octaves );/*CV_WRAP*/ + /** @brief Get current width of bands + */ + int getWidthOfBand();/*CV_WRAP*/ + /** @brief Set width of bands + @param width width of bands + */ + void setWidthOfBand( int width );/*CV_WRAP*/ + /** @brief Get current reduction ratio (used in Gaussian pyramids) + */ + int getReductionRatio();/*CV_WRAP*/ + /** @brief Set reduction ratio (used in Gaussian pyramids) + @param rRatio reduction ratio + */ + void setReductionRatio( int rRatio ); + + /** @brief Read parameters from a FileNode object and store them + + @param fn source FileNode file + */ + virtual void read( const cv::FileNode& fn ); + + /** @brief Store parameters to a FileStorage object + + @param fs output FileStorage file + */ + virtual void write( cv::FileStorage& fs ) const; + + /** @brief Requires line detection + + @param image input image + @param keypoints vector that will store extracted lines for one or more images + @param mask mask matrix to detect only KeyLines of interest + */ + void detect( const Mat& image, CV_OUT std::vector& keypoints, const Mat& mask = Mat() ); + + /** @overload + + @param images input images + @param keylines set of vectors that will store extracted lines for one or more images + @param masks vector of mask matrices to detect only KeyLines of interest from each input image + */ + void detect( const std::vector& images, std::vector >& keylines, const std::vector& masks = + std::vector() ) const; + + /** @brief Requires descriptors computation + + @param image input image + @param keylines vector containing lines for which descriptors must be computed + @param descriptors + @param returnFloatDescr flag (when set to true, original non-binary descriptors are returned) + */ + void compute( const Mat& image, CV_OUT CV_IN_OUT std::vector& keylines, CV_OUT Mat& descriptors, bool returnFloatDescr = false ) const; + + /** @overload + + @param images input images + @param keylines set of vectors containing lines for which descriptors must be computed + @param descriptors + @param returnFloatDescr flag (when set to true, original non-binary descriptors are returned) + */ + void compute( const std::vector& images, std::vector >& keylines, std::vector& descriptors, bool returnFloatDescr = + false ) const; + + /** @brief Return descriptor size + */ + int descriptorSize() const; + + /** @brief Return data type + */ + int descriptorType() const; + + /** returns norm mode */ + /*CV_WRAP*/ + int defaultNorm() const; + + /** @brief Define operator '()' to perform detection of KeyLines and computation of descriptors in a row. + + @param image input image + @param mask mask matrix to select which lines in KeyLines must be accepted among the ones + extracted (used when *keylines* is not empty) + @param keylines vector that contains input lines (when filled, the detection part will be skipped + and input lines will be passed as input to the algorithm computing descriptors) + @param descriptors matrix that will store final descriptors + @param useProvidedKeyLines flag (when set to true, detection phase will be skipped and only + computation of descriptors will be executed, using lines provided in *keylines*) + @param returnFloatDescr flag (when set to true, original non-binary descriptors are returned) + */ + virtual void operator()( InputArray image, InputArray mask, CV_OUT std::vector& keylines, OutputArray descriptors, + bool useProvidedKeyLines = false, bool returnFloatDescr = false ) const; + + protected: + /** implementation of line detection */ + virtual void detectImpl( const Mat& imageSrc, std::vector& keylines, const Mat& mask = Mat() ) const; + + /** implementation of descriptors' computation */ + virtual void computeImpl( const Mat& imageSrc, std::vector& keylines, Mat& descriptors, bool returnFloatDescr, + bool useDetectionData ) const; + + private: + /** struct to represent lines extracted from an octave */ + struct OctaveLine + { + unsigned int octaveCount; //the octave which this line is detected + unsigned int lineIDInOctave; //the line ID in that octave image + unsigned int lineIDInScaleLineVec; //the line ID in Scale line vector + float lineLength; //the length of line in original image scale + }; + + // A 2D line (normal equation parameters). + struct SingleLine + { + //note: rho and theta are based on coordinate origin, i.e. the top-left corner of image + double rho; //unit: pixel length + double theta; //unit: rad + double linePointX; // = rho * cos(theta); + double linePointY; // = rho * sin(theta); + //for EndPoints, the coordinate origin is the top-left corner of image. + double startPointX; + double startPointY; + double endPointX; + double endPointY; + //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. + double direction; + //mean gradient magnitude + double gradientMagnitude; + //mean gray value of pixels in dark side of line + double darkSideGrayValue; + //mean gray value of pixels in light side of line + double lightSideGrayValue; + //the length of line + double lineLength; + //the width of line; + double width; + //number of pixels + int numOfPixels; + //the decriptor of line + std::vector descriptor; + }; + + // Specifies a vector of lines. + typedef std::vector Lines_list; + + struct OctaveSingleLine + { + /*endPoints, the coordinate origin is the top-left corner of the original image. + *startPointX = sPointInOctaveX * (factor)^octaveCount; */ + float startPointX; + float startPointY; + float endPointX; + float endPointY; + //endPoints, the coordinate origin is the top-left corner of the octave image. + float sPointInOctaveX; + float sPointInOctaveY; + float ePointInOctaveX; + float ePointInOctaveY; + //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. + float direction; + //the summation of gradient magnitudes of pixels on lines + float salience; + //the length of line + float lineLength; + //number of pixels + unsigned int numOfPixels; + //the octave which this line is detected + unsigned int octaveCount; + //the decriptor of line + std::vector descriptor; + }; + + struct Pixel + { + unsigned int x; //X coordinate + unsigned int y; //Y coordinate + }; + struct EdgeChains + { + std::vector xCors; //all the x coordinates of edge points + std::vector yCors; //all the y coordinates of edge points + std::vector sId; //the start index of each edge in the coordinate arrays + unsigned int numOfEdges; //the number of edges whose length are larger than minLineLen; numOfEdges < sId.size; + }; + + struct LineChains + { + std::vector xCors; //all the x coordinates of line points + std::vector yCors; //all the y coordinates of line points + std::vector sId; //the start index of each line in the coordinate arrays + unsigned int numOfLines; //the number of lines whose length are larger than minLineLen; numOfLines < sId.size; + }; + + typedef std::list PixelChain; //each edge is a pixel chain + + struct EDLineParam + { + int ksize; + float sigma; + float gradientThreshold; + float anchorThreshold; + int scanIntervals; + int minLineLen; + double lineFitErrThreshold; + }; + + #define RELATIVE_ERROR_FACTOR 100.0 + #define MLN10 2.30258509299404568402 + #define log_gamma(x) ((x)>15.0?log_gamma_windschitl(x):log_gamma_lanczos(x)) + + /** This class is used to detect lines from input image. + * First, edges are extracted from input image following the method presented in Cihan Topal and + * Cuneyt Akinlar's paper:"Edge Drawing: A Heuristic Approach to Robust Real-Time Edge Detection", 2010. + * Then, lines are extracted from the edge image following the method presented in Cuneyt Akinlar and + * Cihan Topal's paper:"EDLines: A real-time line segment detector with a false detection control", 2011 + * PS: The linking step of edge detection has a little bit difference with the Edge drawing algorithm + * described in the paper. The edge chain doesn't stop when the pixel direction is changed. + */ + class EDLineDetector + { + public: + EDLineDetector(); + EDLineDetector( EDLineParam param ); + ~EDLineDetector(); + + /*extract edges from image + *image: In, gray image; + *edges: Out, store the edges, each edge is a pixel chain + *return -1: error happen + */ + int EdgeDrawing( cv::Mat &image, EdgeChains &edgeChains ); + + /*extract lines from image + *image: In, gray image; + *lines: Out, store the extracted lines, + *return -1: error happen + */ + int EDline( cv::Mat &image, LineChains &lines ); + + /** extract line from image, and store them */ + int EDline( cv::Mat &image ); + + cv::Mat dxImg_; //store the dxImg; + + cv::Mat dyImg_; //store the dyImg; + + cv::Mat gImgWO_; //store the gradient image without threshold; + + LineChains lines_; //store the detected line chains; + + //store the line Equation coefficients, vec3=[w1,w2,w3] for line w1*x + w2*y + w3=0; + std::vector > lineEquations_; + + //store the line endpoints, [x1,y1,x2,y3] + std::vector > lineEndpoints_; + + //store the line direction + std::vector lineDirection_; + + //store the line salience, which is the summation of gradients of pixels on line + std::vector lineSalience_; + + // image sizes + unsigned int imageWidth; + unsigned int imageHeight; + + /*The threshold of line fit error; + *If lineFitErr is large than this threshold, then + *the pixel chain is not accepted as a single line segment.*/ + double lineFitErrThreshold_; + + /*the threshold of pixel gradient magnitude. + *Only those pixel whose gradient magnitude are larger than this threshold will be + *taken as possible edge points. Default value is 36*/ + short gradienThreshold_; + + /*If the pixel's gradient value is bigger than both of its neighbors by a + *certain threshold (ANCHOR_THRESHOLD), the pixel is marked to be an anchor. + *Default value is 8*/ + unsigned char anchorThreshold_; + + /*anchor testing can be performed at different scan intervals, i.e., + *every row/column, every second row/column etc. + *Default value is 2*/ + unsigned int scanIntervals_; + + int minLineLen_; //minimal acceptable line length + + private: + void InitEDLine_(); + + /*For an input edge chain, find the best fit line, the default chain length is minLineLen_ + *xCors: In, pointer to the X coordinates of pixel chain; + *yCors: In, pointer to the Y coordinates of pixel chain; + *offsetS:In, start index of this chain in vector; + *lineEquation: Out, [a,b] which are the coefficient of lines y=ax+b(horizontal) or x=ay+b(vertical); + *return: line fit error; -1:error happens; + */ + double LeastSquaresLineFit_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, std::vector &lineEquation ); + + /*For an input pixel chain, find the best fit line. Only do the update based on new points. + *For A*x=v, Least square estimation of x = Inv(A^T * A) * (A^T * v); + *If some new observations are added, i.e, [A; A'] * x = [v; v'], + *then x' = Inv(A^T * A + (A')^T * A') * (A^T * v + (A')^T * v'); + *xCors: In, pointer to the X coordinates of pixel chain; + *yCors: In, pointer to the Y coordinates of pixel chain; + *offsetS:In, start index of this chain in vector; + *newOffsetS: In, start index of extended part; + *offsetE:In, end index of this chain in vector; + *lineEquation: Out, [a,b] which are the coefficient of lines y=ax+b(horizontal) or x=ay+b(vertical); + *return: line fit error; -1:error happens; + */ + double LeastSquaresLineFit_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, unsigned int newOffsetS, unsigned int offsetE, + std::vector &lineEquation ); + + /** Validate line based on the Helmholtz principle, which basically states that + * for a structure to be perceptually meaningful, the expectation of this structure + * by chance must be very low. + */ + bool LineValidation_( unsigned int *xCors, unsigned int *yCors, unsigned int offsetS, unsigned int offsetE, std::vector &lineEquation, + float &direction ); + + bool bValidate_; //flag to decide whether line will be validated + + int ksize_; //the size of Gaussian kernel: ksize X ksize, default value is 5. + + float sigma_; //the sigma of Gaussian kernal, default value is 1.0. + + /*For example, there two edges in the image: + *edge1 = [(7,4), (8,5), (9,6),| (10,7)|, (11, 8), (12,9)] and + *edge2 = [(14,9), (15,10), (16,11), (17,12),| (18, 13)|, (19,14)] ; then we store them as following: + *pFirstPartEdgeX_ = [10, 11, 12, 18, 19];//store the first part of each edge[from middle to end] + *pFirstPartEdgeY_ = [7, 8, 9, 13, 14]; + *pFirstPartEdgeS_ = [0,3,5];// the index of start point of first part of each edge + *pSecondPartEdgeX_ = [10, 9, 8, 7, 18, 17, 16, 15, 14];//store the second part of each edge[from middle to front] + *pSecondPartEdgeY_ = [7, 6, 5, 4, 13, 12, 11, 10, 9];//anchor points(10, 7) and (18, 13) are stored again + *pSecondPartEdgeS_ = [0, 4, 9];// the index of start point of second part of each edge + *This type of storage order is because of the order of edge detection process. + *For each edge, start from one anchor point, first go right, then go left or first go down, then go up*/ + + //store the X coordinates of the first part of the pixels for chains + unsigned int *pFirstPartEdgeX_; + + //store the Y coordinates of the first part of the pixels for chains + unsigned int *pFirstPartEdgeY_; + + //store the start index of every edge chain in the first part arrays + unsigned int *pFirstPartEdgeS_; + + //store the X coordinates of the second part of the pixels for chains + unsigned int *pSecondPartEdgeX_; + + //store the Y coordinates of the second part of the pixels for chains + unsigned int *pSecondPartEdgeY_; + + //store the start index of every edge chain in the second part arrays + unsigned int *pSecondPartEdgeS_; + + //store the X coordinates of anchors + unsigned int *pAnchorX_; + + //store the Y coordinates of anchors + unsigned int *pAnchorY_; + + //edges + cv::Mat edgeImage_; + + cv::Mat gImg_; //store the gradient image; + + cv::Mat dirImg_; //store the direction image + + double logNT_; + + cv::Mat_ ATA; //the previous matrix of A^T * A; + + cv::Mat_ ATV; //the previous vector of A^T * V; + + cv::Mat_ fitMatT; //the matrix used in line fit function; + + cv::Mat_ fitVec; //the vector used in line fit function; + + cv::Mat_ tempMatLineFit; //the matrix used in line fit function; + + cv::Mat_ tempVecLineFit; //the vector used in line fit function; + + /** Compare doubles by relative error. + The resulting rounding error after floating point computations + depend on the specific operations done. The same number computed by + different algorithms could present different rounding errors. For a + useful comparison, an estimation of the relative rounding error + should be considered and compared to a factor times EPS. The factor + should be related to the accumulated rounding error in the chain of + computation. Here, as a simplification, a fixed factor is used. + */ + static int double_equal( double a, double b ) + { + double abs_diff, aa, bb, abs_max; + /* trivial case */ + if( a == b ) + return true; + abs_diff = fabs( a - b ); + aa = fabs( a ); + bb = fabs( b ); + abs_max = aa > bb ? aa : bb; + + /* DBL_MIN is the smallest normalized number, thus, the smallest + number whose relative error is bounded by DBL_EPSILON. For + smaller numbers, the same quantization steps as for DBL_MIN + are used. Then, for smaller numbers, a meaningful "relative" + error should be computed by dividing the difference by DBL_MIN. */ + if( abs_max < DBL_MIN ) + abs_max = DBL_MIN; + + /* equal if relative error <= factor x eps */ + return ( abs_diff / abs_max ) <= ( RELATIVE_ERROR_FACTOR * DBL_EPSILON ); + } + + /** Computes the natural logarithm of the absolute value of + the gamma function of x using the Lanczos approximation. + See http://www.rskey.org/gamma.htm + The formula used is + @f[ + \Gamma(x) = \frac{ \sum_{n=0}^{N} q_n x^n }{ \Pi_{n=0}^{N} (x+n) } + (x+5.5)^{x+0.5} e^{-(x+5.5)} + @f] + so + @f[ + \log\Gamma(x) = \log\left( \sum_{n=0}^{N} q_n x^n \right) + + (x+0.5) \log(x+5.5) - (x+5.5) - \sum_{n=0}^{N} \log(x+n) + @f] + and + q0 = 75122.6331530, + q1 = 80916.6278952, + q2 = 36308.2951477, + q3 = 8687.24529705, + q4 = 1168.92649479, + q5 = 83.8676043424, + q6 = 2.50662827511. + */ + static double log_gamma_lanczos( double x ) + { + static double q[7] = + { 75122.6331530, 80916.6278952, 36308.2951477, 8687.24529705, 1168.92649479, 83.8676043424, 2.50662827511 }; + double a = ( x + 0.5 ) * log( x + 5.5 ) - ( x + 5.5 ); + double b = 0.0; + int n; + for ( n = 0; n < 7; n++ ) + { + a -= log( x + (double) n ); + b += q[n] * pow( x, (double) n ); + } + return a + log( b ); + } + + /** Computes the natural logarithm of the absolute value of + the gamma function of x using Windschitl method. + See http://www.rskey.org/gamma.htm + The formula used is + @f[ + \Gamma(x) = \sqrt{\frac{2\pi}{x}} \left( \frac{x}{e} + \sqrt{ x\sinh(1/x) + \frac{1}{810x^6} } \right)^x + @f] + so + @f[ + \log\Gamma(x) = 0.5\log(2\pi) + (x-0.5)\log(x) - x + + 0.5x\log\left( x\sinh(1/x) + \frac{1}{810x^6} \right). + @f] + This formula is a good approximation when x > 15. + */ + static double log_gamma_windschitl( double x ) + { + return 0.918938533204673 + ( x - 0.5 ) * log( x ) - x + 0.5 * x * log( x * sinh( 1 / x ) + 1 / ( 810.0 * pow( x, 6.0 ) ) ); + } + + /** Computes -log10(NFA). + NFA stands for Number of False Alarms: + @f[ + \mathrm{NFA} = NT \cdot B(n,k,p) + @f] + - NT - number of tests + - B(n,k,p) - tail of binomial distribution with parameters n,k and p: + @f[ + B(n,k,p) = \sum_{j=k}^n + \left(\begin{array}{c}n\\j\end{array}\right) + p^{j} (1-p)^{n-j} + @f] + The value -log10(NFA) is equivalent but more intuitive than NFA: + - -1 corresponds to 10 mean false alarms + - 0 corresponds to 1 mean false alarm + - 1 corresponds to 0.1 mean false alarms + - 2 corresponds to 0.01 mean false alarms + - ... + Used this way, the bigger the value, better the detection, + and a logarithmic scale is used. + @param n,k,p binomial parameters. + @param logNT logarithm of Number of Tests + The computation is based in the gamma function by the following + relation: + @f[ + \left(\begin{array}{c}n\\k\end{array}\right) + = \frac{ \Gamma(n+1) }{ \Gamma(k+1) \cdot \Gamma(n-k+1) }. + @f] + We use efficient algorithms to compute the logarithm of + the gamma function. + To make the computation faster, not all the sum is computed, part + of the terms are neglected based on a bound to the error obtained + (an error of 10% in the result is accepted). + */ + static double nfa( int n, int k, double p, double logNT ) + { + double tolerance = 0.1; /* an error of 10% in the result is accepted */ + double log1term, term, bin_term, mult_term, bin_tail, err, p_term; + int i; + + /* check parameters */ + if( n < 0 || k < 0 || k > n || p <= 0.0 || p >= 1.0 ) + { + std::cout << "nfa: wrong n, k or p values." << std::endl; + exit( 0 ); + } + /* trivial cases */ + if( n == 0 || k == 0 ) + return -logNT; + if( n == k ) + return -logNT - (double) n * log10( p ); + + /* probability term */ + p_term = p / ( 1.0 - p ); + + /* compute the first term of the series */ + /* + binomial_tail(n,k,p) = sum_{i=k}^n bincoef(n,i) * p^i * (1-p)^{n-i} + where bincoef(n,i) are the binomial coefficients. + But + bincoef(n,k) = gamma(n+1) / ( gamma(k+1) * gamma(n-k+1) ). + We use this to compute the first term. Actually the log of it. + */ + log1term = log_gamma( (double) n + 1.0 )- log_gamma( (double ) k + 1.0 )- log_gamma( (double ) ( n - k ) + 1.0 ) ++ (double) k * log( p ) ++ (double) ( n - k ) * log( 1.0 - p ); +term = exp( log1term ); + +/* in some cases no more computations are needed */ +if( double_equal( term, 0.0 ) ) +{ /* the first term is almost zero */ + if( (double) k > (double) n * p ) /* at begin or end of the tail? */ + return -log1term / MLN10 - logNT; /* end: use just the first term */ + else + return -logNT; /* begin: the tail is roughly 1 */ +} + +/* compute more terms if needed */ +bin_tail = term; +for ( i = k + 1; i <= n; i++ ) +{ + /* As + term_i = bincoef(n,i) * p^i * (1-p)^(n-i) + and + bincoef(n,i)/bincoef(n,i-1) = n-i+1 / i, + then, + term_i / term_i-1 = (n-i+1)/i * p/(1-p) + and + term_i = term_i-1 * (n-i+1)/i * p/(1-p). + p/(1-p) is computed only once and stored in 'p_term'. + */ + bin_term = (double) ( n - i + 1 ) / (double) i; + mult_term = bin_term * p_term; + term *= mult_term; + bin_tail += term; + if( bin_term < 1.0 ) + { + /* When bin_term<1 then mult_term_ji. + Then, the error on the binomial tail when truncated at + the i term can be bounded by a geometric series of form + term_i * sum mult_term_i^j. */ + err = term * ( ( 1.0 - pow( mult_term, (double) ( n - i + 1 ) ) ) / ( 1.0 - mult_term ) - 1.0 ); + /* One wants an error at most of tolerance*final_result, or: + tolerance * abs(-log10(bin_tail)-logNT). + Now, the error that can be accepted on bin_tail is + given by tolerance*final_result divided by the derivative + of -log10(x) when x=bin_tail. that is: + tolerance * abs(-log10(bin_tail)-logNT) / (1/bin_tail) + Finally, we truncate the tail if the error is less than: + tolerance * abs(-log10(bin_tail)-logNT) * bin_tail */ + if( err < tolerance * fabs( -log10( bin_tail ) - logNT ) * bin_tail ) + break; + } +} +return -log10( bin_tail ) - logNT; +} +}; + + // Specifies a vector of lines. +typedef std::vector LinesVec; + +// each element in ScaleLines is a vector of lines +// which corresponds the same line detected in different octave images. +typedef std::vector ScaleLines; + +/* compute Gaussian pyramids */ +void computeGaussianPyramid( const Mat& image, const int numOctaves ); + +/* compute Sobel's derivatives */ +void computeSobel( const Mat& image, const int numOctaves ); + +/* conversion of an LBD descriptor to its binary representation */ +unsigned char binaryConversion( float* f1, float* f2 ); + +/* compute LBD descriptors using EDLine extractor */ +int computeLBD( ScaleLines &keyLines, bool useDetectionData = false ); + +/* gathers lines in groups using EDLine extractor. + Each group contains the same line, detected in different octaves */ +int OctaveKeyLines( cv::Mat& image, ScaleLines &keyLines ); + +/* the local gaussian coefficient applied to the orthogonal line direction within each band */ +std::vector gaussCoefL_; + +/* the global gaussian coefficient applied to each row within line support region */ +std::vector gaussCoefG_; + +/* descriptor parameters */ +Params params; + +/* vector of sizes of downsampled and blurred images */ +std::vector images_sizes; + +/*For each octave of image, we define an EDLineDetector, because we can get gradient images (dxImg, dyImg, gImg) + *from the EDLineDetector class without extra computation cost. Another reason is that, if we use + *a single EDLineDetector to detect lines in different octave of images, then we need to allocate and release + *memory for gradient images (dxImg, dyImg, gImg) repeatedly for their varying size*/ +std::vector > edLineVec_; + +/* Sobel's derivatives */ +std::vector dxImg_vector, dyImg_vector; + +/* Gaussian pyramid */ +std::vector octaveImages; + +}; + +/** +Lines extraction methodology +---------------------------- + +The lines extraction methodology described in the following is mainly based on @cite EDL . The +extraction starts with a Gaussian pyramid generated from an original image, downsampled N-1 times, +blurred N times, to obtain N layers (one for each octave), with layer 0 corresponding to input +image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm. + +Differently from EDLine lines extractor used in original article, LSD furnishes information only +about lines extremes; thus, additional information regarding slope and equation of line are computed +via analytic methods. The number of pixels is obtained using *LineIterator*. Extracted lines are +returned in the form of KeyLine objects, but since extraction is based on a method different from +the one used in *BinaryDescriptor* class, data associated to a line's extremes in original image and +in octave it was extracted from, coincide. KeyLine's field *class_id* is used as an index to +indicate the order of extraction of a line inside a single octave. +*/ +class CV_EXPORTS LSDDetector : public Algorithm +{ +public: + +/* constructor */ +/*CV_WRAP*/ +LSDDetector() +{ +} +; + +/** @brief Creates ad LSDDetector object, using smart pointers. + */ +static Ptr createLSDDetector(); + +/** @brief Detect lines inside an image. + +@param image input image +@param keypoints vector that will store extracted lines for one or more images +@param scale scale factor used in pyramids generation +@param numOctaves number of octaves inside pyramid +@param mask mask matrix to detect only KeyLines of interest + */ +void detect( const Mat& image, CV_OUT std::vector& keypoints, int scale, int numOctaves, const Mat& mask = Mat() ); + +/** @overload +@param images input images +@param keylines set of vectors that will store extracted lines for one or more images +@param scale scale factor used in pyramids generation +@param numOctaves number of octaves inside pyramid +@param masks vector of mask matrices to detect only KeyLines of interest from each input image +*/ +void detect( const std::vector& images, std::vector >& keylines, int scale, int numOctaves, +const std::vector& masks = std::vector() ) const; + +private: +/* compute Gaussian pyramid of input image */ +void computeGaussianPyramid( const Mat& image, int numOctaves, int scale ); + +/* implementation of line detection */ +void detectImpl( const Mat& imageSrc, std::vector& keylines, int numOctaves, int scale, const Mat& mask ) const; + +/* matrices for Gaussian pyramids */ +std::vector gaussianPyrs; +}; + +/** @brief furnishes all functionalities for querying a dataset provided by user or internal to +class (that user must, anyway, populate) on the model of @ref features2d_match + + +Once descriptors have been extracted from an image (both they represent lines and points), it +becomes interesting to be able to match a descriptor with another one extracted from a different +image and representing the same line or point, seen from a differente perspective or on a different +scale. In reaching such goal, the main headache is designing an efficient search algorithm to +associate a query descriptor to one extracted from a dataset. In the following, a matching modality +based on *Multi-Index Hashing (MiHashing)* will be described. + +Multi-Index Hashing +------------------- + +The theory described in this section is based on @cite MIH . Given a dataset populated with binary +codes, each code is indexed *m* times into *m* different hash tables, according to *m* substrings it +has been divided into. Thus, given a query code, all the entries close to it at least in one +substring are returned by search as *neighbor candidates*. Returned entries are then checked for +validity by verifying that their full codes are not distant (in Hamming space) more than *r* bits +from query code. In details, each binary code **h** composed of *b* bits is divided into *m* +disjoint substrings \f$\mathbf{h}^{(1)}, ..., \mathbf{h}^{(m)}\f$, each with length +\f$\lfloor b/m \rfloor\f$ or \f$\lceil b/m \rceil\f$ bits. Formally, when two codes **h** and **g** differ +by at the most *r* bits, in at the least one of their *m* substrings they differ by at the most +\f$\lfloor r/m \rfloor\f$ bits. In particular, when \f$||\mathbf{h}-\mathbf{g}||_H \le r\f$ (where \f$||.||_H\f$ +is the Hamming norm), there must exist a substring *k* (with \f$1 \le k \le m\f$) such that + +\f[||\mathbf{h}^{(k)} - \mathbf{g}^{(k)}||_H \le \left\lfloor \frac{r}{m} \right\rfloor .\f] + +That means that if Hamming distance between each of the *m* substring is strictly greater than +\f$\lfloor r/m \rfloor\f$, then \f$||\mathbf{h}-\mathbf{g}||_H\f$ must be larger that *r* and that is a +contradiction. If the codes in dataset are divided into *m* substrings, then *m* tables will be +built. Given a query **q** with substrings \f$\{\mathbf{q}^{(i)}\}^m_{i=1}\f$, *i*-th hash table is +searched for entries distant at the most \f$\lfloor r/m \rfloor\f$ from \f$\mathbf{q}^{(i)}\f$ and a set of +candidates \f$\mathcal{N}_i(\mathbf{q})\f$ is obtained. The union of sets +\f$\mathcal{N}(\mathbf{q}) = \bigcup_i \mathcal{N}_i(\mathbf{q})\f$ is a superset of the *r*-neighbors +of **q**. Then, last step of algorithm is computing the Hamming distance between **q** and each +element in \f$\mathcal{N}(\mathbf{q})\f$, deleting the codes that are distant more that *r* from **q**. +*/ +class CV_EXPORTS BinaryDescriptorMatcher : public Algorithm +{ + +public: +/** @brief For every input query descriptor, retrieve the best matching one from a dataset provided from user +or from the one internal to class + +@param queryDescriptors query descriptors +@param trainDescriptors dataset of descriptors furnished by user +@param matches vector to host retrieved matches +@param mask mask to select which input descriptors must be matched to one in dataset + */ +void match( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector& matches, const Mat& mask = Mat() ) const; + +/** @overload +@param queryDescriptors query descriptors +@param matches vector to host retrieved matches +@param masks vector of masks to select which input descriptors must be matched to one in dataset +(the *i*-th mask in vector indicates whether each input query can be matched with descriptors in +dataset relative to *i*-th image) +*/ +void match( const Mat& queryDescriptors, std::vector& matches, const std::vector& masks = std::vector() ); + +/** @brief For every input query descriptor, retrieve the best *k* matching ones from a dataset provided from +user or from the one internal to class + +@param queryDescriptors query descriptors +@param trainDescriptors dataset of descriptors furnished by user +@param matches vector to host retrieved matches +@param k number of the closest descriptors to be returned for every input query +@param mask mask to select which input descriptors must be matched to ones in dataset +@param compactResult flag to obtain a compact result (if true, a vector that doesn't contain any +matches for a given query is not inserted in final result) + */ +void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, int k, const Mat& mask = Mat(), +bool compactResult = false ) const; + +/** @overload +@param queryDescriptors query descriptors +@param matches vector to host retrieved matches +@param k number of the closest descriptors to be returned for every input query +@param masks vector of masks to select which input descriptors must be matched to ones in dataset +(the *i*-th mask in vector indicates whether each input query can be matched with descriptors in +dataset relative to *i*-th image) +@param compactResult flag to obtain a compact result (if true, a vector that doesn't contain any +matches for a given query is not inserted in final result) +*/ +void knnMatch( const Mat& queryDescriptors, std::vector >& matches, int k, const std::vector& masks = std::vector(), +bool compactResult = false ); + +/** @brief For every input query descriptor, retrieve, from a dataset provided from user or from the one +internal to class, all the descriptors that are not further than *maxDist* from input query + +@param queryDescriptors query descriptors +@param trainDescriptors dataset of descriptors furnished by user +@param matches vector to host retrieved matches +@param maxDistance search radius +@param mask mask to select which input descriptors must be matched to ones in dataset +@param compactResult flag to obtain a compact result (if true, a vector that doesn't contain any +matches for a given query is not inserted in final result) + */ +void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, std::vector >& matches, float maxDistance, +const Mat& mask = Mat(), bool compactResult = false ) const; + +/** @overload +@param queryDescriptors query descriptors +@param matches vector to host retrieved matches +@param maxDistance search radius +@param masks vector of masks to select which input descriptors must be matched to ones in dataset +(the *i*-th mask in vector indicates whether each input query can be matched with descriptors in +dataset relative to *i*-th image) +@param compactResult flag to obtain a compact result (if true, a vector that doesn't contain any +matches for a given query is not inserted in final result) +*/ +void radiusMatch( const Mat& queryDescriptors, std::vector >& matches, float maxDistance, const std::vector& masks = +std::vector(), +bool compactResult = false ); + +/** @brief Store locally new descriptors to be inserted in dataset, without updating dataset. + +@param descriptors matrices containing descriptors to be inserted into dataset + +@note Each matrix *i* in **descriptors** should contain descriptors relative to lines extracted from +*i*-th image. + */ +void add( const std::vector& descriptors ); + +/** @brief Update dataset by inserting into it all descriptors that were stored locally by *add* function. + +@note Every time this function is invoked, current dataset is deleted and locally stored descriptors +are inserted into dataset. The locally stored copy of just inserted descriptors is then removed. + */ +void train(); + +/** @brief Create a BinaryDescriptorMatcher object and return a smart pointer to it. + */ +static Ptr createBinaryDescriptorMatcher(); + +/** @brief Clear dataset and internal data + */ +void clear(); + +/** @brief Constructor. + +The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries. + */ +BinaryDescriptorMatcher(); + +/** destructor */ +~BinaryDescriptorMatcher() +{ +} + +private: +class BucketGroup +{ + +public: +/** constructor */ +BucketGroup(bool needAllocateGroup = true); + +/** destructor */ +~BucketGroup(); + +/** insert data into the bucket */ +void insert( int subindex, UINT32 data ); + +/** perform a query to the bucket */ +UINT32* query( int subindex, int *size ); + +/** utility functions */ +void insert_value( std::vector& vec, int index, UINT32 data ); +void push_value( std::vector& vec, UINT32 Data ); + +/** data fields */ +UINT32 empty; +std::vector group; + + +}; + +class SparseHashtable +{ + +private: + +/** Maximum bits per key before folding the table */ +static const int MAX_B; + +/** Bins (each bin is an Array object for duplicates of the same key) */ +std::vector table; + +public: + +/** constructor */ +SparseHashtable(); + +/** destructor */ +~SparseHashtable(); + +/** initializer */ +int init( int _b ); + +/** insert data */ +void insert( UINT64 index, UINT32 data ); + +/** query data */ +UINT32* query( UINT64 index, int* size ); + +/** Bits per index */ +int b; + +/** Number of bins */ +UINT64 size; + +}; + +/** class defining a sequence of bits */ +class bitarray +{ + +public: +/** pointer to bits sequence and sequence's length */ +UINT32 *arr; +UINT32 length; + +/** constructor setting default values */ +bitarray() +{ +arr = NULL; +length = 0; +} + +/** constructor setting sequence's length */ +bitarray( UINT64 _bits ) +{ +arr = NULL; +init( _bits ); +} + +/** initializer of private fields */ +void init( UINT64 _bits ) +{ +if( arr ) +delete[] arr; +length = (UINT32) ceil( _bits / 32.00 ); +arr = new UINT32[length]; +erase(); +} + +/** destructor */ +~bitarray() +{ +if( arr ) +delete[] arr; +} + +inline void flip( UINT64 index ) +{ +arr[index >> 5] ^= ( (UINT32) 0x01 ) << ( index % 32 ); +} + +inline void set( UINT64 index ) +{ +arr[index >> 5] |= ( (UINT32) 0x01 ) << ( index % 32 ); +} + +inline UINT8 get( UINT64 index ) +{ +return ( arr[index >> 5] & ( ( (UINT32) 0x01 ) << ( index % 32 ) ) ) != 0; +} + +/** reserve menory for an UINT32 */ +inline void erase() +{ +memset( arr, 0, sizeof(UINT32) * length ); +} + +}; + +class Mihasher +{ + +public: +/** Bits per code */ +int B; + +/** B/8 */ +int B_over_8; + +/** Bits per chunk (must be less than 64) */ +int b; + +/** Number of chunks */ +int m; + +/** Number of chunks with b bits (have 1 bit more than others) */ +int mplus; + +/** Maximum hamming search radius (we use B/2 by default) */ +int D; + +/** Maximum hamming search radius per substring */ +int d; + +/** Maximum results to return */ +int K; + +/** Number of codes */ +UINT64 N; + +/** Table of original full-length codes */ +cv::Mat codes; + +/** Counter for eliminating duplicate results (it is not thread safe) */ +Ptr counter; + +/** Array of m hashtables */ +std::vector H; + +/** Volume of a b-bit Hamming ball with radius s (for s = 0 to d) */ +std::vector xornum; + +/** Used within generation of binary codes at a certain Hamming distance */ +int power[100]; + +/** constructor */ +Mihasher(); + +/** desctructor */ +~Mihasher(); + +/** constructor 2 */ +Mihasher( int B, int m ); + +/** K setter */ +void setK( int K ); + +/** populate tables */ +void populate( cv::Mat & codes, UINT32 N, int dim1codes ); + +/** execute a batch query */ +void batchquery( UINT32 * results, UINT32 *numres/*, qstat *stats*/, const cv::Mat & q, UINT32 numq, int dim1queries ); + +private: + +/** execute a single query */ +void query( UINT32 * results, UINT32* numres/*, qstat *stats*/, UINT8 *q, UINT64 * chunks, UINT32 * res ); +}; + +/** retrieve Hamming distances */ +void checkKDistances( UINT32 * numres, int k, std::vector& k_distances, int row, int string_length ) const; + +/** matrix to store new descriptors */ +Mat descriptorsMat; + +/** map storing where each bunch of descriptors benins in DS */ +std::map indexesMap; + +/** internal MiHaser representing dataset */ +Ptr dataset; + +/** index from which next added descriptors' bunch must begin */ +int nextAddedIndex; + +/** number of images whose descriptors are stored in DS */ +int numImages; + +/** number of descriptors in dataset */ +int descrInDS; + +}; + +/* -------------------------------------------------------------------------------------------- + UTILITY FUNCTIONS + -------------------------------------------------------------------------------------------- */ + +/** struct for drawing options */ +struct CV_EXPORTS DrawLinesMatchesFlags +{ +enum +{ +DEFAULT = 0, //!< Output image matrix will be created (Mat::create), + //!< i.e. existing memory of output image may be reused. + //!< Two source images, matches, and single keylines + //!< will be drawn. +DRAW_OVER_OUTIMG = 1,//!< Output image matrix will not be +//!< created (using Mat::create). Matches will be drawn +//!< on existing content of output image. +NOT_DRAW_SINGLE_LINES = 2//!< Single keylines will not be drawn. +}; +}; + +/** @brief Draws the found matches of keylines from two images. + +@param img1 first image +@param keylines1 keylines extracted from first image +@param img2 second image +@param keylines2 keylines extracted from second image +@param matches1to2 vector of matches +@param outImg output matrix to draw on +@param matchColor drawing color for matches (chosen randomly in case of default value) +@param singleLineColor drawing color for keylines (chosen randomly in case of default value) +@param matchesMask mask to indicate which matches must be drawn +@param flags drawing flags, see DrawLinesMatchesFlags + +@note If both *matchColor* and *singleLineColor* are set to their default values, function draws +matched lines and line connecting them with same color + */ +CV_EXPORTS void drawLineMatches( const Mat& img1, const std::vector& keylines1, const Mat& img2, const std::vector& keylines2, + const std::vector& matches1to2, Mat& outImg, const Scalar& matchColor = Scalar::all( -1 ), + const Scalar& singleLineColor = Scalar::all( -1 ), const std::vector& matchesMask = std::vector(), + int flags = DrawLinesMatchesFlags::DEFAULT ); + +/** @brief Draws keylines. + +@param image input image +@param keylines keylines to be drawn +@param outImage output image to draw on +@param color color of lines to be drawn (if set to defaul value, color is chosen randomly) +@param flags drawing flags + */ +CV_EXPORTS void drawKeylines( const Mat& image, const std::vector& keylines, Mat& outImage, const Scalar& color = Scalar::all( -1 ), + int flags = DrawLinesMatchesFlags::DEFAULT ); + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/ml.hpp b/thirdparty/linux/include/opencv2/ml.hpp new file mode 100644 index 0000000..99f5883 --- /dev/null +++ b/thirdparty/linux/include/opencv2/ml.hpp @@ -0,0 +1,1690 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_ML_HPP +#define OPENCV_ML_HPP + +#ifdef __cplusplus +# include "opencv2/core.hpp" +#endif + +#ifdef __cplusplus + +#include +#include +#include + +/** + @defgroup ml Machine Learning + + The Machine Learning Library (MLL) is a set of classes and functions for statistical + classification, regression, and clustering of data. + + Most of the classification and regression algorithms are implemented as C++ classes. As the + algorithms have different sets of features (like an ability to handle missing measurements or + categorical input variables), there is a little common ground between the classes. This common + ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from. + + See detailed overview here: @ref ml_intro. + */ + +namespace cv +{ + +namespace ml +{ + +//! @addtogroup ml +//! @{ + +/** @brief Variable types */ +enum VariableTypes +{ + VAR_NUMERICAL =0, //!< same as VAR_ORDERED + VAR_ORDERED =0, //!< ordered variables + VAR_CATEGORICAL =1 //!< categorical variables +}; + +/** @brief %Error types */ +enum ErrorTypes +{ + TEST_ERROR = 0, + TRAIN_ERROR = 1 +}; + +/** @brief Sample types */ +enum SampleTypes +{ + ROW_SAMPLE = 0, //!< each training sample is a row of samples + COL_SAMPLE = 1 //!< each training sample occupies a column of samples +}; + +/** @brief The structure represents the logarithmic grid range of statmodel parameters. + +It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate +being computed by cross-validation. + */ +class CV_EXPORTS ParamGrid +{ +public: + /** @brief Default constructor */ + ParamGrid(); + /** @brief Constructor with parameters */ + ParamGrid(double _minVal, double _maxVal, double _logStep); + + double minVal; //!< Minimum value of the statmodel parameter. Default value is 0. + double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0. + /** @brief Logarithmic step for iterating the statmodel parameter. + + The grid determines the following iteration sequence of the statmodel parameter values: + \f[(minVal, minVal*step, minVal*{step}^2, \dots, minVal*{logStep}^n),\f] + where \f$n\f$ is the maximal index satisfying + \f[\texttt{minVal} * \texttt{logStep} ^n < \texttt{maxVal}\f] + The grid is logarithmic, so logStep must always be greater then 1. Default value is 1. + */ + double logStep; +}; + +/** @brief Class encapsulating training data. + +Please note that the class only specifies the interface of training data, but not implementation. +All the statistical model classes in _ml_ module accepts Ptr\ as parameter. In other +words, you can create your own class derived from TrainData and pass smart pointer to the instance +of this class into StatModel::train. + +@sa @ref ml_intro_data + */ +class CV_EXPORTS_W TrainData +{ +public: + static inline float missingValue() { return FLT_MAX; } + virtual ~TrainData(); + + CV_WRAP virtual int getLayout() const = 0; + CV_WRAP virtual int getNTrainSamples() const = 0; + CV_WRAP virtual int getNTestSamples() const = 0; + CV_WRAP virtual int getNSamples() const = 0; + CV_WRAP virtual int getNVars() const = 0; + CV_WRAP virtual int getNAllVars() const = 0; + + CV_WRAP virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0; + CV_WRAP virtual Mat getSamples() const = 0; + CV_WRAP virtual Mat getMissing() const = 0; + + /** @brief Returns matrix of train samples + + @param layout The requested layout. If it's different from the initial one, the matrix is + transposed. See ml::SampleTypes. + @param compressSamples if true, the function returns only the training samples (specified by + sampleIdx) + @param compressVars if true, the function returns the shorter training samples, containing only + the active variables. + + In current implementation the function tries to avoid physical data copying and returns the + matrix stored inside TrainData (unless the transposition or compression is needed). + */ + CV_WRAP virtual Mat getTrainSamples(int layout=ROW_SAMPLE, + bool compressSamples=true, + bool compressVars=true) const = 0; + + /** @brief Returns the vector of responses + + The function returns ordered or the original categorical responses. Usually it's used in + regression algorithms. + */ + CV_WRAP virtual Mat getTrainResponses() const = 0; + + /** @brief Returns the vector of normalized categorical responses + + The function returns vector of responses. Each response is integer from `0` to `-1`. The actual label value can be retrieved then from the class label vector, see + TrainData::getClassLabels. + */ + CV_WRAP virtual Mat getTrainNormCatResponses() const = 0; + CV_WRAP virtual Mat getTestResponses() const = 0; + CV_WRAP virtual Mat getTestNormCatResponses() const = 0; + CV_WRAP virtual Mat getResponses() const = 0; + CV_WRAP virtual Mat getNormCatResponses() const = 0; + CV_WRAP virtual Mat getSampleWeights() const = 0; + CV_WRAP virtual Mat getTrainSampleWeights() const = 0; + CV_WRAP virtual Mat getTestSampleWeights() const = 0; + CV_WRAP virtual Mat getVarIdx() const = 0; + CV_WRAP virtual Mat getVarType() const = 0; + CV_WRAP Mat getVarSymbolFlags() const; + CV_WRAP virtual int getResponseType() const = 0; + CV_WRAP virtual Mat getTrainSampleIdx() const = 0; + CV_WRAP virtual Mat getTestSampleIdx() const = 0; + CV_WRAP virtual void getValues(int vi, InputArray sidx, float* values) const = 0; + virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0; + CV_WRAP virtual Mat getDefaultSubstValues() const = 0; + + CV_WRAP virtual int getCatCount(int vi) const = 0; + + /** @brief Returns the vector of class labels + + The function returns vector of unique labels occurred in the responses. + */ + CV_WRAP virtual Mat getClassLabels() const = 0; + + CV_WRAP virtual Mat getCatOfs() const = 0; + CV_WRAP virtual Mat getCatMap() const = 0; + + /** @brief Splits the training data into the training and test parts + @sa TrainData::setTrainTestSplitRatio + */ + CV_WRAP virtual void setTrainTestSplit(int count, bool shuffle=true) = 0; + + /** @brief Splits the training data into the training and test parts + + The function selects a subset of specified relative size and then returns it as the training + set. If the function is not called, all the data is used for training. Please, note that for + each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test + subset can be retrieved and processed as well. + @sa TrainData::setTrainTestSplit + */ + CV_WRAP virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0; + CV_WRAP virtual void shuffleTrainTest() = 0; + + /** @brief Returns matrix of test samples */ + CV_WRAP Mat getTestSamples() const; + + /** @brief Returns vector of symbolic names captured in loadFromCSV() */ + CV_WRAP void getNames(std::vector& names) const; + + CV_WRAP static Mat getSubVector(const Mat& vec, const Mat& idx); + + /** @brief Reads the dataset from a .csv file and returns the ready-to-use training data. + + @param filename The input file name + @param headerLineCount The number of lines in the beginning to skip; besides the header, the + function also skips empty lines and lines staring with `#` + @param responseStartIdx Index of the first output variable. If -1, the function considers the + last variable as the response + @param responseEndIdx Index of the last output variable + 1. If -1, then there is single + response variable at responseStartIdx. + @param varTypeSpec The optional text string that specifies the variables' types. It has the + format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2` + (inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are + considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]` + should cover all the variables. If varTypeSpec is not specified, then algorithm uses the + following rules: + - all input variables are considered ordered by default. If some column contains has non- + numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding + variable is considered categorical. + - if there are several output variables, they are all considered as ordered. Error is + reported when non-numerical values are used. + - if there is a single output variable, then if its values are non-numerical or are all + integers, then it's considered categorical. Otherwise, it's considered ordered. + @param delimiter The character used to separate values in each line. + @param missch The character used to specify missing measurements. It should not be a digit. + Although it's a non-numerical value, it surely does not affect the decision of whether the + variable ordered or categorical. + @note If the dataset only contains input variables and no responses, use responseStartIdx = -2 + and responseEndIdx = 0. The output variables vector will just contain zeros. + */ + static Ptr loadFromCSV(const String& filename, + int headerLineCount, + int responseStartIdx=-1, + int responseEndIdx=-1, + const String& varTypeSpec=String(), + char delimiter=',', + char missch='?'); + + /** @brief Creates training data from in-memory arrays. + + @param samples matrix of samples. It should have CV_32F type. + @param layout see ml::SampleTypes. + @param responses matrix of responses. If the responses are scalar, they should be stored as a + single row or as a single column. The matrix should have type CV_32F or CV_32S (in the + former case the responses are considered as ordered by default; in the latter case - as + categorical) + @param varIdx vector specifying which variables to use for training. It can be an integer vector + (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of + active variables. + @param sampleIdx vector specifying which samples to use for training. It can be an integer + vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask + of training samples. + @param sampleWeights optional vector with weights for each sample. It should have CV_32F type. + @param varType optional vector of type CV_8U and size ` + + `, containing types of each input and output variable. See + ml::VariableTypes. + */ + CV_WRAP static Ptr create(InputArray samples, int layout, InputArray responses, + InputArray varIdx=noArray(), InputArray sampleIdx=noArray(), + InputArray sampleWeights=noArray(), InputArray varType=noArray()); +}; + +/** @brief Base class for statistical models in OpenCV ML. + */ +class CV_EXPORTS_W StatModel : public Algorithm +{ +public: + /** Predict options */ + enum Flags { + UPDATE_MODEL = 1, + RAW_OUTPUT=1, //!< makes the method return the raw results (the sum), not the class label + COMPRESSED_INPUT=2, + PREPROCESSED_INPUT=4 + }; + + /** @brief Returns the number of variables in training samples */ + CV_WRAP virtual int getVarCount() const = 0; + + CV_WRAP virtual bool empty() const; + + /** @brief Returns true if the model is trained */ + CV_WRAP virtual bool isTrained() const = 0; + /** @brief Returns true if the model is classifier */ + CV_WRAP virtual bool isClassifier() const = 0; + + /** @brief Trains the statistical model + + @param trainData training data that can be loaded from file using TrainData::loadFromCSV or + created with TrainData::create. + @param flags optional flags, depending on the model. Some of the models can be updated with the + new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP). + */ + CV_WRAP virtual bool train( const Ptr& trainData, int flags=0 ); + + /** @brief Trains the statistical model + + @param samples training samples + @param layout See ml::SampleTypes. + @param responses vector of responses associated with the training samples. + */ + CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses ); + + /** @brief Computes error on the training or test dataset + + @param data the training data + @param test if true, the error is computed over the test subset of the data, otherwise it's + computed over the training subset of the data. Please note that if you loaded a completely + different dataset to evaluate already trained classifier, you will probably want not to set + the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so + that the error is computed for the whole new set. Yes, this sounds a bit confusing. + @param resp the optional output responses. + + The method uses StatModel::predict to compute the error. For regression models the error is + computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%). + */ + CV_WRAP virtual float calcError( const Ptr& data, bool test, OutputArray resp ) const; + + /** @brief Predicts response(s) for the provided sample(s) + + @param samples The input samples, floating-point matrix + @param results The optional output matrix of results. + @param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags. + */ + CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0; + + /** @brief Create and train model with default parameters + + The class must implement static `create()` method with no parameters or with all default parameter values + */ + template static Ptr<_Tp> train(const Ptr& data, int flags=0) + { + Ptr<_Tp> model = _Tp::create(); + return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>(); + } +}; + +/****************************************************************************************\ +* Normal Bayes Classifier * +\****************************************************************************************/ + +/** @brief Bayes classifier for normally distributed data. + +@sa @ref ml_intro_bayes + */ +class CV_EXPORTS_W NormalBayesClassifier : public StatModel +{ +public: + /** @brief Predicts the response for sample(s). + + The method estimates the most probable classes for input vectors. Input vectors (one or more) + are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one + output vector outputs. The predicted class for a single input vector is returned by the method. + The vector outputProbs contains the output probabilities corresponding to each element of + result. + */ + CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs, + OutputArray outputProbs, int flags=0 ) const = 0; + + /** Creates empty model + Use StatModel::train to train the model after creation. */ + CV_WRAP static Ptr create(); +}; + +/****************************************************************************************\ +* K-Nearest Neighbour Classifier * +\****************************************************************************************/ + +/** @brief The class implements K-Nearest Neighbors model + +@sa @ref ml_intro_knn + */ +class CV_EXPORTS_W KNearest : public StatModel +{ +public: + + /** Default number of neighbors to use in predict method. */ + /** @see setDefaultK */ + CV_WRAP virtual int getDefaultK() const = 0; + /** @copybrief getDefaultK @see getDefaultK */ + CV_WRAP virtual void setDefaultK(int val) = 0; + + /** Whether classification or regression model should be trained. */ + /** @see setIsClassifier */ + CV_WRAP virtual bool getIsClassifier() const = 0; + /** @copybrief getIsClassifier @see getIsClassifier */ + CV_WRAP virtual void setIsClassifier(bool val) = 0; + + /** Parameter for KDTree implementation. */ + /** @see setEmax */ + CV_WRAP virtual int getEmax() const = 0; + /** @copybrief getEmax @see getEmax */ + CV_WRAP virtual void setEmax(int val) = 0; + + /** %Algorithm type, one of KNearest::Types. */ + /** @see setAlgorithmType */ + CV_WRAP virtual int getAlgorithmType() const = 0; + /** @copybrief getAlgorithmType @see getAlgorithmType */ + CV_WRAP virtual void setAlgorithmType(int val) = 0; + + /** @brief Finds the neighbors and predicts responses for input vectors. + + @param samples Input samples stored by rows. It is a single-precision floating-point matrix of + ` * k` size. + @param k Number of used nearest neighbors. Should be greater than 1. + @param results Vector with results of prediction (regression or classification) for each input + sample. It is a single-precision floating-point vector with `` elements. + @param neighborResponses Optional output values for corresponding neighbors. It is a single- + precision floating-point matrix of ` * k` size. + @param dist Optional output distances from the input vectors to the corresponding neighbors. It + is a single-precision floating-point matrix of ` * k` size. + + For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. + In case of regression, the predicted result is a mean value of the particular vector's neighbor + responses. In case of classification, the class is determined by voting. + + For each input vector, the neighbors are sorted by their distances to the vector. + + In case of C++ interface you can use output pointers to empty matrices and the function will + allocate memory itself. + + If only a single input vector is passed, all output matrices are optional and the predicted + value is returned by the method. + + The function is parallelized with the TBB library. + */ + CV_WRAP virtual float findNearest( InputArray samples, int k, + OutputArray results, + OutputArray neighborResponses=noArray(), + OutputArray dist=noArray() ) const = 0; + + /** @brief Implementations of KNearest algorithm + */ + enum Types + { + BRUTE_FORCE=1, + KDTREE=2 + }; + + /** @brief Creates the empty model + + The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method. + */ + CV_WRAP static Ptr create(); +}; + +/****************************************************************************************\ +* Support Vector Machines * +\****************************************************************************************/ + +/** @brief Support Vector Machines. + +@sa @ref ml_intro_svm + */ +class CV_EXPORTS_W SVM : public StatModel +{ +public: + + class CV_EXPORTS Kernel : public Algorithm + { + public: + virtual int getType() const = 0; + virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0; + }; + + /** Type of a %SVM formulation. + See SVM::Types. Default value is SVM::C_SVC. */ + /** @see setType */ + CV_WRAP virtual int getType() const = 0; + /** @copybrief getType @see getType */ + CV_WRAP virtual void setType(int val) = 0; + + /** Parameter \f$\gamma\f$ of a kernel function. + For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */ + /** @see setGamma */ + CV_WRAP virtual double getGamma() const = 0; + /** @copybrief getGamma @see getGamma */ + CV_WRAP virtual void setGamma(double val) = 0; + + /** Parameter _coef0_ of a kernel function. + For SVM::POLY or SVM::SIGMOID. Default value is 0.*/ + /** @see setCoef0 */ + CV_WRAP virtual double getCoef0() const = 0; + /** @copybrief getCoef0 @see getCoef0 */ + CV_WRAP virtual void setCoef0(double val) = 0; + + /** Parameter _degree_ of a kernel function. + For SVM::POLY. Default value is 0. */ + /** @see setDegree */ + CV_WRAP virtual double getDegree() const = 0; + /** @copybrief getDegree @see getDegree */ + CV_WRAP virtual void setDegree(double val) = 0; + + /** Parameter _C_ of a %SVM optimization problem. + For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */ + /** @see setC */ + CV_WRAP virtual double getC() const = 0; + /** @copybrief getC @see getC */ + CV_WRAP virtual void setC(double val) = 0; + + /** Parameter \f$\nu\f$ of a %SVM optimization problem. + For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */ + /** @see setNu */ + CV_WRAP virtual double getNu() const = 0; + /** @copybrief getNu @see getNu */ + CV_WRAP virtual void setNu(double val) = 0; + + /** Parameter \f$\epsilon\f$ of a %SVM optimization problem. + For SVM::EPS_SVR. Default value is 0. */ + /** @see setP */ + CV_WRAP virtual double getP() const = 0; + /** @copybrief getP @see getP */ + CV_WRAP virtual void setP(double val) = 0; + + /** Optional weights in the SVM::C_SVC problem, assigned to particular classes. + They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus + these weights affect the misclassification penalty for different classes. The larger weight, + the larger penalty on misclassification of data from the corresponding class. Default value is + empty Mat. */ + /** @see setClassWeights */ + CV_WRAP virtual cv::Mat getClassWeights() const = 0; + /** @copybrief getClassWeights @see getClassWeights */ + CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0; + + /** Termination criteria of the iterative %SVM training procedure which solves a partial + case of constrained quadratic optimization problem. + You can specify tolerance and/or the maximum number of iterations. Default value is + `TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */ + /** @see setTermCriteria */ + CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0; + + /** Type of a %SVM kernel. + See SVM::KernelTypes. Default value is SVM::RBF. */ + CV_WRAP virtual int getKernelType() const = 0; + + /** Initialize with one of predefined kernels. + See SVM::KernelTypes. */ + CV_WRAP virtual void setKernel(int kernelType) = 0; + + /** Initialize with custom kernel. + See SVM::Kernel class for implementation details */ + virtual void setCustomKernel(const Ptr &_kernel) = 0; + + //! %SVM type + enum Types { + /** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows + imperfect separation of classes with penalty multiplier C for outliers. */ + C_SVC=100, + /** \f$\nu\f$-Support Vector Classification. n-class classification with possible + imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother + the decision boundary) is used instead of C. */ + NU_SVC=101, + /** Distribution Estimation (One-class %SVM). All the training data are from + the same class, %SVM builds a boundary that separates the class from the rest of the feature + space. */ + ONE_CLASS=102, + /** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors + from the training set and the fitting hyper-plane must be less than p. For outliers the + penalty multiplier C is used. */ + EPS_SVR=103, + /** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p. + See @cite LibSVM for details. */ + NU_SVR=104 + }; + + /** @brief %SVM kernel type + + A comparison of different kernels on the following 2D test case with four classes. Four + SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three + different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score. + Bright means max-score \> 0, dark means max-score \< 0. + ![image](pics/SVM_Comparison.png) + */ + enum KernelTypes { + /** Returned by SVM::getKernelType in case when custom kernel has been set */ + CUSTOM=-1, + /** Linear kernel. No mapping is done, linear discrimination (or regression) is + done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$. */ + LINEAR=0, + /** Polynomial kernel: + \f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. */ + POLY=1, + /** Radial basis function (RBF), a good choice in most cases. + \f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. */ + RBF=2, + /** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. */ + SIGMOID=3, + /** Exponential Chi2 kernel, similar to the RBF kernel: + \f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. */ + CHI2=4, + /** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. */ + INTER=5 + }; + + //! %SVM params type + enum ParamTypes { + C=0, + GAMMA=1, + P=2, + NU=3, + COEF=4, + DEGREE=5 + }; + + /** @brief Trains an %SVM with optimal parameters. + + @param data the training data that can be constructed using TrainData::create or + TrainData::loadFromCSV. + @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One + subset is used to test the model, the others form the train set. So, the %SVM algorithm is + executed kFold times. + @param Cgrid grid for C + @param gammaGrid grid for gamma + @param pGrid grid for p + @param nuGrid grid for nu + @param coeffGrid grid for coeff + @param degreeGrid grid for degree + @param balanced If true and the problem is 2-class classification then the method creates more + balanced cross-validation subsets that is proportions between classes in subsets are close + to such proportion in the whole train dataset. + + The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, + nu, coef0, degree. Parameters are considered optimal when the cross-validation + estimate of the test set error is minimal. + + If there is no need to optimize a parameter, the corresponding grid step should be set to any + value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step + = 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value + `Gamma` is taken for gamma. + + And, finally, if the optimization in a parameter is required but the corresponding grid is + unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for + gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`. + + This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the + regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and + the usual %SVM with parameters specified in params is executed. + */ + virtual bool trainAuto( const Ptr& data, int kFold = 10, + ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C), + ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA), + ParamGrid pGrid = SVM::getDefaultGrid(SVM::P), + ParamGrid nuGrid = SVM::getDefaultGrid(SVM::NU), + ParamGrid coeffGrid = SVM::getDefaultGrid(SVM::COEF), + ParamGrid degreeGrid = SVM::getDefaultGrid(SVM::DEGREE), + bool balanced=false) = 0; + + /** @brief Retrieves all the support vectors + + The method returns all the support vectors as a floating-point matrix, where support vectors are + stored as matrix rows. + */ + CV_WRAP virtual Mat getSupportVectors() const = 0; + + /** @brief Retrieves all the uncompressed support vectors of a linear %SVM + + The method returns all the uncompressed support vectors of a linear %SVM that the compressed + support vector, used for prediction, was derived from. They are returned in a floating-point + matrix, where the support vectors are stored as matrix rows. + */ + CV_WRAP Mat getUncompressedSupportVectors() const; + + /** @brief Retrieves the decision function + + @param i the index of the decision function. If the problem solved is regression, 1-class or + 2-class classification, then there will be just one decision function and the index should + always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$ + decision functions. + @param alpha the optional output vector for weights, corresponding to different support vectors. + In the case of linear %SVM all the alpha's will be 1's. + @param svidx the optional output vector of indices of support vectors within the matrix of + support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear + %SVM each decision function consists of a single "compressed" support vector. + + The method returns rho parameter of the decision function, a scalar subtracted from the weighted + sum of kernel responses. + */ + CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0; + + /** @brief Generates a grid for %SVM parameters. + + @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is + generated for the parameter with this ID. + + The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be + passed to the function SVM::trainAuto. + */ + static ParamGrid getDefaultGrid( int param_id ); + + /** Creates empty model. + Use StatModel::train to train the model. Since %SVM has several parameters, you may want to + find the best parameters for your problem, it can be done with SVM::trainAuto. */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized svm from a file + * + * Use SVM::save to serialize and store an SVM to disk. + * Load the SVM from this file again, by calling this function with the path to the file. + * + * @param filepath path to serialized svm + */ + CV_WRAP static Ptr load(const String& filepath); +}; + +/****************************************************************************************\ +* Expectation - Maximization * +\****************************************************************************************/ + +/** @brief The class implements the Expectation Maximization algorithm. + +@sa @ref ml_intro_em + */ +class CV_EXPORTS_W EM : public StatModel +{ +public: + //! Type of covariation matrices + enum Types { + /** A scaled identity matrix \f$\mu_k * I\f$. There is the only + parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases, + when the constraint is relevant, or as a first step in the optimization (for example in case + when the data is preprocessed with PCA). The results of such preliminary estimation may be + passed again to the optimization procedure, this time with + covMatType=EM::COV_MAT_DIAGONAL. */ + COV_MAT_SPHERICAL=0, + /** A diagonal matrix with positive diagonal elements. The number of + free parameters is d for each matrix. This is most commonly used option yielding good + estimation results. */ + COV_MAT_DIAGONAL=1, + /** A symmetric positively defined matrix. The number of free + parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless + there is pretty accurate initial estimation of the parameters and/or a huge number of + training samples. */ + COV_MAT_GENERIC=2, + COV_MAT_DEFAULT=COV_MAT_DIAGONAL + }; + + //! Default parameters + enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100}; + + //! The initial step + enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0}; + + /** The number of mixture components in the Gaussian mixture model. + Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could + determine the optimal number of mixtures within a specified value range, but that is not the + case in ML yet. */ + /** @see setClustersNumber */ + CV_WRAP virtual int getClustersNumber() const = 0; + /** @copybrief getClustersNumber @see getClustersNumber */ + CV_WRAP virtual void setClustersNumber(int val) = 0; + + /** Constraint on covariance matrices which defines type of matrices. + See EM::Types. */ + /** @see setCovarianceMatrixType */ + CV_WRAP virtual int getCovarianceMatrixType() const = 0; + /** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */ + CV_WRAP virtual void setCovarianceMatrixType(int val) = 0; + + /** The termination criteria of the %EM algorithm. + The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of + M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default + maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0; + + /** @brief Returns weights of the mixtures + + Returns vector with the number of elements equal to the number of mixtures. + */ + CV_WRAP virtual Mat getWeights() const = 0; + /** @brief Returns the cluster centers (means of the Gaussian mixture) + + Returns matrix with the number of rows equal to the number of mixtures and number of columns + equal to the space dimensionality. + */ + CV_WRAP virtual Mat getMeans() const = 0; + /** @brief Returns covariation matrices + + Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, + each matrix is a square floating-point matrix NxN, where N is the space dimensionality. + */ + CV_WRAP virtual void getCovs(CV_OUT std::vector& covs) const = 0; + + /** @brief Returns a likelihood logarithm value and an index of the most probable mixture component + for the given sample. + + @param sample A sample for classification. It should be a one-channel matrix of + \f$1 \times dims\f$ or \f$dims \times 1\f$ size. + @param probs Optional output matrix that contains posterior probabilities of each component + given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type. + + The method returns a two-element double vector. Zero element is a likelihood logarithm value for + the sample. First element is an index of the most probable mixture component for the given + sample. + */ + CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0; + + /** @brief Estimate the Gaussian mixture parameters from a samples set. + + This variation starts with Expectation step. Initial values of the model parameters will be + estimated by the k-means algorithm. + + Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take + responses (class labels or function values) as input. Instead, it computes the *Maximum + Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the + parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in + covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each + sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most + probable mixture component for each sample). + + The trained model can be used further for prediction, just like any other classifier. The + trained model is similar to the NormalBayesClassifier. + + @param samples Samples from which the Gaussian mixture model will be estimated. It should be a + one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type + it will be converted to the inner matrix of such type for the further computing. + @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for + each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. + @param labels The optional output "class label" for each sample: + \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable + mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. + @param probs The optional output matrix that contains posterior probabilities of each Gaussian + mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and + CV_64FC1 type. + */ + CV_WRAP virtual bool trainEM(InputArray samples, + OutputArray logLikelihoods=noArray(), + OutputArray labels=noArray(), + OutputArray probs=noArray()) = 0; + + /** @brief Estimate the Gaussian mixture parameters from a samples set. + + This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of + mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices + \f$S_k\f$ of mixture components. + + @param samples Samples from which the Gaussian mixture model will be estimated. It should be a + one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type + it will be converted to the inner matrix of such type for the further computing. + @param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of + \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be + converted to the inner matrix of such type for the further computing. + @param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of + covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices + do not have CV_64F type they will be converted to the inner matrices of such type for the + further computing. + @param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel + floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size. + @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for + each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. + @param labels The optional output "class label" for each sample: + \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable + mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. + @param probs The optional output matrix that contains posterior probabilities of each Gaussian + mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and + CV_64FC1 type. + */ + CV_WRAP virtual bool trainE(InputArray samples, InputArray means0, + InputArray covs0=noArray(), + InputArray weights0=noArray(), + OutputArray logLikelihoods=noArray(), + OutputArray labels=noArray(), + OutputArray probs=noArray()) = 0; + + /** @brief Estimate the Gaussian mixture parameters from a samples set. + + This variation starts with Maximization step. You need to provide initial probabilities + \f$p_{i,k}\f$ to use this option. + + @param samples Samples from which the Gaussian mixture model will be estimated. It should be a + one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type + it will be converted to the inner matrix of such type for the further computing. + @param probs0 + @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for + each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. + @param labels The optional output "class label" for each sample: + \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable + mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. + @param probs The optional output matrix that contains posterior probabilities of each Gaussian + mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and + CV_64FC1 type. + */ + CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0, + OutputArray logLikelihoods=noArray(), + OutputArray labels=noArray(), + OutputArray probs=noArray()) = 0; + + /** Creates empty %EM model. + The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you + can use one of the EM::train\* methods or load it from file using Algorithm::load\(filename). + */ + CV_WRAP static Ptr create(); +}; + +/****************************************************************************************\ +* Decision Tree * +\****************************************************************************************/ + +/** @brief The class represents a single decision tree or a collection of decision trees. + +The current public interface of the class allows user to train only a single decision tree, however +the class is capable of storing multiple decision trees and using them for prediction (by summing +responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) +use this capability to implement decision tree ensembles. + +@sa @ref ml_intro_trees +*/ +class CV_EXPORTS_W DTrees : public StatModel +{ +public: + /** Predict options */ + enum Flags { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) }; + + /** Cluster possible values of a categorical variable into K\<=maxCategories clusters to + find a suboptimal split. + If a discrete variable, on which the training procedure tries to make a split, takes more than + maxCategories values, the precise best subset estimation may take a very long time because the + algorithm is exponential. Instead, many decision trees engines (including our implementation) + try to find sub-optimal split in this case by clustering all the samples into maxCategories + clusters that is some categories are merged together. The clustering is applied only in n \> + 2-class classification problems for categorical variables with N \> max_categories possible + values. In case of regression and 2-class classification the optimal split can be found + efficiently without employing clustering, thus the parameter is not used in these cases. + Default value is 10.*/ + /** @see setMaxCategories */ + CV_WRAP virtual int getMaxCategories() const = 0; + /** @copybrief getMaxCategories @see getMaxCategories */ + CV_WRAP virtual void setMaxCategories(int val) = 0; + + /** The maximum possible depth of the tree. + That is the training algorithms attempts to split a node while its depth is less than maxDepth. + The root node has zero depth. The actual depth may be smaller if the other termination criteria + are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the + tree is pruned. Default value is INT_MAX.*/ + /** @see setMaxDepth */ + CV_WRAP virtual int getMaxDepth() const = 0; + /** @copybrief getMaxDepth @see getMaxDepth */ + CV_WRAP virtual void setMaxDepth(int val) = 0; + + /** If the number of samples in a node is less than this parameter then the node will not be split. + + Default value is 10.*/ + /** @see setMinSampleCount */ + CV_WRAP virtual int getMinSampleCount() const = 0; + /** @copybrief getMinSampleCount @see getMinSampleCount */ + CV_WRAP virtual void setMinSampleCount(int val) = 0; + + /** If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold + cross-validation procedure where K is equal to CVFolds. + Default value is 10.*/ + /** @see setCVFolds */ + CV_WRAP virtual int getCVFolds() const = 0; + /** @copybrief getCVFolds @see getCVFolds */ + CV_WRAP virtual void setCVFolds(int val) = 0; + + /** If true then surrogate splits will be built. + These splits allow to work with missing data and compute variable importance correctly. + Default value is false. + @note currently it's not implemented.*/ + /** @see setUseSurrogates */ + CV_WRAP virtual bool getUseSurrogates() const = 0; + /** @copybrief getUseSurrogates @see getUseSurrogates */ + CV_WRAP virtual void setUseSurrogates(bool val) = 0; + + /** If true then a pruning will be harsher. + This will make a tree more compact and more resistant to the training data noise but a bit less + accurate. Default value is true.*/ + /** @see setUse1SERule */ + CV_WRAP virtual bool getUse1SERule() const = 0; + /** @copybrief getUse1SERule @see getUse1SERule */ + CV_WRAP virtual void setUse1SERule(bool val) = 0; + + /** If true then pruned branches are physically removed from the tree. + Otherwise they are retained and it is possible to get results from the original unpruned (or + pruned less aggressively) tree. Default value is true.*/ + /** @see setTruncatePrunedTree */ + CV_WRAP virtual bool getTruncatePrunedTree() const = 0; + /** @copybrief getTruncatePrunedTree @see getTruncatePrunedTree */ + CV_WRAP virtual void setTruncatePrunedTree(bool val) = 0; + + /** Termination criteria for regression trees. + If all absolute differences between an estimated value in a node and values of train samples + in this node are less than this parameter then the node will not be split further. Default + value is 0.01f*/ + /** @see setRegressionAccuracy */ + CV_WRAP virtual float getRegressionAccuracy() const = 0; + /** @copybrief getRegressionAccuracy @see getRegressionAccuracy */ + CV_WRAP virtual void setRegressionAccuracy(float val) = 0; + + /** @brief The array of a priori class probabilities, sorted by the class label value. + + The parameter can be used to tune the decision tree preferences toward a certain class. For + example, if you want to detect some rare anomaly occurrence, the training base will likely + contain much more normal cases than anomalies, so a very good classification performance + will be achieved just by considering every case as normal. To avoid this, the priors can be + specified, where the anomaly probability is artificially increased (up to 0.5 or even + greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is + adjusted properly. + + You can also think about this parameter as weights of prediction categories which determine + relative weights that you give to misclassification. That is, if the weight of the first + category is 1 and the weight of the second category is 10, then each mistake in predicting + the second category is equivalent to making 10 mistakes in predicting the first category. + Default value is empty Mat.*/ + /** @see setPriors */ + CV_WRAP virtual cv::Mat getPriors() const = 0; + /** @copybrief getPriors @see getPriors */ + CV_WRAP virtual void setPriors(const cv::Mat &val) = 0; + + /** @brief The class represents a decision tree node. + */ + class CV_EXPORTS Node + { + public: + Node(); + double value; //!< Value at the node: a class label in case of classification or estimated + //!< function value in case of regression. + int classIdx; //!< Class index normalized to 0..class_count-1 range and assigned to the + //!< node. It is used internally in classification trees and tree ensembles. + int parent; //!< Index of the parent node + int left; //!< Index of the left child node + int right; //!< Index of right child node + int defaultDir; //!< Default direction where to go (-1: left or +1: right). It helps in the + //!< case of missing values. + int split; //!< Index of the first split + }; + + /** @brief The class represents split in a decision tree. + */ + class CV_EXPORTS Split + { + public: + Split(); + int varIdx; //!< Index of variable on which the split is created. + bool inversed; //!< If true, then the inverse split rule is used (i.e. left and right + //!< branches are exchanged in the rule expressions below). + float quality; //!< The split quality, a positive number. It is used to choose the best split. + int next; //!< Index of the next split in the list of splits for the node + float c; /**< The threshold value in case of split on an ordered variable. + The rule is: + @code{.none} + if var_value < c + then next_node <- left + else next_node <- right + @endcode */ + int subsetOfs; /**< Offset of the bitset used by the split on a categorical variable. + The rule is: + @code{.none} + if bitset[var_value] == 1 + then next_node <- left + else next_node <- right + @endcode */ + }; + + /** @brief Returns indices of root nodes + */ + virtual const std::vector& getRoots() const = 0; + /** @brief Returns all the nodes + + all the node indices are indices in the returned vector + */ + virtual const std::vector& getNodes() const = 0; + /** @brief Returns all the splits + + all the split indices are indices in the returned vector + */ + virtual const std::vector& getSplits() const = 0; + /** @brief Returns all the bitsets for categorical splits + + Split::subsetOfs is an offset in the returned vector + */ + virtual const std::vector& getSubsets() const = 0; + + /** @brief Creates the empty model + + The static method creates empty decision tree with the specified parameters. It should be then + trained using train method (see StatModel::train). Alternatively, you can load the model from + file using Algorithm::load\(filename). + */ + CV_WRAP static Ptr create(); +}; + +/****************************************************************************************\ +* Random Trees Classifier * +\****************************************************************************************/ + +/** @brief The class implements the random forest predictor. + +@sa @ref ml_intro_rtrees + */ +class CV_EXPORTS_W RTrees : public DTrees +{ +public: + + /** If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. + Default value is false.*/ + /** @see setCalculateVarImportance */ + CV_WRAP virtual bool getCalculateVarImportance() const = 0; + /** @copybrief getCalculateVarImportance @see getCalculateVarImportance */ + CV_WRAP virtual void setCalculateVarImportance(bool val) = 0; + + /** The size of the randomly selected subset of features at each tree node and that are used + to find the best split(s). + If you set it to 0 then the size will be set to the square root of the total number of + features. Default value is 0.*/ + /** @see setActiveVarCount */ + CV_WRAP virtual int getActiveVarCount() const = 0; + /** @copybrief getActiveVarCount @see getActiveVarCount */ + CV_WRAP virtual void setActiveVarCount(int val) = 0; + + /** The termination criteria that specifies when the training algorithm stops. + Either when the specified number of trees is trained and added to the ensemble or when + sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the + better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes + pass a certain number of trees. Also to keep in mind, the number of tree increases the + prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS + + TermCriteria::EPS, 50, 0.1)*/ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0; + + /** Returns the variable importance array. + The method returns the variable importance vector, computed at the training stage when + CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is + returned. + */ + CV_WRAP virtual Mat getVarImportance() const = 0; + + /** Creates the empty model. + Use StatModel::train to train the model, StatModel::train to create and train the model, + Algorithm::load to load the pre-trained model. + */ + CV_WRAP static Ptr create(); +}; + +/****************************************************************************************\ +* Boosted tree classifier * +\****************************************************************************************/ + +/** @brief Boosted tree classifier derived from DTrees + +@sa @ref ml_intro_boost + */ +class CV_EXPORTS_W Boost : public DTrees +{ +public: + /** Type of the boosting algorithm. + See Boost::Types. Default value is Boost::REAL. */ + /** @see setBoostType */ + CV_WRAP virtual int getBoostType() const = 0; + /** @copybrief getBoostType @see getBoostType */ + CV_WRAP virtual void setBoostType(int val) = 0; + + /** The number of weak classifiers. + Default value is 100. */ + /** @see setWeakCount */ + CV_WRAP virtual int getWeakCount() const = 0; + /** @copybrief getWeakCount @see getWeakCount */ + CV_WRAP virtual void setWeakCount(int val) = 0; + + /** A threshold between 0 and 1 used to save computational time. + Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next* + iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.*/ + /** @see setWeightTrimRate */ + CV_WRAP virtual double getWeightTrimRate() const = 0; + /** @copybrief getWeightTrimRate @see getWeightTrimRate */ + CV_WRAP virtual void setWeightTrimRate(double val) = 0; + + /** Boosting type. + Gentle AdaBoost and Real AdaBoost are often the preferable choices. */ + enum Types { + DISCRETE=0, //!< Discrete AdaBoost. + REAL=1, //!< Real AdaBoost. It is a technique that utilizes confidence-rated predictions + //!< and works well with categorical data. + LOGIT=2, //!< LogitBoost. It can produce good regression fits. + GENTLE=3 //!< Gentle AdaBoost. It puts less weight on outlier data points and for that + //!(filename) to load the pre-trained model. */ + CV_WRAP static Ptr create(); +}; + +/****************************************************************************************\ +* Gradient Boosted Trees * +\****************************************************************************************/ + +/*class CV_EXPORTS_W GBTrees : public DTrees +{ +public: + struct CV_EXPORTS_W_MAP Params : public DTrees::Params + { + CV_PROP_RW int weakCount; + CV_PROP_RW int lossFunctionType; + CV_PROP_RW float subsamplePortion; + CV_PROP_RW float shrinkage; + + Params(); + Params( int lossFunctionType, int weakCount, float shrinkage, + float subsamplePortion, int maxDepth, bool useSurrogates ); + }; + + enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS}; + + virtual void setK(int k) = 0; + + virtual float predictSerial( InputArray samples, + OutputArray weakResponses, int flags) const = 0; + + static Ptr create(const Params& p); +};*/ + +/****************************************************************************************\ +* Artificial Neural Networks (ANN) * +\****************************************************************************************/ + +/////////////////////////////////// Multi-Layer Perceptrons ////////////////////////////// + +/** @brief Artificial Neural Networks - Multi-Layer Perceptrons. + +Unlike many other models in ML that are constructed and trained at once, in the MLP model these +steps are separated. First, a network with the specified topology is created using the non-default +constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is +trained using a set of input and output vectors. The training procedure can be repeated more than +once, that is, the weights can be adjusted based on the new training data. + +Additional flags for StatModel::train are available: ANN_MLP::TrainFlags. + +@sa @ref ml_intro_ann + */ +class CV_EXPORTS_W ANN_MLP : public StatModel +{ +public: + /** Available training methods */ + enum TrainingMethods { + BACKPROP=0, //!< The back-propagation algorithm. + RPROP=1 //!< The RPROP algorithm. See @cite RPROP93 for details. + }; + + /** Sets training method and common parameters. + @param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods. + @param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP + @param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP. + */ + CV_WRAP virtual void setTrainMethod(int method, double param1 = 0, double param2 = 0) = 0; + + /** Returns current training method */ + CV_WRAP virtual int getTrainMethod() const = 0; + + /** Initialize the activation function for each neuron. + Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM. + @param type The type of activation function. See ANN_MLP::ActivationFunctions. + @param param1 The first parameter of the activation function, \f$\alpha\f$. Default value is 0. + @param param2 The second parameter of the activation function, \f$\beta\f$. Default value is 0. + */ + CV_WRAP virtual void setActivationFunction(int type, double param1 = 0, double param2 = 0) = 0; + + /** Integer vector specifying the number of neurons in each layer including the input and output layers. + The very first element specifies the number of elements in the input layer. + The last element - number of elements in the output layer. Default value is empty Mat. + @sa getLayerSizes */ + CV_WRAP virtual void setLayerSizes(InputArray _layer_sizes) = 0; + + /** Integer vector specifying the number of neurons in each layer including the input and output layers. + The very first element specifies the number of elements in the input layer. + The last element - number of elements in the output layer. + @sa setLayerSizes */ + CV_WRAP virtual cv::Mat getLayerSizes() const = 0; + + /** Termination criteria of the training algorithm. + You can specify the maximum number of iterations (maxCount) and/or how much the error could + change between the iterations to make the algorithm continue (epsilon). Default value is + TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).*/ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0; + + /** BPROP: Strength of the weight gradient term. + The recommended value is about 0.1. Default value is 0.1.*/ + /** @see setBackpropWeightScale */ + CV_WRAP virtual double getBackpropWeightScale() const = 0; + /** @copybrief getBackpropWeightScale @see getBackpropWeightScale */ + CV_WRAP virtual void setBackpropWeightScale(double val) = 0; + + /** BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations). + This parameter provides some inertia to smooth the random fluctuations of the weights. It can + vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. + Default value is 0.1.*/ + /** @see setBackpropMomentumScale */ + CV_WRAP virtual double getBackpropMomentumScale() const = 0; + /** @copybrief getBackpropMomentumScale @see getBackpropMomentumScale */ + CV_WRAP virtual void setBackpropMomentumScale(double val) = 0; + + /** RPROP: Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$. + Default value is 0.1.*/ + /** @see setRpropDW0 */ + CV_WRAP virtual double getRpropDW0() const = 0; + /** @copybrief getRpropDW0 @see getRpropDW0 */ + CV_WRAP virtual void setRpropDW0(double val) = 0; + + /** RPROP: Increase factor \f$\eta^+\f$. + It must be \>1. Default value is 1.2.*/ + /** @see setRpropDWPlus */ + CV_WRAP virtual double getRpropDWPlus() const = 0; + /** @copybrief getRpropDWPlus @see getRpropDWPlus */ + CV_WRAP virtual void setRpropDWPlus(double val) = 0; + + /** RPROP: Decrease factor \f$\eta^-\f$. + It must be \<1. Default value is 0.5.*/ + /** @see setRpropDWMinus */ + CV_WRAP virtual double getRpropDWMinus() const = 0; + /** @copybrief getRpropDWMinus @see getRpropDWMinus */ + CV_WRAP virtual void setRpropDWMinus(double val) = 0; + + /** RPROP: Update-values lower limit \f$\Delta_{min}\f$. + It must be positive. Default value is FLT_EPSILON.*/ + /** @see setRpropDWMin */ + CV_WRAP virtual double getRpropDWMin() const = 0; + /** @copybrief getRpropDWMin @see getRpropDWMin */ + CV_WRAP virtual void setRpropDWMin(double val) = 0; + + /** RPROP: Update-values upper limit \f$\Delta_{max}\f$. + It must be \>1. Default value is 50.*/ + /** @see setRpropDWMax */ + CV_WRAP virtual double getRpropDWMax() const = 0; + /** @copybrief getRpropDWMax @see getRpropDWMax */ + CV_WRAP virtual void setRpropDWMax(double val) = 0; + + /** possible activation functions */ + enum ActivationFunctions { + /** Identity function: \f$f(x)=x\f$ */ + IDENTITY = 0, + /** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\f$ + @note + If you are using the default sigmoid activation function with the default parameter values + fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output + will range from [-1.7159, 1.7159], instead of [0,1].*/ + SIGMOID_SYM = 1, + /** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */ + GAUSSIAN = 2 + }; + + /** Train options */ + enum TrainFlags { + /** Update the network weights, rather than compute them from scratch. In the latter case + the weights are initialized using the Nguyen-Widrow algorithm. */ + UPDATE_WEIGHTS = 1, + /** Do not normalize the input vectors. If this flag is not set, the training algorithm + normalizes each input feature independently, shifting its mean value to 0 and making the + standard deviation equal to 1. If the network is assumed to be updated frequently, the new + training data could be much different from original one. In this case, you should take care + of proper normalization. */ + NO_INPUT_SCALE = 2, + /** Do not normalize the output vectors. If the flag is not set, the training algorithm + normalizes each output feature independently, by transforming it to the certain range + depending on the used activation function. */ + NO_OUTPUT_SCALE = 4 + }; + + CV_WRAP virtual Mat getWeights(int layerIdx) const = 0; + + /** @brief Creates empty model + + Use StatModel::train to train the model, Algorithm::load\(filename) to load the pre-trained model. + Note that the train method has optional flags: ANN_MLP::TrainFlags. + */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized ANN from a file + * + * Use ANN::save to serialize and store an ANN to disk. + * Load the ANN from this file again, by calling this function with the path to the file. + * + * @param filepath path to serialized ANN + */ + CV_WRAP static Ptr load(const String& filepath); + +}; + +/****************************************************************************************\ +* Logistic Regression * +\****************************************************************************************/ + +/** @brief Implements Logistic Regression classifier. + +@sa @ref ml_intro_lr + */ +class CV_EXPORTS_W LogisticRegression : public StatModel +{ +public: + + /** Learning rate. */ + /** @see setLearningRate */ + CV_WRAP virtual double getLearningRate() const = 0; + /** @copybrief getLearningRate @see getLearningRate */ + CV_WRAP virtual void setLearningRate(double val) = 0; + + /** Number of iterations. */ + /** @see setIterations */ + CV_WRAP virtual int getIterations() const = 0; + /** @copybrief getIterations @see getIterations */ + CV_WRAP virtual void setIterations(int val) = 0; + + /** Kind of regularization to be applied. See LogisticRegression::RegKinds. */ + /** @see setRegularization */ + CV_WRAP virtual int getRegularization() const = 0; + /** @copybrief getRegularization @see getRegularization */ + CV_WRAP virtual void setRegularization(int val) = 0; + + /** Kind of training method used. See LogisticRegression::Methods. */ + /** @see setTrainMethod */ + CV_WRAP virtual int getTrainMethod() const = 0; + /** @copybrief getTrainMethod @see getTrainMethod */ + CV_WRAP virtual void setTrainMethod(int val) = 0; + + /** Specifies the number of training samples taken in each step of Mini-Batch Gradient + Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It + has to take values less than the total number of training samples. */ + /** @see setMiniBatchSize */ + CV_WRAP virtual int getMiniBatchSize() const = 0; + /** @copybrief getMiniBatchSize @see getMiniBatchSize */ + CV_WRAP virtual void setMiniBatchSize(int val) = 0; + + /** Termination criteria of the algorithm. */ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0; + + //! Regularization kinds + enum RegKinds { + REG_DISABLE = -1, //!< Regularization disabled + REG_L1 = 0, //!< %L1 norm + REG_L2 = 1 //!< %L2 norm + }; + + //! Training methods + enum Methods { + BATCH = 0, + MINI_BATCH = 1 //!< Set MiniBatchSize to a positive integer when using this method. + }; + + /** @brief Predicts responses for input samples and returns a float type. + + @param samples The input data for the prediction algorithm. Matrix [m x n], where each row + contains variables (features) of one object being classified. Should have data type CV_32F. + @param results Predicted labels as a column matrix of type CV_32S. + @param flags Not used. + */ + CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0; + + /** @brief This function returns the trained paramters arranged across rows. + + For a two class classifcation problem, it returns a row matrix. It returns learnt paramters of + the Logistic Regression as a matrix of type CV_32F. + */ + CV_WRAP virtual Mat get_learnt_thetas() const = 0; + + /** @brief Creates empty model. + + Creates Logistic Regression model with parameters given. + */ + CV_WRAP static Ptr create(); +}; + + +/****************************************************************************************\ +* Stochastic Gradient Descent SVM Classifier * +\****************************************************************************************/ + +/*! +@brief Stochastic Gradient Descent SVM classifier + +SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach, +as presented in @cite bottou2010large. + +The classifier has following parameters: +- model type, +- margin type, +- margin regularization (\f$\lambda\f$), +- initial step size (\f$\gamma_0\f$), +- step decreasing power (\f$c\f$), +- and termination criteria. + +The model type may have one of the following values: \ref SGD and \ref ASGD. + +- \ref SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula + \f[w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}\f] + where + - \f$w_t\f$ is the weights vector for decision function at step \f$t\f$, + - \f$\gamma(t)\f$ is the step size of model parameters at the iteration \f$t\f$, it is decreased on each step by the formula + \f$\gamma(t) = \gamma_0 (1 + \lambda \gamma_0 t) ^ {-c}\f$ + - \f$Q_i\f$ is the target functional from SVM task for sample with number \f$i\f$, this sample is chosen stochastically on each step of the algorithm. + +- \ref ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula +\f$\widehat{w}_{t+1} = \frac{t}{1+t}\widehat{w}_{t} + \frac{1}{1+t}w_{t+1}\f$ + +The recommended model type is ASGD (following @cite bottou2010large). + +The margin type may have one of the following values: \ref SOFT_MARGIN or \ref HARD_MARGIN. + +- You should use \ref HARD_MARGIN type, if you have linearly separable sets. +- You should use \ref SOFT_MARGIN type, if you have non-linearly separable sets or sets with outliers. +- In the general case (if you know nothing about linear separability of your sets), use SOFT_MARGIN. + +The other parameters may be described as follows: +- Margin regularization parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers + (the less the parameter, the less probability that an outlier will be ignored). + Recommended value for SGD model is 0.0001, for ASGD model is 0.00001. + +- Initial step size parameter is the initial value for the step size \f$\gamma(t)\f$. + You will have to find the best initial step for your problem. + +- Step decreasing power is the power parameter for \f$\gamma(t)\f$ decreasing by the formula, mentioned above. + Recommended value for SGD model is 1, for ASGD model is 0.75. + +- Termination criteria can be TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT + TermCriteria::EPS. + You will have to find the best termination criteria for your problem. + +Note that the parameters margin regularization, initial step size, and step decreasing power should be positive. + +To use SVMSGD algorithm do as follows: + +- first, create the SVMSGD object. The algoorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(), + setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower(). + +- then the SVM model can be trained using the train features and the correspondent labels by the method train(). + +- after that, the label of a new feature vector can be predicted using the method predict(). + +@code +// Create empty object +cv::Ptr svmsgd = SVMSGD::create(); + +// Train the Stochastic Gradient Descent SVM +svmsgd->train(trainData); + +// Predict labels for the new samples +svmsgd->predict(samples, responses); +@endcode + +*/ + +class CV_EXPORTS_W SVMSGD : public cv::ml::StatModel +{ +public: + + /** SVMSGD type. + ASGD is often the preferable choice. */ + enum SvmsgdType + { + SGD, //!< Stochastic Gradient Descent + ASGD //!< Average Stochastic Gradient Descent + }; + + /** Margin type.*/ + enum MarginType + { + SOFT_MARGIN, //!< General case, suits to the case of non-linearly separable sets, allows outliers. + HARD_MARGIN //!< More accurate for the case of linearly separable sets. + }; + + /** + * @return the weights of the trained model (decision function f(x) = weights * x + shift). + */ + CV_WRAP virtual Mat getWeights() = 0; + + /** + * @return the shift of the trained model (decision function f(x) = weights * x + shift). + */ + CV_WRAP virtual float getShift() = 0; + + /** @brief Creates empty model. + * Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to + * find the best parameters for your problem or use setOptimalParameters() to set some default parameters. + */ + CV_WRAP static Ptr create(); + + /** @brief Function sets optimal parameters values for chosen SVM SGD model. + * @param svmsgdType is the type of SVMSGD classifier. + * @param marginType is the type of margin constraint. + */ + CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0; + + /** @brief %Algorithm type, one of SVMSGD::SvmsgdType. */ + /** @see setSvmsgdType */ + CV_WRAP virtual int getSvmsgdType() const = 0; + /** @copybrief getSvmsgdType @see getSvmsgdType */ + CV_WRAP virtual void setSvmsgdType(int svmsgdType) = 0; + + /** @brief %Margin type, one of SVMSGD::MarginType. */ + /** @see setMarginType */ + CV_WRAP virtual int getMarginType() const = 0; + /** @copybrief getMarginType @see getMarginType */ + CV_WRAP virtual void setMarginType(int marginType) = 0; + + /** @brief Parameter marginRegularization of a %SVMSGD optimization problem. */ + /** @see setMarginRegularization */ + CV_WRAP virtual float getMarginRegularization() const = 0; + /** @copybrief getMarginRegularization @see getMarginRegularization */ + CV_WRAP virtual void setMarginRegularization(float marginRegularization) = 0; + + /** @brief Parameter initialStepSize of a %SVMSGD optimization problem. */ + /** @see setInitialStepSize */ + CV_WRAP virtual float getInitialStepSize() const = 0; + /** @copybrief getInitialStepSize @see getInitialStepSize */ + CV_WRAP virtual void setInitialStepSize(float InitialStepSize) = 0; + + /** @brief Parameter stepDecreasingPower of a %SVMSGD optimization problem. */ + /** @see setStepDecreasingPower */ + CV_WRAP virtual float getStepDecreasingPower() const = 0; + /** @copybrief getStepDecreasingPower @see getStepDecreasingPower */ + CV_WRAP virtual void setStepDecreasingPower(float stepDecreasingPower) = 0; + + /** @brief Termination criteria of the training algorithm. + You can specify the maximum number of iterations (maxCount) and/or how much the error could + change between the iterations to make the algorithm continue (epsilon).*/ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0; +}; + + +/****************************************************************************************\ +* Auxilary functions declarations * +\****************************************************************************************/ + +/** @brief Generates _sample_ from multivariate normal distribution + +@param mean an average row vector +@param cov symmetric covariation matrix +@param nsamples returned samples count +@param samples returned samples array +*/ +CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples); + +/** @brief Creates test set */ +CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses, + OutputArray samples, OutputArray responses); + +//! @} ml + +} +} + +#endif // __cplusplus +#endif // OPENCV_ML_HPP + +/* End of file. */ diff --git a/thirdparty/linux/include/opencv2/ml/ml.hpp b/thirdparty/linux/include/opencv2/ml/ml.hpp new file mode 100644 index 0000000..f6f9cd8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/ml/ml.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/ml.hpp" diff --git a/thirdparty/linux/include/opencv2/objdetect.hpp b/thirdparty/linux/include/opencv2/objdetect.hpp new file mode 100644 index 0000000..cd444d2 --- /dev/null +++ b/thirdparty/linux/include/opencv2/objdetect.hpp @@ -0,0 +1,466 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OBJDETECT_HPP +#define OPENCV_OBJDETECT_HPP + +#include "opencv2/core.hpp" + +/** +@defgroup objdetect Object Detection + +Haar Feature-based Cascade Classifier for Object Detection +---------------------------------------------------------- + +The object detector described below has been initially proposed by Paul Viola @cite Viola01 and +improved by Rainer Lienhart @cite Lienhart02 . + +First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is +trained with a few hundred sample views of a particular object (i.e., a face or a car), called +positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary +images of the same size. + +After a classifier is trained, it can be applied to a region of interest (of the same size as used +during the training) in an input image. The classifier outputs a "1" if the region is likely to show +the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can +move the search window across the image and check every location using the classifier. The +classifier is designed so that it can be easily "resized" in order to be able to find the objects of +interest at different sizes, which is more efficient than resizing the image itself. So, to find an +object of an unknown size in the image the scan procedure should be done several times at different +scales. + +The word "cascade" in the classifier name means that the resultant classifier consists of several +simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some +stage the candidate is rejected or all the stages are passed. The word "boosted" means that the +classifiers at every stage of the cascade are complex themselves and they are built out of basic +classifiers using one of four different boosting techniques (weighted voting). Currently Discrete +Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are +decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic +classifiers, and are calculated as described below. The current algorithm uses the following +Haar-like features: + +![image](pics/haarfeatures.png) + +The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within +the region of interest and the scale (this scale is not the same as the scale used at the detection +stage, though these two scales are multiplied). For example, in the case of the third line feature +(2c) the response is calculated as the difference between the sum of image pixels under the +rectangle covering the whole feature (including the two white stripes and the black stripe in the +middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to +compensate for the differences in the size of areas. The sums of pixel values over a rectangular +regions are calculated rapidly using integral images (see below and the integral description). + +To see the object detector at work, have a look at the facedetect demo: + + +The following reference is for the detection part only. There is a separate application called +opencv_traincascade that can train a cascade of boosted classifiers from a set of samples. + +@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in +addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection +using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at + + +@{ + @defgroup objdetect_c C API +@} + */ + +typedef struct CvHaarClassifierCascade CvHaarClassifierCascade; + +namespace cv +{ + +//! @addtogroup objdetect +//! @{ + +///////////////////////////// Object Detection //////////////////////////// + +//! class for grouping object candidates, detected by Cascade Classifier, HOG etc. +//! instance of the class is to be passed to cv::partition (see cxoperations.hpp) +class CV_EXPORTS SimilarRects +{ +public: + SimilarRects(double _eps) : eps(_eps) {} + inline bool operator()(const Rect& r1, const Rect& r2) const + { + double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5; + return std::abs(r1.x - r2.x) <= delta && + std::abs(r1.y - r2.y) <= delta && + std::abs(r1.x + r1.width - r2.x - r2.width) <= delta && + std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; + } + double eps; +}; + +/** @brief Groups the object candidate rectangles. + +@param rectList Input/output vector of rectangles. Output vector includes retained and grouped +rectangles. (The Python list is not modified in place.) +@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a +group of rectangles to retain it. +@param eps Relative difference between sides of the rectangles to merge them into a group. + +The function is a wrapper for the generic function partition . It clusters all the input rectangles +using the rectangle equivalence criteria that combines rectangles with similar sizes and similar +locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If +\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small +clusters containing less than or equal to groupThreshold rectangles are rejected. In each other +cluster, the average rectangle is computed and put into the output rectangle list. + */ +CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector& rectList, CV_OUT std::vector& weights, + int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, + double eps, std::vector* weights, std::vector* levelWeights ); +/** @overload */ +CV_EXPORTS void groupRectangles(std::vector& rectList, std::vector& rejectLevels, + std::vector& levelWeights, int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS void groupRectangles_meanshift(std::vector& rectList, std::vector& foundWeights, + std::vector& foundScales, + double detectThreshold = 0.0, Size winDetSize = Size(64, 128)); + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvHaarClassifierCascade* obj) const; + +enum { CASCADE_DO_CANNY_PRUNING = 1, + CASCADE_SCALE_IMAGE = 2, + CASCADE_FIND_BIGGEST_OBJECT = 4, + CASCADE_DO_ROUGH_SEARCH = 8 + }; + +class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm +{ +public: + virtual ~BaseCascadeClassifier(); + virtual bool empty() const = 0; + virtual bool load( const String& filename ) = 0; + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize ) = 0; + + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& numDetections, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize ) = 0; + + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& rejectLevels, + CV_OUT std::vector& levelWeights, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize, + bool outputRejectLevels ) = 0; + + virtual bool isOldFormatCascade() const = 0; + virtual Size getOriginalWindowSize() const = 0; + virtual int getFeatureType() const = 0; + virtual void* getOldCascade() = 0; + + class CV_EXPORTS MaskGenerator + { + public: + virtual ~MaskGenerator() {} + virtual Mat generateMask(const Mat& src)=0; + virtual void initializeMask(const Mat& /*src*/) { } + }; + virtual void setMaskGenerator(const Ptr& maskGenerator) = 0; + virtual Ptr getMaskGenerator() = 0; +}; + +/** @brief Cascade classifier class for object detection. + */ +class CV_EXPORTS_W CascadeClassifier +{ +public: + CV_WRAP CascadeClassifier(); + /** @brief Loads a classifier from a file. + + @param filename Name of the file from which the classifier is loaded. + */ + CV_WRAP CascadeClassifier(const String& filename); + ~CascadeClassifier(); + /** @brief Checks whether the classifier has been loaded. + */ + CV_WRAP bool empty() const; + /** @brief Loads a classifier from a file. + + @param filename Name of the file from which the classifier is loaded. The file may contain an old + HAAR classifier trained by the haartraining application or a new cascade classifier trained by the + traincascade application. + */ + CV_WRAP bool load( const String& filename ); + /** @brief Reads a classifier from a FileStorage node. + + @note The file may contain a new cascade classifier (trained traincascade application) only. + */ + CV_WRAP bool read( const FileNode& node ); + + /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list + of rectangles. + + @param image Matrix of the type CV_8U containing an image where objects are detected. + @param objects Vector of rectangles where each rectangle contains the detected object, the + rectangles may be partially outside the original image. + @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. + @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have + to retain it. + @param flags Parameter with the same meaning for an old cascade as in the function + cvHaarDetectObjects. It is not used for a new cascade. + @param minSize Minimum possible object size. Objects smaller than that are ignored. + @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. + + The function is parallelized with the TBB library. + + @note + - (Python) A face detection example using cascade classifiers can be found at + opencv_source_code/samples/python/facedetect.py + */ + CV_WRAP void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + double scaleFactor = 1.1, + int minNeighbors = 3, int flags = 0, + Size minSize = Size(), + Size maxSize = Size() ); + + /** @overload + @param image Matrix of the type CV_8U containing an image where objects are detected. + @param objects Vector of rectangles where each rectangle contains the detected object, the + rectangles may be partially outside the original image. + @param numDetections Vector of detection numbers for the corresponding objects. An object's number + of detections is the number of neighboring positively classified rectangles that were joined + together to form the object. + @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. + @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have + to retain it. + @param flags Parameter with the same meaning for an old cascade as in the function + cvHaarDetectObjects. It is not used for a new cascade. + @param minSize Minimum possible object size. Objects smaller than that are ignored. + @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. + */ + CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& numDetections, + double scaleFactor=1.1, + int minNeighbors=3, int flags=0, + Size minSize=Size(), + Size maxSize=Size() ); + + /** @overload + if `outputRejectLevels` is `true` returns `rejectLevels` and `levelWeights` + */ + CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& rejectLevels, + CV_OUT std::vector& levelWeights, + double scaleFactor = 1.1, + int minNeighbors = 3, int flags = 0, + Size minSize = Size(), + Size maxSize = Size(), + bool outputRejectLevels = false ); + + CV_WRAP bool isOldFormatCascade() const; + CV_WRAP Size getOriginalWindowSize() const; + CV_WRAP int getFeatureType() const; + void* getOldCascade(); + + CV_WRAP static bool convert(const String& oldcascade, const String& newcascade); + + void setMaskGenerator(const Ptr& maskGenerator); + Ptr getMaskGenerator(); + + Ptr cc; +}; + +CV_EXPORTS Ptr createFaceDetectionMaskGenerator(); + +//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// + +//! struct for detection region of interest (ROI) +struct DetectionROI +{ + //! scale(size) of the bounding box + double scale; + //! set of requrested locations to be evaluated + std::vector locations; + //! vector that will contain confidence values for each location + std::vector confidences; +}; + +struct CV_EXPORTS_W HOGDescriptor +{ +public: + enum { L2Hys = 0 + }; + enum { DEFAULT_NLEVELS = 64 + }; + + CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8), + cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1), + histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true), + free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false) + {} + + CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, + Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1, + int _histogramNormType=HOGDescriptor::L2Hys, + double _L2HysThreshold=0.2, bool _gammaCorrection=false, + int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false) + : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize), + nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma), + histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold), + gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient) + {} + + CV_WRAP HOGDescriptor(const String& filename) + { + load(filename); + } + + HOGDescriptor(const HOGDescriptor& d) + { + d.copyTo(*this); + } + + virtual ~HOGDescriptor() {} + + CV_WRAP size_t getDescriptorSize() const; + CV_WRAP bool checkDetectorSize() const; + CV_WRAP double getWinSigma() const; + + CV_WRAP virtual void setSVMDetector(InputArray _svmdetector); + + virtual bool read(FileNode& fn); + virtual void write(FileStorage& fs, const String& objname) const; + + CV_WRAP virtual bool load(const String& filename, const String& objname = String()); + CV_WRAP virtual void save(const String& filename, const String& objname = String()) const; + virtual void copyTo(HOGDescriptor& c) const; + + CV_WRAP virtual void compute(InputArray img, + CV_OUT std::vector& descriptors, + Size winStride = Size(), Size padding = Size(), + const std::vector& locations = std::vector()) const; + + //! with found weights output + CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, + CV_OUT std::vector& weights, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), + const std::vector& searchLocations = std::vector()) const; + //! without found weights output + virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), + const std::vector& searchLocations=std::vector()) const; + + //! with result weights output + CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, + CV_OUT std::vector& foundWeights, double hitThreshold = 0, + Size winStride = Size(), Size padding = Size(), double scale = 1.05, + double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const; + //! without found weights output + virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), double scale = 1.05, + double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const; + + CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs, + Size paddingTL = Size(), Size paddingBR = Size()) const; + + CV_WRAP static std::vector getDefaultPeopleDetector(); + CV_WRAP static std::vector getDaimlerPeopleDetector(); + + CV_PROP Size winSize; + CV_PROP Size blockSize; + CV_PROP Size blockStride; + CV_PROP Size cellSize; + CV_PROP int nbins; + CV_PROP int derivAperture; + CV_PROP double winSigma; + CV_PROP int histogramNormType; + CV_PROP double L2HysThreshold; + CV_PROP bool gammaCorrection; + CV_PROP std::vector svmDetector; + UMat oclSvmDetector; + float free_coef; + CV_PROP int nlevels; + CV_PROP bool signedGradient; + + + //! evaluate specified ROI and return confidence value for each location + virtual void detectROI(const cv::Mat& img, const std::vector &locations, + CV_OUT std::vector& foundLocations, CV_OUT std::vector& confidences, + double hitThreshold = 0, cv::Size winStride = Size(), + cv::Size padding = Size()) const; + + //! evaluate specified ROI and return confidence value for each location in multiple scales + virtual void detectMultiScaleROI(const cv::Mat& img, + CV_OUT std::vector& foundLocations, + std::vector& locations, + double hitThreshold = 0, + int groupThreshold = 0) const; + + //! read/parse Dalal's alt model file + void readALTModel(String modelfile); + void groupRectangles(std::vector& rectList, std::vector& weights, int groupThreshold, double eps) const; +}; + +//! @} objdetect + +} + +#include "opencv2/objdetect/detection_based_tracker.hpp" + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/objdetect/objdetect_c.h" +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/objdetect/detection_based_tracker.hpp b/thirdparty/linux/include/opencv2/objdetect/detection_based_tracker.hpp new file mode 100644 index 0000000..b93c8f5 --- /dev/null +++ b/thirdparty/linux/include/opencv2/objdetect/detection_based_tracker.hpp @@ -0,0 +1,225 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OBJDETECT_DBT_HPP +#define OPENCV_OBJDETECT_DBT_HPP + +// After this condition removal update blacklist for bindings: modules/python/common.cmake +#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(__ANDROID__) || \ + (defined(__cplusplus) && __cplusplus > 199711L) || (defined(_MSC_VER) && _MSC_VER >= 1700) + +#include + +namespace cv +{ + +//! @addtogroup objdetect +//! @{ + +class CV_EXPORTS DetectionBasedTracker +{ + public: + struct CV_EXPORTS Parameters + { + int maxTrackLifetime; + int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0 + + Parameters(); + }; + + class IDetector + { + public: + IDetector(): + minObjSize(96, 96), + maxObjSize(INT_MAX, INT_MAX), + minNeighbours(2), + scaleFactor(1.1f) + {} + + virtual void detect(const cv::Mat& image, std::vector& objects) = 0; + + void setMinObjectSize(const cv::Size& min) + { + minObjSize = min; + } + void setMaxObjectSize(const cv::Size& max) + { + maxObjSize = max; + } + cv::Size getMinObjectSize() const + { + return minObjSize; + } + cv::Size getMaxObjectSize() const + { + return maxObjSize; + } + float getScaleFactor() + { + return scaleFactor; + } + void setScaleFactor(float value) + { + scaleFactor = value; + } + int getMinNeighbours() + { + return minNeighbours; + } + void setMinNeighbours(int value) + { + minNeighbours = value; + } + virtual ~IDetector() {} + + protected: + cv::Size minObjSize; + cv::Size maxObjSize; + int minNeighbours; + float scaleFactor; + }; + + DetectionBasedTracker(cv::Ptr mainDetector, cv::Ptr trackingDetector, const Parameters& params); + virtual ~DetectionBasedTracker(); + + virtual bool run(); + virtual void stop(); + virtual void resetTracking(); + + virtual void process(const cv::Mat& imageGray); + + bool setParameters(const Parameters& params); + const Parameters& getParameters() const; + + + typedef std::pair Object; + virtual void getObjects(std::vector& result) const; + virtual void getObjects(std::vector& result) const; + + enum ObjectStatus + { + DETECTED_NOT_SHOWN_YET, + DETECTED, + DETECTED_TEMPORARY_LOST, + WRONG_OBJECT + }; + struct ExtObject + { + int id; + cv::Rect location; + ObjectStatus status; + ExtObject(int _id, cv::Rect _location, ObjectStatus _status) + :id(_id), location(_location), status(_status) + { + } + }; + virtual void getObjects(std::vector& result) const; + + + virtual int addObject(const cv::Rect& location); //returns id of the new object + + protected: + class SeparateDetectionWork; + cv::Ptr separateDetectionWork; + friend void* workcycleObjectDetectorFunction(void* p); + + struct InnerParameters + { + int numLastPositionsToTrack; + int numStepsToWaitBeforeFirstShow; + int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown; + int numStepsToShowWithoutDetecting; + + float coeffTrackingWindowSize; + float coeffObjectSizeToTrack; + float coeffObjectSpeedUsingInPrediction; + + InnerParameters(); + }; + Parameters parameters; + InnerParameters innerParameters; + + struct TrackedObject + { + typedef std::vector PositionsVector; + + PositionsVector lastPositions; + + int numDetectedFrames; + int numFramesNotDetected; + int id; + + TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0) + { + lastPositions.push_back(rect); + id=getNextId(); + }; + + static int getNextId() + { + static int _id=0; + return _id++; + } + }; + + int numTrackedSteps; + std::vector trackedObjects; + + std::vector weightsPositionsSmoothing; + std::vector weightsSizesSmoothing; + + cv::Ptr cascadeForTracking; + + void updateTrackedObjects(const std::vector& detectedObjects); + cv::Rect calcTrackedObjectPositionToShow(int i) const; + cv::Rect calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const; + void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector& detectedObjectsInRegions); +}; + +//! @} objdetect + +} //end of cv namespace +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/objdetect/objdetect.hpp b/thirdparty/linux/include/opencv2/objdetect/objdetect.hpp new file mode 100644 index 0000000..3ee284f --- /dev/null +++ b/thirdparty/linux/include/opencv2/objdetect/objdetect.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/objdetect.hpp" diff --git a/thirdparty/linux/include/opencv2/objdetect/objdetect_c.h b/thirdparty/linux/include/opencv2/objdetect/objdetect_c.h new file mode 100644 index 0000000..b3ee7f4 --- /dev/null +++ b/thirdparty/linux/include/opencv2/objdetect/objdetect_c.h @@ -0,0 +1,165 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OBJDETECT_C_H +#define OPENCV_OBJDETECT_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +#include +#include + +extern "C" { +#endif + +/** @addtogroup objdetect_c + @{ + */ + +/****************************************************************************************\ +* Haar-like Object Detection functions * +\****************************************************************************************/ + +#define CV_HAAR_MAGIC_VAL 0x42500000 +#define CV_TYPE_NAME_HAAR "opencv-haar-classifier" + +#define CV_IS_HAAR_CLASSIFIER( haar ) \ + ((haar) != NULL && \ + (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL) + +#define CV_HAAR_FEATURE_MAX 3 + +typedef struct CvHaarFeature +{ + int tilted; + struct + { + CvRect r; + float weight; + } rect[CV_HAAR_FEATURE_MAX]; +} CvHaarFeature; + +typedef struct CvHaarClassifier +{ + int count; + CvHaarFeature* haar_feature; + float* threshold; + int* left; + int* right; + float* alpha; +} CvHaarClassifier; + +typedef struct CvHaarStageClassifier +{ + int count; + float threshold; + CvHaarClassifier* classifier; + + int next; + int child; + int parent; +} CvHaarStageClassifier; + +typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade; + +typedef struct CvHaarClassifierCascade +{ + int flags; + int count; + CvSize orig_window_size; + CvSize real_window_size; + double scale; + CvHaarStageClassifier* stage_classifier; + CvHidHaarClassifierCascade* hid_cascade; +} CvHaarClassifierCascade; + +typedef struct CvAvgComp +{ + CvRect rect; + int neighbors; +} CvAvgComp; + +/* Loads haar classifier cascade from a directory. + It is obsolete: convert your cascade to xml and use cvLoad instead */ +CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade( + const char* directory, CvSize orig_window_size); + +CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade ); + +#define CV_HAAR_DO_CANNY_PRUNING 1 +#define CV_HAAR_SCALE_IMAGE 2 +#define CV_HAAR_FIND_BIGGEST_OBJECT 4 +#define CV_HAAR_DO_ROUGH_SEARCH 8 + +CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image, + CvHaarClassifierCascade* cascade, CvMemStorage* storage, + double scale_factor CV_DEFAULT(1.1), + int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0), + CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0))); + +/* sets images for haar classifier cascade */ +CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, + const CvArr* sum, const CvArr* sqsum, + const CvArr* tilted_sum, double scale ); + +/* runs the cascade on the specified window */ +CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade, + CvPoint pt, int start_stage CV_DEFAULT(0)); + +/** @} objdetect_c */ + +#ifdef __cplusplus +} + +CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image, + CvHaarClassifierCascade* cascade, CvMemStorage* storage, + std::vector& rejectLevels, std::vector& levelWeightds, + double scale_factor = 1.1, + int min_neighbors = 3, int flags = 0, + CvSize min_size = cvSize(0, 0), CvSize max_size = cvSize(0, 0), + bool outputRejectLevels = false ); + +#endif + +#endif /* OPENCV_OBJDETECT_C_H */ diff --git a/thirdparty/linux/include/opencv2/opencv.hpp b/thirdparty/linux/include/opencv2/opencv.hpp new file mode 100644 index 0000000..532d7a3 --- /dev/null +++ b/thirdparty/linux/include/opencv2/opencv.hpp @@ -0,0 +1,136 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_ALL_HPP +#define OPENCV_ALL_HPP + +// File that defines what modules where included during the build of OpenCV +// These are purely the defines of the correct HAVE_OPENCV_modulename values +#include "opencv2/opencv_modules.hpp" + +// Then the list of defines is checked to include the correct headers +// Core library is always included --> without no OpenCV functionality available +#include "opencv2/core.hpp" + +// Then the optional modules are checked +#ifdef HAVE_OPENCV_CALIB3D +#include "opencv2/calib3d.hpp" +#endif +#ifdef HAVE_OPENCV_FEATURES2D +#include "opencv2/features2d.hpp" +#endif +#ifdef HAVE_OPENCV_FLANN +#include "opencv2/flann.hpp" +#endif +#ifdef HAVE_OPENCV_HIGHGUI +#include "opencv2/highgui.hpp" +#endif +#ifdef HAVE_OPENCV_IMGCODECS +#include "opencv2/imgcodecs.hpp" +#endif +#ifdef HAVE_OPENCV_IMGPROC +#include "opencv2/imgproc.hpp" +#endif +#ifdef HAVE_OPENCV_ML +#include "opencv2/ml.hpp" +#endif +#ifdef HAVE_OPENCV_OBJDETECT +#include "opencv2/objdetect.hpp" +#endif +#ifdef HAVE_OPENCV_PHOTO +#include "opencv2/photo.hpp" +#endif +#ifdef HAVE_OPENCV_SHAPE +#include "opencv2/shape.hpp" +#endif +#ifdef HAVE_OPENCV_STITCHING +#include "opencv2/stitching.hpp" +#endif +#ifdef HAVE_OPENCV_SUPERRES +#include "opencv2/superres.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEO +#include "opencv2/video.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOIO +#include "opencv2/videoio.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOSTAB +#include "opencv2/videostab.hpp" +#endif +#ifdef HAVE_OPENCV_VIZ +#include "opencv2/viz.hpp" +#endif + +// Finally CUDA specific entries are checked and added +#ifdef HAVE_OPENCV_CUDAARITHM +#include "opencv2/cudaarithm.hpp" +#endif +#ifdef HAVE_OPENCV_CUDABGSEGM +#include "opencv2/cudabgsegm.hpp" +#endif +#ifdef HAVE_OPENCV_CUDACODEC +#include "opencv2/cudacodec.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAFEATURES2D +#include "opencv2/cudafeatures2d.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAFILTERS +#include "opencv2/cudafilters.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAIMGPROC +#include "opencv2/cudaimgproc.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAOBJDETECT +#include "opencv2/cudaobjdetect.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAOPTFLOW +#include "opencv2/cudaoptflow.hpp" +#endif +#ifdef HAVE_OPENCV_CUDASTEREO +#include "opencv2/cudastereo.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAWARPING +#include "opencv2/cudawarping.hpp" +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/opencv_modules.hpp b/thirdparty/linux/include/opencv2/opencv_modules.hpp new file mode 100644 index 0000000..f02ce2e --- /dev/null +++ b/thirdparty/linux/include/opencv2/opencv_modules.hpp @@ -0,0 +1,58 @@ +/* + * ** File generated automatically, do not modify ** + * + * This file defines the list of modules available in current build configuration + * + * +*/ + +// This definition means that OpenCV is built with enabled non-free code. +// For example, patented algorithms for non-profit/non-commercial use only. +/* #undef OPENCV_ENABLE_NONFREE */ + +#define HAVE_OPENCV_ARUCO +#define HAVE_OPENCV_BGSEGM +#define HAVE_OPENCV_BIOINSPIRED +#define HAVE_OPENCV_CALIB3D +#define HAVE_OPENCV_CCALIB +#define HAVE_OPENCV_CORE +#define HAVE_OPENCV_DATASETS +#define HAVE_OPENCV_DNN +#define HAVE_OPENCV_DPM +#define HAVE_OPENCV_FACE +#define HAVE_OPENCV_FEATURES2D +#define HAVE_OPENCV_FLANN +#define HAVE_OPENCV_FREETYPE +#define HAVE_OPENCV_FUZZY +#define HAVE_OPENCV_HDF +#define HAVE_OPENCV_HIGHGUI +#define HAVE_OPENCV_IMGCODECS +#define HAVE_OPENCV_IMGPROC +#define HAVE_OPENCV_LINE_DESCRIPTOR +#define HAVE_OPENCV_ML +#define HAVE_OPENCV_OBJDETECT +#define HAVE_OPENCV_OPTFLOW +#define HAVE_OPENCV_PHASE_UNWRAPPING +#define HAVE_OPENCV_PHOTO +#define HAVE_OPENCV_PLOT +#define HAVE_OPENCV_REG +#define HAVE_OPENCV_RGBD +#define HAVE_OPENCV_SALIENCY +#define HAVE_OPENCV_SHAPE +#define HAVE_OPENCV_STEREO +#define HAVE_OPENCV_STITCHING +#define HAVE_OPENCV_STRUCTURED_LIGHT +#define HAVE_OPENCV_SUPERRES +#define HAVE_OPENCV_SURFACE_MATCHING +#define HAVE_OPENCV_TEXT +#define HAVE_OPENCV_TRACKING +#define HAVE_OPENCV_VIDEO +#define HAVE_OPENCV_VIDEOIO +#define HAVE_OPENCV_VIDEOSTAB +#define HAVE_OPENCV_VIZ +#define HAVE_OPENCV_XFEATURES2D +#define HAVE_OPENCV_XIMGPROC +#define HAVE_OPENCV_XOBJDETECT +#define HAVE_OPENCV_XPHOTO + + diff --git a/thirdparty/linux/include/opencv2/optflow.hpp b/thirdparty/linux/include/opencv2/optflow.hpp new file mode 100644 index 0000000..e68d5b7 --- /dev/null +++ b/thirdparty/linux/include/opencv2/optflow.hpp @@ -0,0 +1,364 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_OPTFLOW_HPP__ +#define __OPENCV_OPTFLOW_HPP__ + +#include "opencv2/core.hpp" +#include "opencv2/video.hpp" + +/** +@defgroup optflow Optical Flow Algorithms + +Dense optical flow algorithms compute motion for each point: + +- cv::optflow::calcOpticalFlowSF +- cv::optflow::createOptFlow_DeepFlow + +Motion templates is alternative technique for detecting motion and computing its direction. +See samples/motempl.py. + +- cv::motempl::updateMotionHistory +- cv::motempl::calcMotionGradient +- cv::motempl::calcGlobalOrientation +- cv::motempl::segmentMotion + +Functions reading and writing .flo files in "Middlebury" format, see: + +- cv::optflow::readOpticalFlow +- cv::optflow::writeOpticalFlow + + */ + +#include "opencv2/optflow/pcaflow.hpp" +#include "opencv2/optflow/sparse_matching_gpc.hpp" + +namespace cv +{ +namespace optflow +{ + +//! @addtogroup optflow +//! @{ + +/** @overload */ +CV_EXPORTS_W void calcOpticalFlowSF( InputArray from, InputArray to, OutputArray flow, + int layers, int averaging_block_size, int max_flow); + +/** @brief Calculate an optical flow using "SimpleFlow" algorithm. + +@param from First 8-bit 3-channel image. +@param to Second 8-bit 3-channel image of the same size as prev +@param flow computed flow image that has the same size as prev and type CV_32FC2 +@param layers Number of layers +@param averaging_block_size Size of block through which we sum up when calculate cost function +for pixel +@param max_flow maximal flow that we search at each level +@param sigma_dist vector smooth spatial sigma parameter +@param sigma_color vector smooth color sigma parameter +@param postprocess_window window size for postprocess cross bilateral filter +@param sigma_dist_fix spatial sigma for postprocess cross bilateralf filter +@param sigma_color_fix color sigma for postprocess cross bilateral filter +@param occ_thr threshold for detecting occlusions +@param upscale_averaging_radius window size for bilateral upscale operation +@param upscale_sigma_dist spatial sigma for bilateral upscale operation +@param upscale_sigma_color color sigma for bilateral upscale operation +@param speed_up_thr threshold to detect point with irregular flow - where flow should be +recalculated after upscale + +See @cite Tao2012 . And site of project - . + +@note + - An example using the simpleFlow algorithm can be found at samples/simpleflow_demo.cpp + */ +CV_EXPORTS_W void calcOpticalFlowSF( InputArray from, InputArray to, OutputArray flow, int layers, + int averaging_block_size, int max_flow, + double sigma_dist, double sigma_color, int postprocess_window, + double sigma_dist_fix, double sigma_color_fix, double occ_thr, + int upscale_averaging_radius, double upscale_sigma_dist, + double upscale_sigma_color, double speed_up_thr ); + +/** @brief Fast dense optical flow based on PyrLK sparse matches interpolation. + +@param from first 8-bit 3-channel or 1-channel image. +@param to second 8-bit 3-channel or 1-channel image of the same size as from +@param flow computed flow image that has the same size as from and CV_32FC2 type +@param grid_step stride used in sparse match computation. Lower values usually + result in higher quality but slow down the algorithm. +@param k number of nearest-neighbor matches considered, when fitting a locally affine + model. Lower values can make the algorithm noticeably faster at the cost of + some quality degradation. +@param sigma parameter defining how fast the weights decrease in the locally-weighted affine + fitting. Higher values can help preserve fine details, lower values can help to get rid + of the noise in the output flow. +@param use_post_proc defines whether the ximgproc::fastGlobalSmootherFilter() is used + for post-processing after interpolation +@param fgs_lambda see the respective parameter of the ximgproc::fastGlobalSmootherFilter() +@param fgs_sigma see the respective parameter of the ximgproc::fastGlobalSmootherFilter() + */ +CV_EXPORTS_W void calcOpticalFlowSparseToDense ( InputArray from, InputArray to, OutputArray flow, + int grid_step = 8, int k = 128, float sigma = 0.05f, + bool use_post_proc = true, float fgs_lambda = 500.0f, + float fgs_sigma = 1.5f ); + +/** @brief Read a .flo file + +@param path Path to the file to be loaded + +The function readOpticalFlow loads a flow field from a file and returns it as a single matrix. +Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the +flow in the horizontal direction (u), second - vertical (v). + */ +CV_EXPORTS_W Mat readOpticalFlow( const String& path ); +/** @brief Write a .flo to disk + +@param path Path to the file to be written +@param flow Flow field to be stored + +The function stores a flow field in a file, returns true on success, false otherwise. +The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds +to the flow in the horizontal direction (u), second - vertical (v). + */ +CV_EXPORTS_W bool writeOpticalFlow( const String& path, InputArray flow ); + +/** @brief Variational optical flow refinement + +This class implements variational refinement of the input flow field, i.e. +it uses input flow to initialize the minimization of the following functional: +\f$E(U) = \int_{\Omega} \delta \Psi(E_I) + \gamma \Psi(E_G) + \alpha \Psi(E_S) \f$, +where \f$E_I,E_G,E_S\f$ are color constancy, gradient constancy and smoothness terms +respectively. \f$\Psi(s^2)=\sqrt{s^2+\epsilon^2}\f$ is a robust penalizer to limit the +influence of outliers. A complete formulation and a description of the minimization +procedure can be found in @cite Brox2004 +*/ +class CV_EXPORTS_W VariationalRefinement : public DenseOpticalFlow +{ +public: + /** @brief @ref calc function overload to handle separate horizontal (u) and vertical (v) flow components + (to avoid extra splits/merges) */ + CV_WRAP virtual void calcUV(InputArray I0, InputArray I1, InputOutputArray flow_u, InputOutputArray flow_v) = 0; + + /** @brief Number of outer (fixed-point) iterations in the minimization procedure. + @see setFixedPointIterations */ + CV_WRAP virtual int getFixedPointIterations() const = 0; + /** @copybrief getFixedPointIterations @see getFixedPointIterations */ + CV_WRAP virtual void setFixedPointIterations(int val) = 0; + + /** @brief Number of inner successive over-relaxation (SOR) iterations + in the minimization procedure to solve the respective linear system. + @see setSorIterations */ + CV_WRAP virtual int getSorIterations() const = 0; + /** @copybrief getSorIterations @see getSorIterations */ + CV_WRAP virtual void setSorIterations(int val) = 0; + + /** @brief Relaxation factor in SOR + @see setOmega */ + CV_WRAP virtual float getOmega() const = 0; + /** @copybrief getOmega @see getOmega */ + CV_WRAP virtual void setOmega(float val) = 0; + + /** @brief Weight of the smoothness term + @see setAlpha */ + CV_WRAP virtual float getAlpha() const = 0; + /** @copybrief getAlpha @see getAlpha */ + CV_WRAP virtual void setAlpha(float val) = 0; + + /** @brief Weight of the color constancy term + @see setDelta */ + CV_WRAP virtual float getDelta() const = 0; + /** @copybrief getDelta @see getDelta */ + CV_WRAP virtual void setDelta(float val) = 0; + + /** @brief Weight of the gradient constancy term + @see setGamma */ + CV_WRAP virtual float getGamma() const = 0; + /** @copybrief getGamma @see getGamma */ + CV_WRAP virtual void setGamma(float val) = 0; +}; + +/** @brief Creates an instance of VariationalRefinement +*/ +CV_EXPORTS_W Ptr createVariationalFlowRefinement(); + +/** @brief DeepFlow optical flow algorithm implementation. + +The class implements the DeepFlow optical flow algorithm described in @cite Weinzaepfel2013 . See +also . +Parameters - class fields - that may be modified after creating a class instance: +- member float alpha +Smoothness assumption weight +- member float delta +Color constancy assumption weight +- member float gamma +Gradient constancy weight +- member float sigma +Gaussian smoothing parameter +- member int minSize +Minimal dimension of an image in the pyramid (next, smaller images in the pyramid are generated +until one of the dimensions reaches this size) +- member float downscaleFactor +Scaling factor in the image pyramid (must be \< 1) +- member int fixedPointIterations +How many iterations on each level of the pyramid +- member int sorIterations +Iterations of Succesive Over-Relaxation (solver) +- member float omega +Relaxation factor in SOR + */ +CV_EXPORTS_W Ptr createOptFlow_DeepFlow(); + +//! Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF() +CV_EXPORTS_W Ptr createOptFlow_SimpleFlow(); + +//! Additional interface to the Farneback's algorithm - calcOpticalFlowFarneback() +CV_EXPORTS_W Ptr createOptFlow_Farneback(); + +//! Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense() +CV_EXPORTS_W Ptr createOptFlow_SparseToDense(); + +/** @brief DIS optical flow algorithm. + +This class implements the Dense Inverse Search (DIS) optical flow algorithm. More +details about the algorithm can be found at @cite Kroeger2016 . Includes three presets with preselected +parameters to provide reasonable trade-off between speed and quality. However, even the slowest preset is +still relatively fast, use DeepFlow if you need better quality and don't care about speed. + +This implementation includes several additional features compared to the algorithm described in the paper, +including spatial propagation of flow vectors (@ref getUseSpatialPropagation), as well as an option to +utilize an initial flow approximation passed to @ref calc (which is, essentially, temporal propagation, +if the previous frame's flow field is passed). +*/ +class CV_EXPORTS_W DISOpticalFlow : public DenseOpticalFlow +{ +public: + enum + { + PRESET_ULTRAFAST = 0, + PRESET_FAST = 1, + PRESET_MEDIUM = 2 + }; + + /** @brief Finest level of the Gaussian pyramid on which the flow is computed (zero level + corresponds to the original image resolution). The final flow is obtained by bilinear upscaling. + @see setFinestScale */ + CV_WRAP virtual int getFinestScale() const = 0; + /** @copybrief getFinestScale @see getFinestScale */ + CV_WRAP virtual void setFinestScale(int val) = 0; + + /** @brief Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well + enough in most cases. + @see setPatchSize */ + CV_WRAP virtual int getPatchSize() const = 0; + /** @copybrief getPatchSize @see getPatchSize */ + CV_WRAP virtual void setPatchSize(int val) = 0; + + /** @brief Stride between neighbor patches. Must be less than patch size. Lower values correspond + to higher flow quality. + @see setPatchStride */ + CV_WRAP virtual int getPatchStride() const = 0; + /** @copybrief getPatchStride @see getPatchStride */ + CV_WRAP virtual void setPatchStride(int val) = 0; + + /** @brief Maximum number of gradient descent iterations in the patch inverse search stage. Higher values + may improve quality in some cases. + @see setGradientDescentIterations */ + CV_WRAP virtual int getGradientDescentIterations() const = 0; + /** @copybrief getGradientDescentIterations @see getGradientDescentIterations */ + CV_WRAP virtual void setGradientDescentIterations(int val) = 0; + + /** @brief Number of fixed point iterations of variational refinement per scale. Set to zero to + disable variational refinement completely. Higher values will typically result in more smooth and + high-quality flow. + @see setGradientDescentIterations */ + CV_WRAP virtual int getVariationalRefinementIterations() const = 0; + /** @copybrief getGradientDescentIterations @see getGradientDescentIterations */ + CV_WRAP virtual void setVariationalRefinementIterations(int val) = 0; + + /** @brief Weight of the smoothness term + @see setVariationalRefinementAlpha */ + CV_WRAP virtual float getVariationalRefinementAlpha() const = 0; + /** @copybrief getVariationalRefinementAlpha @see getVariationalRefinementAlpha */ + CV_WRAP virtual void setVariationalRefinementAlpha(float val) = 0; + + /** @brief Weight of the color constancy term + @see setVariationalRefinementDelta */ + CV_WRAP virtual float getVariationalRefinementDelta() const = 0; + /** @copybrief getVariationalRefinementDelta @see getVariationalRefinementDelta */ + CV_WRAP virtual void setVariationalRefinementDelta(float val) = 0; + + /** @brief Weight of the gradient constancy term + @see setVariationalRefinementGamma */ + CV_WRAP virtual float getVariationalRefinementGamma() const = 0; + /** @copybrief getVariationalRefinementGamma @see getVariationalRefinementGamma */ + CV_WRAP virtual void setVariationalRefinementGamma(float val) = 0; + + + /** @brief Whether to use mean-normalization of patches when computing patch distance. It is turned on + by default as it typically provides a noticeable quality boost because of increased robustness to + illumination variations. Turn it off if you are certain that your sequence doesn't contain any changes + in illumination. + @see setUseMeanNormalization */ + CV_WRAP virtual bool getUseMeanNormalization() const = 0; + /** @copybrief getUseMeanNormalization @see getUseMeanNormalization */ + CV_WRAP virtual void setUseMeanNormalization(bool val) = 0; + + /** @brief Whether to use spatial propagation of good optical flow vectors. This option is turned on by + default, as it tends to work better on average and can sometimes help recover from major errors + introduced by the coarse-to-fine scheme employed by the DIS optical flow algorithm. Turning this + option off can make the output flow field a bit smoother, however. + @see setUseSpatialPropagation */ + CV_WRAP virtual bool getUseSpatialPropagation() const = 0; + /** @copybrief getUseSpatialPropagation @see getUseSpatialPropagation */ + CV_WRAP virtual void setUseSpatialPropagation(bool val) = 0; +}; + +/** @brief Creates an instance of DISOpticalFlow + +@param preset one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM +*/ +CV_EXPORTS_W Ptr createOptFlow_DIS(int preset = DISOpticalFlow::PRESET_FAST); + +//! @} + +} //optflow +} + +#include "opencv2/optflow/motempl.hpp" + +#endif diff --git a/thirdparty/linux/include/opencv2/optflow/motempl.hpp b/thirdparty/linux/include/opencv2/optflow/motempl.hpp new file mode 100644 index 0000000..aeea9e8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/optflow/motempl.hpp @@ -0,0 +1,147 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_OPTFLOW_MOTEMPL_HPP__ +#define __OPENCV_OPTFLOW_MOTEMPL_HPP__ + +#include "opencv2/core.hpp" + +namespace cv +{ +namespace motempl +{ + +//! @addtogroup optflow +//! @{ + +/** @brief Updates the motion history image by a moving silhouette. + +@param silhouette Silhouette mask that has non-zero pixels where the motion occurs. +@param mhi Motion history image that is updated by the function (single-channel, 32-bit +floating-point). +@param timestamp Current time in milliseconds or other units. +@param duration Maximal duration of the motion track in the same units as timestamp . + +The function updates the motion history image as follows: + +\f[\texttt{mhi} (x,y)= \forkthree{\texttt{timestamp}}{if \(\texttt{silhouette}(x,y) \ne 0\)}{0}{if \(\texttt{silhouette}(x,y) = 0\) and \(\texttt{mhi} < (\texttt{timestamp} - \texttt{duration})\)}{\texttt{mhi}(x,y)}{otherwise}\f] + +That is, MHI pixels where the motion occurs are set to the current timestamp , while the pixels +where the motion happened last time a long time ago are cleared. + +The function, together with calcMotionGradient and calcGlobalOrientation , implements a motion +templates technique described in @cite Davis97 and @cite Bradski00 . + */ +CV_EXPORTS_W void updateMotionHistory( InputArray silhouette, InputOutputArray mhi, + double timestamp, double duration ); + +/** @brief Calculates a gradient orientation of a motion history image. + +@param mhi Motion history single-channel floating-point image. +@param mask Output mask image that has the type CV_8UC1 and the same size as mhi . Its non-zero +elements mark pixels where the motion gradient data is correct. +@param orientation Output motion gradient orientation image that has the same type and the same +size as mhi . Each pixel of the image is a motion orientation, from 0 to 360 degrees. +@param delta1 Minimal (or maximal) allowed difference between mhi values within a pixel +neighborhood. +@param delta2 Maximal (or minimal) allowed difference between mhi values within a pixel +neighborhood. That is, the function finds the minimum ( \f$m(x,y)\f$ ) and maximum ( \f$M(x,y)\f$ ) mhi +values over \f$3 \times 3\f$ neighborhood of each pixel and marks the motion orientation at \f$(x, y)\f$ +as valid only if +\f[\min ( \texttt{delta1} , \texttt{delta2} ) \le M(x,y)-m(x,y) \le \max ( \texttt{delta1} , \texttt{delta2} ).\f] +@param apertureSize Aperture size of the Sobel operator. + +The function calculates a gradient orientation at each pixel \f$(x, y)\f$ as: + +\f[\texttt{orientation} (x,y)= \arctan{\frac{d\texttt{mhi}/dy}{d\texttt{mhi}/dx}}\f] + +In fact, fastAtan2 and phase are used so that the computed angle is measured in degrees and covers +the full range 0..360. Also, the mask is filled to indicate pixels where the computed angle is +valid. + +@note + - (Python) An example on how to perform a motion template technique can be found at + opencv_source_code/samples/python2/motempl.py + */ +CV_EXPORTS_W void calcMotionGradient( InputArray mhi, OutputArray mask, OutputArray orientation, + double delta1, double delta2, int apertureSize = 3 ); + +/** @brief Calculates a global motion orientation in a selected region. + +@param orientation Motion gradient orientation image calculated by the function calcMotionGradient +@param mask Mask image. It may be a conjunction of a valid gradient mask, also calculated by +calcMotionGradient , and the mask of a region whose direction needs to be calculated. +@param mhi Motion history image calculated by updateMotionHistory . +@param timestamp Timestamp passed to updateMotionHistory . +@param duration Maximum duration of a motion track in milliseconds, passed to updateMotionHistory + +The function calculates an average motion direction in the selected region and returns the angle +between 0 degrees and 360 degrees. The average direction is computed from the weighted orientation +histogram, where a recent motion has a larger weight and the motion occurred in the past has a +smaller weight, as recorded in mhi . + */ +CV_EXPORTS_W double calcGlobalOrientation( InputArray orientation, InputArray mask, InputArray mhi, + double timestamp, double duration ); + +/** @brief Splits a motion history image into a few parts corresponding to separate independent motions (for +example, left hand, right hand). + +@param mhi Motion history image. +@param segmask Image where the found mask should be stored, single-channel, 32-bit floating-point. +@param boundingRects Vector containing ROIs of motion connected components. +@param timestamp Current time in milliseconds or other units. +@param segThresh Segmentation threshold that is recommended to be equal to the interval between +motion history "steps" or greater. + +The function finds all of the motion segments and marks them in segmask with individual values +(1,2,...). It also computes a vector with ROIs of motion connected components. After that the motion +direction for every component can be calculated with calcGlobalOrientation using the extracted mask +of the particular component. + */ +CV_EXPORTS_W void segmentMotion( InputArray mhi, OutputArray segmask, + CV_OUT std::vector& boundingRects, + double timestamp, double segThresh ); + + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/optflow/pcaflow.hpp b/thirdparty/linux/include/opencv2/optflow/pcaflow.hpp new file mode 100644 index 0000000..6645363 --- /dev/null +++ b/thirdparty/linux/include/opencv2/optflow/pcaflow.hpp @@ -0,0 +1,149 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2016, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +/** + * @file pcaflow.hpp + * @author Vladislav Samsonov + * @brief Implementation of the PCAFlow algorithm from the following paper: + * http://files.is.tue.mpg.de/black/papers/cvpr2015_pcaflow.pdf + * + * @cite Wulff:CVPR:2015 + * + * There are some key differences which distinguish this algorithm from the original PCAFlow (see paper): + * - Discrete Cosine Transform basis is used instead of basis extracted with PCA. + * Reasoning: DCT basis has comparable performance and it doesn't require additional storage space. + * Also, this decision helps to avoid overloading the algorithm with a lot of external input. + * - Usage of built-in OpenCV feature tracking instead of libviso. +*/ + +#ifndef __OPENCV_OPTFLOW_PCAFLOW_HPP__ +#define __OPENCV_OPTFLOW_PCAFLOW_HPP__ + +#include "opencv2/core.hpp" +#include "opencv2/video.hpp" + +namespace cv +{ +namespace optflow +{ + +//! @addtogroup optflow +//! @{ + +/** @brief + * This class can be used for imposing a learned prior on the resulting optical flow. + * Solution will be regularized according to this prior. + * You need to generate appropriate prior file with "learn_prior.py" script beforehand. + */ +class CV_EXPORTS_W PCAPrior +{ +private: + Mat L1; + Mat L2; + Mat c1; + Mat c2; + +public: + PCAPrior( const char *pathToPrior ); + + int getPadding() const { return L1.size().height; } + + int getBasisSize() const { return L1.size().width; } + + void fillConstraints( float *A1, float *A2, float *b1, float *b2 ) const; +}; + +/** @brief PCAFlow algorithm. + */ +class CV_EXPORTS_W OpticalFlowPCAFlow : public DenseOpticalFlow +{ +protected: + const Ptr prior; + const Size basisSize; + const float sparseRate; // (0 .. 0.1) + const float retainedCornersFraction; // [0 .. 1] + const float occlusionsThreshold; + const float dampingFactor; + const float claheClip; + bool useOpenCL; + +public: + /** @brief Creates an instance of PCAFlow algorithm. + * @param _prior Learned prior or no prior (default). @see cv::optflow::PCAPrior + * @param _basisSize Number of basis vectors. + * @param _sparseRate Controls density of sparse matches. + * @param _retainedCornersFraction Retained corners fraction. + * @param _occlusionsThreshold Occlusion threshold. + * @param _dampingFactor Regularization term for solving least-squares. It is not related to the prior regularization. + * @param _claheClip Clip parameter for CLAHE. + */ + OpticalFlowPCAFlow( Ptr _prior = Ptr(), const Size _basisSize = Size( 18, 14 ), + float _sparseRate = 0.024, float _retainedCornersFraction = 0.2, + float _occlusionsThreshold = 0.0003, float _dampingFactor = 0.00002, float _claheClip = 14 ); + + void calc( InputArray I0, InputArray I1, InputOutputArray flow ); + void collectGarbage(); + +private: + void findSparseFeatures( UMat &from, UMat &to, std::vector &features, + std::vector &predictedFeatures ) const; + + void removeOcclusions( UMat &from, UMat &to, std::vector &features, + std::vector &predictedFeatures ) const; + + void getSystem( OutputArray AOut, OutputArray b1Out, OutputArray b2Out, const std::vector &features, + const std::vector &predictedFeatures, const Size size ); + + void getSystem( OutputArray A1Out, OutputArray A2Out, OutputArray b1Out, OutputArray b2Out, + const std::vector &features, const std::vector &predictedFeatures, + const Size size ); + + OpticalFlowPCAFlow& operator=( const OpticalFlowPCAFlow& ); // make it non-assignable +}; + +/** @brief Creates an instance of PCAFlow +*/ +CV_EXPORTS_W Ptr createOptFlow_PCAFlow(); + +//! @} + +} +} + +#endif diff --git a/thirdparty/linux/include/opencv2/optflow/sparse_matching_gpc.hpp b/thirdparty/linux/include/opencv2/optflow/sparse_matching_gpc.hpp new file mode 100644 index 0000000..3127710 --- /dev/null +++ b/thirdparty/linux/include/opencv2/optflow/sparse_matching_gpc.hpp @@ -0,0 +1,380 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2016, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +/** + * @file sparse_matching_gpc.hpp + * @author Vladislav Samsonov + * @brief Implementation of the Global Patch Collider. + * + * Implementation of the Global Patch Collider algorithm from the following paper: + * http://research.microsoft.com/en-us/um/people/pkohli/papers/wfrik_cvpr2016.pdf + * + * @cite Wang_2016_CVPR + */ + +#ifndef __OPENCV_OPTFLOW_SPARSE_MATCHING_GPC_HPP__ +#define __OPENCV_OPTFLOW_SPARSE_MATCHING_GPC_HPP__ + +#include "opencv2/core.hpp" +#include "opencv2/core/hal/intrin.hpp" +#include "opencv2/imgproc.hpp" + +namespace cv +{ +namespace optflow +{ + +//! @addtogroup optflow +//! @{ + +struct CV_EXPORTS_W GPCPatchDescriptor +{ + static const unsigned nFeatures = 18; //!< number of features in a patch descriptor + Vec< double, nFeatures > feature; + + double dot( const Vec< double, nFeatures > &coef ) const; + + void markAsSeparated() { feature[0] = std::numeric_limits< double >::quiet_NaN(); } + + bool isSeparated() const { return cvIsNaN( feature[0] ) != 0; } +}; + +struct CV_EXPORTS_W GPCPatchSample +{ + GPCPatchDescriptor ref; + GPCPatchDescriptor pos; + GPCPatchDescriptor neg; + + void getDirections( bool &refdir, bool &posdir, bool &negdir, const Vec< double, GPCPatchDescriptor::nFeatures > &coef, double rhs ) const; +}; + +typedef std::vector< GPCPatchSample > GPCSamplesVector; + +/** @brief Descriptor types for the Global Patch Collider. + */ +enum GPCDescType +{ + GPC_DESCRIPTOR_DCT = 0, //!< Better quality but slow + GPC_DESCRIPTOR_WHT //!< Worse quality but much faster +}; + +/** @brief Class encapsulating training samples. + */ +class CV_EXPORTS_W GPCTrainingSamples +{ +private: + GPCSamplesVector samples; + int descriptorType; + +public: + /** @brief This function can be used to extract samples from a pair of images and a ground truth flow. + * Sizes of all the provided vectors must be equal. + */ + static Ptr< GPCTrainingSamples > create( const std::vector< String > &imagesFrom, const std::vector< String > &imagesTo, + const std::vector< String > >, int descriptorType ); + + static Ptr< GPCTrainingSamples > create( InputArrayOfArrays imagesFrom, InputArrayOfArrays imagesTo, InputArrayOfArrays gt, + int descriptorType ); + + size_t size() const { return samples.size(); } + + int type() const { return descriptorType; } + + operator GPCSamplesVector &() { return samples; } +}; + +/** @brief Class encapsulating training parameters. + */ +struct GPCTrainingParams +{ + unsigned maxTreeDepth; //!< Maximum tree depth to stop partitioning. + int minNumberOfSamples; //!< Minimum number of samples in the node to stop partitioning. + int descriptorType; //!< Type of descriptors to use. + bool printProgress; //!< Print progress to stdout. + + GPCTrainingParams( unsigned _maxTreeDepth = 20, int _minNumberOfSamples = 3, GPCDescType _descriptorType = GPC_DESCRIPTOR_DCT, + bool _printProgress = true ) + : maxTreeDepth( _maxTreeDepth ), minNumberOfSamples( _minNumberOfSamples ), descriptorType( _descriptorType ), + printProgress( _printProgress ) + { + CV_Assert( check() ); + } + + GPCTrainingParams( const GPCTrainingParams ¶ms ) + : maxTreeDepth( params.maxTreeDepth ), minNumberOfSamples( params.minNumberOfSamples ), descriptorType( params.descriptorType ), + printProgress( params.printProgress ) + { + CV_Assert( check() ); + } + + bool check() const { return maxTreeDepth > 1 && minNumberOfSamples > 1; } +}; + +/** @brief Class encapsulating matching parameters. + */ +struct GPCMatchingParams +{ + bool useOpenCL; //!< Whether to use OpenCL to speed up the matching. + + GPCMatchingParams( bool _useOpenCL = false ) : useOpenCL( _useOpenCL ) {} + + GPCMatchingParams( const GPCMatchingParams ¶ms ) : useOpenCL( params.useOpenCL ) {} +}; + +/** @brief Class for individual tree. + */ +class CV_EXPORTS_W GPCTree : public Algorithm +{ +public: + struct Node + { + Vec< double, GPCPatchDescriptor::nFeatures > coef; //!< Hyperplane coefficients + double rhs; //!< Bias term of the hyperplane + unsigned left; + unsigned right; + + bool operator==( const Node &n ) const { return coef == n.coef && rhs == n.rhs && left == n.left && right == n.right; } + }; + +private: + typedef GPCSamplesVector::iterator SIter; + + std::vector< Node > nodes; + GPCTrainingParams params; + + bool trainNode( size_t nodeId, SIter begin, SIter end, unsigned depth ); + +public: + void train( GPCTrainingSamples &samples, const GPCTrainingParams params = GPCTrainingParams() ); + + void write( FileStorage &fs ) const; + + void read( const FileNode &fn ); + + unsigned findLeafForPatch( const GPCPatchDescriptor &descr ) const; + + static Ptr< GPCTree > create() { return makePtr< GPCTree >(); } + + bool operator==( const GPCTree &t ) const { return nodes == t.nodes; } + + int getDescriptorType() const { return params.descriptorType; } +}; + +template < int T > class CV_EXPORTS_W GPCForest : public Algorithm +{ +private: + struct Trail + { + unsigned leaf[T]; //!< Inside which leaf of the tree 0..T the patch fell? + Point2i coord; //!< Patch coordinates. + + bool operator==( const Trail &trail ) const { return memcmp( leaf, trail.leaf, sizeof( leaf ) ) == 0; } + + bool operator<( const Trail &trail ) const + { + for ( int i = 0; i < T - 1; ++i ) + if ( leaf[i] != trail.leaf[i] ) + return leaf[i] < trail.leaf[i]; + return leaf[T - 1] < trail.leaf[T - 1]; + } + }; + + class ParallelTrailsFilling : public ParallelLoopBody + { + private: + const GPCForest *forest; + const std::vector< GPCPatchDescriptor > *descr; + std::vector< Trail > *trails; + + ParallelTrailsFilling &operator=( const ParallelTrailsFilling & ); + + public: + ParallelTrailsFilling( const GPCForest *_forest, const std::vector< GPCPatchDescriptor > *_descr, std::vector< Trail > *_trails ) + : forest( _forest ), descr( _descr ), trails( _trails ){}; + + void operator()( const Range &range ) const + { + for ( int t = range.start; t < range.end; ++t ) + for ( size_t i = 0; i < descr->size(); ++i ) + trails->at( i ).leaf[t] = forest->tree[t].findLeafForPatch( descr->at( i ) ); + } + }; + + GPCTree tree[T]; + +public: + /** @brief Train the forest using one sample set for every tree. + * Please, consider using the next method instead of this one for better quality. + */ + void train( GPCTrainingSamples &samples, const GPCTrainingParams params = GPCTrainingParams() ) + { + for ( int i = 0; i < T; ++i ) + tree[i].train( samples, params ); + } + + /** @brief Train the forest using individual samples for each tree. + * It is generally better to use this instead of the first method. + */ + void train( const std::vector< String > &imagesFrom, const std::vector< String > &imagesTo, const std::vector< String > >, + const GPCTrainingParams params = GPCTrainingParams() ) + { + for ( int i = 0; i < T; ++i ) + { + Ptr< GPCTrainingSamples > samples = + GPCTrainingSamples::create( imagesFrom, imagesTo, gt, params.descriptorType ); // Create training set for the tree + tree[i].train( *samples, params ); + } + } + + void train( InputArrayOfArrays imagesFrom, InputArrayOfArrays imagesTo, InputArrayOfArrays gt, + const GPCTrainingParams params = GPCTrainingParams() ) + { + for ( int i = 0; i < T; ++i ) + { + Ptr< GPCTrainingSamples > samples = + GPCTrainingSamples::create( imagesFrom, imagesTo, gt, params.descriptorType ); // Create training set for the tree + tree[i].train( *samples, params ); + } + } + + void write( FileStorage &fs ) const + { + fs << "ntrees" << T << "trees" + << "["; + for ( int i = 0; i < T; ++i ) + { + fs << "{"; + tree[i].write( fs ); + fs << "}"; + } + fs << "]"; + } + + void read( const FileNode &fn ) + { + CV_Assert( T <= (int)fn["ntrees"] ); + FileNodeIterator it = fn["trees"].begin(); + for ( int i = 0; i < T; ++i, ++it ) + tree[i].read( *it ); + } + + /** @brief Find correspondences between two images. + * @param[in] imgFrom First image in a sequence. + * @param[in] imgTo Second image in a sequence. + * @param[out] corr Output vector with pairs of corresponding points. + * @param[in] params Additional matching parameters for fine-tuning. + */ + void findCorrespondences( InputArray imgFrom, InputArray imgTo, std::vector< std::pair< Point2i, Point2i > > &corr, + const GPCMatchingParams params = GPCMatchingParams() ) const; + + static Ptr< GPCForest > create() { return makePtr< GPCForest >(); } +}; + +class CV_EXPORTS_W GPCDetails +{ +public: + static void dropOutliers( std::vector< std::pair< Point2i, Point2i > > &corr ); + + static void getAllDescriptorsForImage( const Mat *imgCh, std::vector< GPCPatchDescriptor > &descr, const GPCMatchingParams &mp, + int type ); + + static void getCoordinatesFromIndex( size_t index, Size sz, int &x, int &y ); +}; + +template < int T > +void GPCForest< T >::findCorrespondences( InputArray imgFrom, InputArray imgTo, std::vector< std::pair< Point2i, Point2i > > &corr, + const GPCMatchingParams params ) const +{ + CV_Assert( imgFrom.channels() == 3 ); + CV_Assert( imgTo.channels() == 3 ); + + Mat from, to; + imgFrom.getMat().convertTo( from, CV_32FC3 ); + imgTo.getMat().convertTo( to, CV_32FC3 ); + cvtColor( from, from, COLOR_BGR2YCrCb ); + cvtColor( to, to, COLOR_BGR2YCrCb ); + + Mat fromCh[3], toCh[3]; + split( from, fromCh ); + split( to, toCh ); + + std::vector< GPCPatchDescriptor > descr; + GPCDetails::getAllDescriptorsForImage( fromCh, descr, params, tree[0].getDescriptorType() ); + std::vector< Trail > trailsFrom( descr.size() ), trailsTo( descr.size() ); + + for ( size_t i = 0; i < descr.size(); ++i ) + GPCDetails::getCoordinatesFromIndex( i, from.size(), trailsFrom[i].coord.x, trailsFrom[i].coord.y ); + parallel_for_( Range( 0, T ), ParallelTrailsFilling( this, &descr, &trailsFrom ) ); + + descr.clear(); + GPCDetails::getAllDescriptorsForImage( toCh, descr, params, tree[0].getDescriptorType() ); + + for ( size_t i = 0; i < descr.size(); ++i ) + GPCDetails::getCoordinatesFromIndex( i, to.size(), trailsTo[i].coord.x, trailsTo[i].coord.y ); + parallel_for_( Range( 0, T ), ParallelTrailsFilling( this, &descr, &trailsTo ) ); + + std::sort( trailsFrom.begin(), trailsFrom.end() ); + std::sort( trailsTo.begin(), trailsTo.end() ); + + for ( size_t i = 0; i < trailsFrom.size(); ++i ) + { + bool uniq = true; + while ( i + 1 < trailsFrom.size() && trailsFrom[i] == trailsFrom[i + 1] ) + ++i, uniq = false; + if ( uniq ) + { + typename std::vector< Trail >::const_iterator lb = std::lower_bound( trailsTo.begin(), trailsTo.end(), trailsFrom[i] ); + if ( lb != trailsTo.end() && *lb == trailsFrom[i] && ( ( lb + 1 ) == trailsTo.end() || !( *lb == *( lb + 1 ) ) ) ) + corr.push_back( std::make_pair( trailsFrom[i].coord, lb->coord ) ); + } + } + + GPCDetails::dropOutliers( corr ); +} + +//! @} + +} // namespace optflow + +CV_EXPORTS void write( FileStorage &fs, const String &name, const optflow::GPCTree::Node &node ); + +CV_EXPORTS void read( const FileNode &fn, optflow::GPCTree::Node &node, optflow::GPCTree::Node ); +} // namespace cv + +#endif diff --git a/thirdparty/linux/include/opencv2/phase_unwrapping.hpp b/thirdparty/linux/include/opencv2/phase_unwrapping.hpp new file mode 100644 index 0000000..0e15e71 --- /dev/null +++ b/thirdparty/linux/include/opencv2/phase_unwrapping.hpp @@ -0,0 +1,61 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// + // + // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + // + // By downloading, copying, installing or using the software you agree to this license. + // If you do not agree to this license, do not download, install, + // copy or use the software. + // + // + // License Agreement + // For Open Source Computer Vision Library + // + // Copyright (C) 2015, OpenCV Foundation, all rights reserved. + // Third party copyrights are property of their respective owners. + // + // Redistribution and use in source and binary forms, with or without modification, + // are permitted provided that the following conditions are met: + // + // * Redistribution's of source code must retain the above copyright notice, + // this list of conditions and the following disclaimer. + // + // * Redistribution's in binary form must reproduce the above copyright notice, + // this list of conditions and the following disclaimer in the documentation + // and/or other materials provided with the distribution. + // + // * The name of the copyright holders may not be used to endorse or promote products + // derived from this software without specific prior written permission. + // + // This software is provided by the copyright holders and contributors "as is" and + // any express or implied warranties, including, but not limited to, the implied + // warranties of merchantability and fitness for a particular purpose are disclaimed. + // In no event shall the Intel Corporation or contributors be liable for any direct, + // indirect, incidental, special, exemplary, or consequential damages + // (including, but not limited to, procurement of substitute goods or services; + // loss of use, data, or profits; or business interruption) however caused + // and on any theory of liability, whether in contract, strict liability, + // or tort (including negligence or otherwise) arising in any way out of + // the use of this software, even if advised of the possibility of such damage. + // + //M*/ + +#include "opencv2/phase_unwrapping/phase_unwrapping.hpp" +#include "opencv2/phase_unwrapping/histogramphaseunwrapping.hpp" + +/** @defgroup phase_unwrapping Phase Unwrapping API + +Two-dimensional phase unwrapping is found in different applications like terrain elevation estimation +in synthetic aperture radar (SAR), field mapping in magnetic resonance imaging or as a way of finding +corresponding pixels in structured light reconstruction with sinusoidal patterns. + +Given a phase map, wrapped between [-pi; pi], phase unwrapping aims at finding the "true" phase map +by adding the right number of 2*pi to each pixel. + +The problem is straightforward for perfect wrapped phase map, but real data are usually not noise-free. +Among the different algorithms that were developed, quality-guided phase unwrapping methods are fast +and efficient. They follow a path that unwraps high quality pixels first, +avoiding error propagation from the start. + +In this module, a quality-guided phase unwrapping is implemented following the approach described in @cite histogramUnwrapping . + +*/ \ No newline at end of file diff --git a/thirdparty/linux/include/opencv2/phase_unwrapping/histogramphaseunwrapping.hpp b/thirdparty/linux/include/opencv2/phase_unwrapping/histogramphaseunwrapping.hpp new file mode 100644 index 0000000..e75d889 --- /dev/null +++ b/thirdparty/linux/include/opencv2/phase_unwrapping/histogramphaseunwrapping.hpp @@ -0,0 +1,107 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// + // + // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + // + // By downloading, copying, installing or using the software you agree to this license. + // If you do not agree to this license, do not download, install, + // copy or use the software. + // + // + // License Agreement + // For Open Source Computer Vision Library + // + // Copyright (C) 2015, OpenCV Foundation, all rights reserved. + // Third party copyrights are property of their respective owners. + // + // Redistribution and use in source and binary forms, with or without modification, + // are permitted provided that the following conditions are met: + // + // * Redistribution's of source code must retain the above copyright notice, + // this list of conditions and the following disclaimer. + // + // * Redistribution's in binary form must reproduce the above copyright notice, + // this list of conditions and the following disclaimer in the documentation + // and/or other materials provided with the distribution. + // + // * The name of the copyright holders may not be used to endorse or promote products + // derived from this software without specific prior written permission. + // + // This software is provided by the copyright holders and contributors "as is" and + // any express or implied warranties, including, but not limited to, the implied + // warranties of merchantability and fitness for a particular purpose are disclaimed. + // In no event shall the Intel Corporation or contributors be liable for any direct, + // indirect, incidental, special, exemplary, or consequential damages + // (including, but not limited to, procurement of substitute goods or services; + // loss of use, data, or profits; or business interruption) however caused + // and on any theory of liability, whether in contract, strict liability, + // or tort (including negligence or otherwise) arising in any way out of + // the use of this software, even if advised of the possibility of such damage. + // + //M*/ + +#ifndef __OPENCV_HISTOGRAM_PHASE_UNWRAPPING_HPP__ +#define __OPENCV_HISTOGRAM_PHASE_UNWRAPPING_HPP__ + +#include "opencv2/core.hpp" +#include +#include "opencv2/phase_unwrapping/phase_unwrapping.hpp" + +namespace cv { +namespace phase_unwrapping { +//! @addtogroup phase_unwrapping +//! @{ + + /** @brief Class implementing two-dimensional phase unwrapping based on @cite histogramUnwrapping + * This algorithm belongs to the quality-guided phase unwrapping methods. + * First, it computes a reliability map from second differences between a pixel and its eight neighbours. + * Reliability values lie between 0 and 16*pi*pi. Then, this reliability map is used to compute + * the reliabilities of "edges". An edge is an entity defined by two pixels that are connected + * horizontally or vertically. Its reliability is found by adding the the reliabilities of the + * two pixels connected through it. Edges are sorted in a histogram based on their reliability values. + * This histogram is then used to unwrap pixels, starting from the highest quality pixel. + + * The wrapped phase map and the unwrapped result are stored in CV_32FC1 Mat. + */ +class CV_EXPORTS_W HistogramPhaseUnwrapping : public PhaseUnwrapping +{ + +public: + /** + * @brief Parameters of phaseUnwrapping constructor. + + * @param width Phase map width. + * @param height Phase map height. + * @param histThresh Bins in the histogram are not of equal size. Default value is 3*pi*pi. The one before "histThresh" value are smaller. + * @param nbrOfSmallBins Number of bins between 0 and "histThresh". Default value is 10. + * @param nbrOfLargeBins Number of bins between "histThresh" and 32*pi*pi (highest edge reliability value). Default value is 5. + */ + struct CV_EXPORTS Params + { + Params(); + int width; + int height; + float histThresh; + int nbrOfSmallBins; + int nbrOfLargeBins; + }; + /** + * @brief Constructor + + * @param parameters HistogramPhaseUnwrapping parameters HistogramPhaseUnwrapping::Params: width,height of the phase map and histogram characteristics. + */ + static Ptr create( const HistogramPhaseUnwrapping::Params ¶meters = + HistogramPhaseUnwrapping::Params() ); + + /** + * @brief Get the reliability map computed from the wrapped phase map. + + * @param reliabilityMap Image where the reliability map is stored. + */ + CV_WRAP + virtual void getInverseReliabilityMap( OutputArray reliabilityMap ) = 0; +}; + +//! @} +} +} +#endif \ No newline at end of file diff --git a/thirdparty/linux/include/opencv2/phase_unwrapping/phase_unwrapping.hpp b/thirdparty/linux/include/opencv2/phase_unwrapping/phase_unwrapping.hpp new file mode 100644 index 0000000..5b5cb51 --- /dev/null +++ b/thirdparty/linux/include/opencv2/phase_unwrapping/phase_unwrapping.hpp @@ -0,0 +1,74 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// + // + // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + // + // By downloading, copying, installing or using the software you agree to this license. + // If you do not agree to this license, do not download, install, + // copy or use the software. + // + // + // License Agreement + // For Open Source Computer Vision Library + // + // Copyright (C) 2015, OpenCV Foundation, all rights reserved. + // Third party copyrights are property of their respective owners. + // + // Redistribution and use in source and binary forms, with or without modification, + // are permitted provided that the following conditions are met: + // + // * Redistribution's of source code must retain the above copyright notice, + // this list of conditions and the following disclaimer. + // + // * Redistribution's in binary form must reproduce the above copyright notice, + // this list of conditions and the following disclaimer in the documentation + // and/or other materials provided with the distribution. + // + // * The name of the copyright holders may not be used to endorse or promote products + // derived from this software without specific prior written permission. + // + // This software is provided by the copyright holders and contributors "as is" and + // any express or implied warranties, including, but not limited to, the implied + // warranties of merchantability and fitness for a particular purpose are disclaimed. + // In no event shall the Intel Corporation or contributors be liable for any direct, + // indirect, incidental, special, exemplary, or consequential damages + // (including, but not limited to, procurement of substitute goods or services; + // loss of use, data, or profits; or business interruption) however caused + // and on any theory of liability, whether in contract, strict liability, + // or tort (including negligence or otherwise) arising in any way out of + // the use of this software, even if advised of the possibility of such damage. + // + //M*/ + +#ifndef __OPENCV_PHASE_UNWRAPPING_HPP__ +#define __OPENCV_PHASE_UNWRAPPING_HPP__ + +#include "opencv2/core.hpp" + +namespace cv { +namespace phase_unwrapping { +//! @addtogroup phase_unwrapping +//! @{ + + /** + @brief Abstract base class for phase unwrapping. + */ +class CV_EXPORTS_W PhaseUnwrapping : public virtual Algorithm +{ +public: + /** + * @brief Unwraps a 2D phase map. + + * @param wrappedPhaseMap The wrapped phase map that needs to be unwrapped. + * @param unwrappedPhaseMap The unwrapped phase map. + * @param shadowMask Optional parameter used when some pixels do not hold any phase information in the wrapped phase map. + */ + CV_WRAP + virtual void unwrapPhaseMap( InputArray wrappedPhaseMap, OutputArray unwrappedPhaseMap, + InputArray shadowMask = noArray() ) = 0; + +}; + +//! @} +} +} +#endif \ No newline at end of file diff --git a/thirdparty/linux/include/opencv2/photo.hpp b/thirdparty/linux/include/opencv2/photo.hpp new file mode 100644 index 0000000..a445dd3 --- /dev/null +++ b/thirdparty/linux/include/opencv2/photo.hpp @@ -0,0 +1,870 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_PHOTO_HPP +#define OPENCV_PHOTO_HPP + +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" + +/** +@defgroup photo Computational Photography +@{ + @defgroup photo_denoise Denoising + @defgroup photo_hdr HDR imaging + +This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment, +camera calibration with multiple exposures and exposure fusion. + + @defgroup photo_clone Seamless Cloning + @defgroup photo_render Non-Photorealistic Rendering + @defgroup photo_c C API +@} + */ + +namespace cv +{ + +//! @addtogroup photo +//! @{ + +//! the inpainting algorithm +enum +{ + INPAINT_NS = 0, // Navier-Stokes algorithm + INPAINT_TELEA = 1 // A. Telea algorithm +}; + +enum +{ + NORMAL_CLONE = 1, + MIXED_CLONE = 2, + MONOCHROME_TRANSFER = 3 +}; + +enum +{ + RECURS_FILTER = 1, + NORMCONV_FILTER = 2 +}; + +/** @brief Restores the selected region in an image using the region neighborhood. + +@param src Input 8-bit 1-channel or 3-channel image. +@param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that +needs to be inpainted. +@param dst Output image with the same size and type as src . +@param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered +by the algorithm. +@param flags Inpainting method that could be one of the following: +- **INPAINT_NS** Navier-Stokes based method [Navier01] +- **INPAINT_TELEA** Method by Alexandru Telea @cite Telea04 . + +The function reconstructs the selected image area from the pixel near the area boundary. The +function may be used to remove dust and scratches from a scanned photo, or to remove undesirable +objects from still images or video. See for more details. + +@note + - An example using the inpainting technique can be found at + opencv_source_code/samples/cpp/inpaint.cpp + - (Python) An example using the inpainting technique can be found at + opencv_source_code/samples/python/inpaint.py + */ +CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask, + OutputArray dst, double inpaintRadius, int flags ); + +//! @addtogroup photo_denoise +//! @{ + +/** @brief Perform image denoising using Non-local Means Denoising algorithm + with several computational +optimizations. Noise expected to be a gaussian white noise + +@param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. +@param dst Output image with the same size and type as src . +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Parameter regulating filter strength. Big h value perfectly removes noise but also +removes image details, smaller h value preserves details but also preserves some noise + +This function expected to be applied to grayscale images. For colored images look at +fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored +image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting +image to CIELAB colorspace and then separately denoise L and AB components with different h +parameter. + */ +CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3, + int templateWindowSize = 7, int searchWindowSize = 21); + +/** @brief Perform image denoising using Non-local Means Denoising algorithm + with several computational +optimizations. Noise expected to be a gaussian white noise + +@param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel, +2-channel, 3-channel or 4-channel image. +@param dst Output image with the same size and type as src . +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Array of parameters regulating filter strength, either one +parameter applied to all channels or one per channel in dst. Big h value +perfectly removes noise but also removes image details, smaller h +value preserves details but also preserves some noise +@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 + +This function expected to be applied to grayscale images. For colored images look at +fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored +image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting +image to CIELAB colorspace and then separately denoise L and AB components with different h +parameter. + */ +CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, + const std::vector& h, + int templateWindowSize = 7, int searchWindowSize = 21, + int normType = NORM_L2); + +/** @brief Modification of fastNlMeansDenoising function for colored images + +@param src Input 8-bit 3-channel image. +@param dst Output image with the same size and type as src . +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly +removes noise but also removes image details, smaller h value preserves details but also preserves +some noise +@param hColor The same as h but for color components. For most images value equals 10 +will be enough to remove colored noise and do not distort colors + +The function converts image to CIELAB colorspace and then separately denoise L and AB components +with given h parameters using fastNlMeansDenoising function. + */ +CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst, + float h = 3, float hColor = 3, + int templateWindowSize = 7, int searchWindowSize = 21); + +/** @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been +captured in small period of time. For example video. This version of the function is for grayscale +images or for manual manipulation with colorspaces. For more details see + + +@param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or +4-channel images sequence. All images should have the same type and +size. +@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence +@param temporalWindowSize Number of surrounding images to use for target image denoising. Should +be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to +imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise +srcImgs[imgToDenoiseIndex] image. +@param dst Output image with the same size and type as srcImgs images. +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Parameter regulating filter strength. Bigger h value +perfectly removes noise but also removes image details, smaller h +value preserves details but also preserves some noise + */ +CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, + int imgToDenoiseIndex, int temporalWindowSize, + float h = 3, int templateWindowSize = 7, int searchWindowSize = 21); + +/** @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been +captured in small period of time. For example video. This version of the function is for grayscale +images or for manual manipulation with colorspaces. For more details see + + +@param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel, +2-channel, 3-channel or 4-channel images sequence. All images should +have the same type and size. +@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence +@param temporalWindowSize Number of surrounding images to use for target image denoising. Should +be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to +imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise +srcImgs[imgToDenoiseIndex] image. +@param dst Output image with the same size and type as srcImgs images. +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Array of parameters regulating filter strength, either one +parameter applied to all channels or one per channel in dst. Big h value +perfectly removes noise but also removes image details, smaller h +value preserves details but also preserves some noise +@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 + */ +CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, + int imgToDenoiseIndex, int temporalWindowSize, + const std::vector& h, + int templateWindowSize = 7, int searchWindowSize = 21, + int normType = NORM_L2); + +/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences + +@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and +size. +@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence +@param temporalWindowSize Number of surrounding images to use for target image denoising. Should +be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to +imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise +srcImgs[imgToDenoiseIndex] image. +@param dst Output image with the same size and type as srcImgs images. +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly +removes noise but also removes image details, smaller h value preserves details but also preserves +some noise. +@param hColor The same as h but for color components. + +The function converts images to CIELAB colorspace and then separately denoise L and AB components +with given h parameters using fastNlMeansDenoisingMulti function. + */ +CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst, + int imgToDenoiseIndex, int temporalWindowSize, + float h = 3, float hColor = 3, + int templateWindowSize = 7, int searchWindowSize = 21); + +/** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, +finding a function to minimize some functional). As the image denoising, in particular, may be seen +as the variational problem, primal-dual algorithm then can be used to perform denoising and this is +exactly what is implemented. + +It should be noted, that this implementation was taken from the July 2013 blog entry +@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python. +Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end +of July 2013 and finally it was slightly adapted by later authors. + +Although the thorough discussion and justification of the algorithm involved may be found in +@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin +with, we consider the 1-byte gray-level images as the functions from the rectangular domain of +pixels (it may be seen as set +\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some +\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with +this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula + +\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f] + +\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our +image to be smooth (ideally, having zero gradient, thus being constant) and the second states that +we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is +exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play. + +@param observations This array should contain one or more noised versions of the image that is to +be restored. +@param result Here the denoised image will be stored. There is no need to do pre-allocation of +storage space, as it will be automatically allocated, if necessary. +@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth +(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly +speaking, as it becomes smaller, the result will be more blur but more sever outliers will be +removed. +@param niters Number of iterations that the algorithm will run. Of course, as more iterations as +better, but it is hard to quantitatively refine this statement, so just use the default and +increase it if the results are poor. + */ +CV_EXPORTS_W void denoise_TVL1(const std::vector& observations,Mat& result, double lambda=1.0, int niters=30); + +//! @} photo_denoise + +//! @addtogroup photo_hdr +//! @{ + +enum { LDR_SIZE = 256 }; + +/** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range. + */ +class CV_EXPORTS_W Tonemap : public Algorithm +{ +public: + /** @brief Tonemaps image + + @param src source image - 32-bit 3-channel Mat + @param dst destination image - 32-bit 3-channel Mat with values in [0, 1] range + */ + CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0; + + CV_WRAP virtual float getGamma() const = 0; + CV_WRAP virtual void setGamma(float gamma) = 0; +}; + +/** @brief Creates simple linear mapper with gamma correction + +@param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma +equal to 2.2f is suitable for most displays. +Generally gamma \> 1 brightens the image and gamma \< 1 darkens it. + */ +CV_EXPORTS_W Ptr createTonemap(float gamma = 1.0f); + +/** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in +logarithmic domain. + +Since it's a global operator the same function is applied to all the pixels, it is controlled by the +bias parameter. + +Optional saturation enhancement is possible as described in @cite FL02 . + +For more information see @cite DM03 . + */ +class CV_EXPORTS_W TonemapDrago : public Tonemap +{ +public: + + CV_WRAP virtual float getSaturation() const = 0; + CV_WRAP virtual void setSaturation(float saturation) = 0; + + CV_WRAP virtual float getBias() const = 0; + CV_WRAP virtual void setBias(float bias) = 0; +}; + +/** @brief Creates TonemapDrago object + +@param gamma gamma value for gamma correction. See createTonemap +@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater +than 1 increase saturation and values less than 1 decrease it. +@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best +results, default value is 0.85. + */ +CV_EXPORTS_W Ptr createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f); + +/** @brief This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter +and compresses contrast of the base layer thus preserving all the details. + +This implementation uses regular bilateral filter from opencv. + +Saturation enhancement is possible as in ocvTonemapDrago. + +For more information see @cite DD02 . + */ +class CV_EXPORTS_W TonemapDurand : public Tonemap +{ +public: + + CV_WRAP virtual float getSaturation() const = 0; + CV_WRAP virtual void setSaturation(float saturation) = 0; + + CV_WRAP virtual float getContrast() const = 0; + CV_WRAP virtual void setContrast(float contrast) = 0; + + CV_WRAP virtual float getSigmaSpace() const = 0; + CV_WRAP virtual void setSigmaSpace(float sigma_space) = 0; + + CV_WRAP virtual float getSigmaColor() const = 0; + CV_WRAP virtual void setSigmaColor(float sigma_color) = 0; +}; + +/** @brief Creates TonemapDurand object + +@param gamma gamma value for gamma correction. See createTonemap +@param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min +are maximum and minimum luminance values of the resulting image. +@param saturation saturation enhancement value. See createTonemapDrago +@param sigma_space bilateral filter sigma in color space +@param sigma_color bilateral filter sigma in coordinate space + */ +CV_EXPORTS_W Ptr +createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float saturation = 1.0f, float sigma_space = 2.0f, float sigma_color = 2.0f); + +/** @brief This is a global tonemapping operator that models human visual system. + +Mapping function is controlled by adaptation parameter, that is computed using light adaptation and +color adaptation. + +For more information see @cite RD05 . + */ +class CV_EXPORTS_W TonemapReinhard : public Tonemap +{ +public: + CV_WRAP virtual float getIntensity() const = 0; + CV_WRAP virtual void setIntensity(float intensity) = 0; + + CV_WRAP virtual float getLightAdaptation() const = 0; + CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0; + + CV_WRAP virtual float getColorAdaptation() const = 0; + CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0; +}; + +/** @brief Creates TonemapReinhard object + +@param gamma gamma value for gamma correction. See createTonemap +@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results. +@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel +value, if 0 it's global, otherwise it's a weighted mean of this two cases. +@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently, +if 0 adaptation level is the same for each channel. + */ +CV_EXPORTS_W Ptr +createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f); + +/** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid, +transforms contrast values to HVS response and scales the response. After this the image is +reconstructed from new contrast values. + +For more information see @cite MM06 . + */ +class CV_EXPORTS_W TonemapMantiuk : public Tonemap +{ +public: + CV_WRAP virtual float getScale() const = 0; + CV_WRAP virtual void setScale(float scale) = 0; + + CV_WRAP virtual float getSaturation() const = 0; + CV_WRAP virtual void setSaturation(float saturation) = 0; +}; + +/** @brief Creates TonemapMantiuk object + +@param gamma gamma value for gamma correction. See createTonemap +@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing +dynamic range. Values from 0.6 to 0.9 produce best results. +@param saturation saturation enhancement value. See createTonemapDrago + */ +CV_EXPORTS_W Ptr +createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f); + +/** @brief The base class for algorithms that align images of the same scene with different exposures + */ +class CV_EXPORTS_W AlignExposures : public Algorithm +{ +public: + /** @brief Aligns images + + @param src vector of input images + @param dst vector of aligned images + @param times vector of exposure time values for each image + @param response 256x1 matrix with inverse camera response function for each pixel value, it should + have the same number of channels as images. + */ + CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst, + InputArray times, InputArray response) = 0; +}; + +/** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median +luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations. + +It is invariant to exposure, so exposure values and camera response are not necessary. + +In this implementation new image regions are filled with zeros. + +For more information see @cite GW03 . + */ +class CV_EXPORTS_W AlignMTB : public AlignExposures +{ +public: + CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst, + InputArray times, InputArray response) = 0; + + /** @brief Short version of process, that doesn't take extra arguments. + + @param src vector of input images + @param dst vector of aligned images + */ + CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst) = 0; + + /** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the + first. + + @param img0 first image + @param img1 second image + */ + CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0; + /** @brief Helper function, that shift Mat filling new regions with zeros. + + @param src input image + @param dst result image + @param shift shift value + */ + CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0; + /** @brief Computes median threshold and exclude bitmaps of given image. + + @param img input image + @param tb median threshold bitmap + @param eb exclude bitmap + */ + CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0; + + CV_WRAP virtual int getMaxBits() const = 0; + CV_WRAP virtual void setMaxBits(int max_bits) = 0; + + CV_WRAP virtual int getExcludeRange() const = 0; + CV_WRAP virtual void setExcludeRange(int exclude_range) = 0; + + CV_WRAP virtual bool getCut() const = 0; + CV_WRAP virtual void setCut(bool value) = 0; +}; + +/** @brief Creates AlignMTB object + +@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are +usually good enough (31 and 63 pixels shift respectively). +@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the +median value. +@param cut if true cuts images, otherwise fills the new regions with zeros. + */ +CV_EXPORTS_W Ptr createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true); + +/** @brief The base class for camera response calibration algorithms. + */ +class CV_EXPORTS_W CalibrateCRF : public Algorithm +{ +public: + /** @brief Recovers inverse camera response. + + @param src vector of input images + @param dst 256x1 matrix with inverse camera response function + @param times vector of exposure time values for each image + */ + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; +}; + +/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective +function as linear system. Objective function is constructed using pixel values on the same position +in all images, extra term is added to make the result smoother. + +For more information see @cite DM97 . + */ +class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF +{ +public: + CV_WRAP virtual float getLambda() const = 0; + CV_WRAP virtual void setLambda(float lambda) = 0; + + CV_WRAP virtual int getSamples() const = 0; + CV_WRAP virtual void setSamples(int samples) = 0; + + CV_WRAP virtual bool getRandom() const = 0; + CV_WRAP virtual void setRandom(bool random) = 0; +}; + +/** @brief Creates CalibrateDebevec object + +@param samples number of pixel locations to use +@param lambda smoothness term weight. Greater values produce smoother results, but can alter the +response. +@param random if true sample pixel locations are chosen at random, otherwise the form a +rectangular grid. + */ +CV_EXPORTS_W Ptr createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false); + +/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective +function as linear system. This algorithm uses all image pixels. + +For more information see @cite RB99 . + */ +class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF +{ +public: + CV_WRAP virtual int getMaxIter() const = 0; + CV_WRAP virtual void setMaxIter(int max_iter) = 0; + + CV_WRAP virtual float getThreshold() const = 0; + CV_WRAP virtual void setThreshold(float threshold) = 0; + + CV_WRAP virtual Mat getRadiance() const = 0; +}; + +/** @brief Creates CalibrateRobertson object + +@param max_iter maximal number of Gauss-Seidel solver iterations. +@param threshold target difference between results of two successive steps of the minimization. + */ +CV_EXPORTS_W Ptr createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f); + +/** @brief The base class algorithms that can merge exposure sequence to a single image. + */ +class CV_EXPORTS_W MergeExposures : public Algorithm +{ +public: + /** @brief Merges images. + + @param src vector of input images + @param dst result image + @param times vector of exposure time values for each image + @param response 256x1 matrix with inverse camera response function for each pixel value, it should + have the same number of channels as images. + */ + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, + InputArray times, InputArray response) = 0; +}; + +/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure +values and camera response. + +For more information see @cite DM97 . + */ +class CV_EXPORTS_W MergeDebevec : public MergeExposures +{ +public: + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, + InputArray times, InputArray response) = 0; + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; +}; + +/** @brief Creates MergeDebevec object + */ +CV_EXPORTS_W Ptr createMergeDebevec(); + +/** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are +combined using laplacian pyramids. + +The resulting image weight is constructed as weighted average of contrast, saturation and +well-exposedness measures. + +The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying +by 255, but it's recommended to apply gamma correction and/or linear tonemapping. + +For more information see @cite MK07 . + */ +class CV_EXPORTS_W MergeMertens : public MergeExposures +{ +public: + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, + InputArray times, InputArray response) = 0; + /** @brief Short version of process, that doesn't take extra arguments. + + @param src vector of input images + @param dst result image + */ + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0; + + CV_WRAP virtual float getContrastWeight() const = 0; + CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0; + + CV_WRAP virtual float getSaturationWeight() const = 0; + CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0; + + CV_WRAP virtual float getExposureWeight() const = 0; + CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0; +}; + +/** @brief Creates MergeMertens object + +@param contrast_weight contrast measure weight. See MergeMertens. +@param saturation_weight saturation measure weight +@param exposure_weight well-exposedness measure weight + */ +CV_EXPORTS_W Ptr +createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f); + +/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure +values and camera response. + +For more information see @cite RB99 . + */ +class CV_EXPORTS_W MergeRobertson : public MergeExposures +{ +public: + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, + InputArray times, InputArray response) = 0; + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; +}; + +/** @brief Creates MergeRobertson object + */ +CV_EXPORTS_W Ptr createMergeRobertson(); + +//! @} photo_hdr + +/** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized +black-and-white photograph rendering, and in many single channel image processing applications +@cite CL12 . + +@param src Input 8-bit 3-channel image. +@param grayscale Output 8-bit 1-channel image. +@param color_boost Output 8-bit 3-channel image. + +This function is to be applied on color images. + */ +CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost); + +//! @addtogroup photo_clone +//! @{ + +/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters, +deformations) or local changes concerned to a selection. Here we are interested in achieving local +changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless +manner. The extent of the changes ranges from slight distortions to complete replacement by novel +content @cite PM03 . + +@param src Input 8-bit 3-channel image. +@param dst Input 8-bit 3-channel image. +@param mask Input 8-bit 1 or 3-channel image. +@param p Point in dst image where object is placed. +@param blend Output image with the same size and type as dst. +@param flags Cloning method that could be one of the following: +- **NORMAL_CLONE** The power of the method is fully expressed when inserting objects with +complex outlines into a new background +- **MIXED_CLONE** The classic method, color-based selection and alpha masking might be time +consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the +original image, is not effective. Mixed seamless cloning based on a loose selection proves +effective. +- **FEATURE_EXCHANGE** Feature exchange allows the user to easily replace certain features of +one object by alternative features. + */ +CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p, + OutputArray blend, int flags); + +/** @brief Given an original color image, two differently colored versions of this image can be mixed +seamlessly. + +@param src Input 8-bit 3-channel image. +@param mask Input 8-bit 1 or 3-channel image. +@param dst Output image with the same size and type as src . +@param red_mul R-channel multiply factor. +@param green_mul G-channel multiply factor. +@param blue_mul B-channel multiply factor. + +Multiplication factor is between .5 to 2.5. + */ +CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f, + float green_mul = 1.0f, float blue_mul = 1.0f); + +/** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and +then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. + +@param src Input 8-bit 3-channel image. +@param mask Input 8-bit 1 or 3-channel image. +@param dst Output image with the same size and type as src. +@param alpha Value ranges between 0-2. +@param beta Value ranges between 0-2. + +This is useful to highlight under-exposed foreground objects or to reduce specular reflections. + */ +CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst, + float alpha = 0.2f, float beta = 0.4f); + +/** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one +washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge +Detector is used. + +@param src Input 8-bit 3-channel image. +@param mask Input 8-bit 1 or 3-channel image. +@param dst Output image with the same size and type as src. +@param low_threshold Range from 0 to 100. +@param high_threshold Value \> 100. +@param kernel_size The size of the Sobel kernel to be used. + +**NOTE:** + +The algorithm assumes that the color of the source image is close to that of the destination. This +assumption means that when the colors don't match, the source image color gets tinted toward the +color of the destination image. + */ +CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst, + float low_threshold = 30, float high_threshold = 45, + int kernel_size = 3); + +//! @} photo_clone + +//! @addtogroup photo_render +//! @{ + +/** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing +filters are used in many different applications @cite EM11 . + +@param src Input 8-bit 3-channel image. +@param dst Output 8-bit 3-channel image. +@param flags Edge preserving filters: +- **RECURS_FILTER** = 1 +- **NORMCONV_FILTER** = 2 +@param sigma_s Range between 0 to 200. +@param sigma_r Range between 0 to 1. + */ +CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1, + float sigma_s = 60, float sigma_r = 0.4f); + +/** @brief This filter enhances the details of a particular image. + +@param src Input 8-bit 3-channel image. +@param dst Output image with the same size and type as src. +@param sigma_s Range between 0 to 200. +@param sigma_r Range between 0 to 1. + */ +CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10, + float sigma_r = 0.15f); + +/** @brief Pencil-like non-photorealistic line drawing + +@param src Input 8-bit 3-channel image. +@param dst1 Output 8-bit 1-channel image. +@param dst2 Output image with the same size and type as src. +@param sigma_s Range between 0 to 200. +@param sigma_r Range between 0 to 1. +@param shade_factor Range between 0 to 0.1. + */ +CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2, + float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f); + +/** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on +photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low +contrast while preserving, or enhancing, high-contrast features. + +@param src Input 8-bit 3-channel image. +@param dst Output image with the same size and type as src. +@param sigma_s Range between 0 to 200. +@param sigma_r Range between 0 to 1. + */ +CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60, + float sigma_r = 0.45f); + +//! @} photo_render + +//! @} photo + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/photo/photo_c.h" +#endif + +#endif diff --git a/thirdparty/linux/include/opencv2/photo/cuda.hpp b/thirdparty/linux/include/opencv2/photo/cuda.hpp new file mode 100644 index 0000000..a2f3816 --- /dev/null +++ b/thirdparty/linux/include/opencv2/photo/cuda.hpp @@ -0,0 +1,132 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_PHOTO_CUDA_HPP +#define OPENCV_PHOTO_CUDA_HPP + +#include "opencv2/core/cuda.hpp" + +namespace cv { namespace cuda { + +//! @addtogroup photo_denoise +//! @{ + +/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast. + +@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3. +@param dst Destination image. +@param h Filter sigma regulating filter strength for color. +@param search_window Size of search window. +@param block_size Size of block used for computing weights. +@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 , +BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now. +@param stream Stream for the asynchronous version. + +@sa + fastNlMeansDenoising + */ +CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst, + float h, + int search_window = 21, + int block_size = 7, + int borderMode = BORDER_DEFAULT, + Stream& stream = Stream::Null()); + +/** @brief Perform image denoising using Non-local Means Denoising algorithm + with several computational +optimizations. Noise expected to be a gaussian white noise + +@param src Input 8-bit 1-channel, 2-channel or 3-channel image. +@param dst Output image with the same size and type as src . +@param h Parameter regulating filter strength. Big h value perfectly removes noise but also +removes image details, smaller h value preserves details but also preserves some noise +@param search_window Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater search_window - greater +denoising time. Recommended value 21 pixels +@param block_size Size in pixels of the template patch that is used to compute weights. Should be +odd. Recommended value 7 pixels +@param stream Stream for the asynchronous invocations. + +This function expected to be applied to grayscale images. For colored images look at +FastNonLocalMeansDenoising::labMethod. + +@sa + fastNlMeansDenoising + */ +CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst, + float h, + int search_window = 21, + int block_size = 7, + Stream& stream = Stream::Null()); + +/** @brief Modification of fastNlMeansDenoising function for colored images + +@param src Input 8-bit 3-channel image. +@param dst Output image with the same size and type as src . +@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but +also removes image details, smaller h value preserves details but also preserves some noise +@param photo_render float The same as h but for color components. For most images value equals 10 will be +enough to remove colored noise and do not distort colors +@param search_window Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater search_window - greater +denoising time. Recommended value 21 pixels +@param block_size Size in pixels of the template patch that is used to compute weights. Should be +odd. Recommended value 7 pixels +@param stream Stream for the asynchronous invocations. + +The function converts image to CIELAB colorspace and then separately denoise L and AB components +with given h parameters using FastNonLocalMeansDenoising::simpleMethod function. + +@sa + fastNlMeansDenoisingColored + */ +CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst, + float h_luminance, float photo_render, + int search_window = 21, + int block_size = 7, + Stream& stream = Stream::Null()); + +//! @} photo + +}} // namespace cv { namespace cuda { + +#endif /* OPENCV_PHOTO_CUDA_HPP */ diff --git a/thirdparty/linux/include/opencv2/photo/photo.hpp b/thirdparty/linux/include/opencv2/photo/photo.hpp new file mode 100644 index 0000000..8af5e9f --- /dev/null +++ b/thirdparty/linux/include/opencv2/photo/photo.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/photo.hpp" diff --git a/thirdparty/linux/include/opencv2/photo/photo_c.h b/thirdparty/linux/include/opencv2/photo/photo_c.h new file mode 100644 index 0000000..cd623c1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/photo/photo_c.h @@ -0,0 +1,74 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_PHOTO_C_H +#define OPENCV_PHOTO_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup photo_c + @{ + */ + +/* Inpainting algorithms */ +enum InpaintingModes +{ + CV_INPAINT_NS =0, + CV_INPAINT_TELEA =1 +}; + + +/* Inpaints the selected region in the image */ +CVAPI(void) cvInpaint( const CvArr* src, const CvArr* inpaint_mask, + CvArr* dst, double inpaintRange, int flags ); + +/** @} */ + +#ifdef __cplusplus +} //extern "C" +#endif + +#endif //OPENCV_PHOTO_C_H diff --git a/thirdparty/linux/include/opencv2/plot.hpp b/thirdparty/linux/include/opencv2/plot.hpp new file mode 100644 index 0000000..6673bd1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/plot.hpp @@ -0,0 +1,109 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ +//################################################################################ +// +// Created by Nuno Moutinho +// +//################################################################################ + +#ifndef _OPENCV_PLOT_H_ +#define _OPENCV_PLOT_H_ +#ifdef __cplusplus + +#include + +/** +@defgroup plot Plot function for Mat data +*/ + +namespace cv +{ + namespace plot + { + //! @addtogroup plot + //! @{ + + class CV_EXPORTS_W Plot2d : public Algorithm + { + public: + + CV_WRAP virtual void setMinX(double _plotMinX) = 0; + CV_WRAP virtual void setMinY(double _plotMinY) = 0; + CV_WRAP virtual void setMaxX(double _plotMaxX) = 0; + CV_WRAP virtual void setMaxY(double _plotMaxY) = 0; + CV_WRAP virtual void setPlotLineWidth(int _plotLineWidth) = 0; + /** + * @brief Switches data visualization mode + * + * @param _needPlotLine if true then neighbour plot points will be connected by lines. + * In other case data will be plotted as a set of standalone points. + */ + CV_WRAP virtual void setNeedPlotLine(bool _needPlotLine) = 0; + CV_WRAP virtual void setPlotLineColor(Scalar _plotLineColor) = 0; + CV_WRAP virtual void setPlotBackgroundColor(Scalar _plotBackgroundColor) = 0; + CV_WRAP virtual void setPlotAxisColor(Scalar _plotAxisColor) = 0; + CV_WRAP virtual void setPlotGridColor(Scalar _plotGridColor) = 0; + CV_WRAP virtual void setPlotTextColor(Scalar _plotTextColor) = 0; + CV_WRAP virtual void setPlotSize(int _plotSizeWidth, int _plotSizeHeight) = 0; + CV_WRAP virtual void render(OutputArray _plotResult) = 0; + }; + + /** + * @brief Creates Plot2d object + * + * @param data \f$1xN\f$ or \f$Nx1\f$ matrix containing \f$Y\f$ values of points to plot. \f$X\f$ values + * will be equal to indexes of correspondind elements in data matrix. + */ + CV_EXPORTS_W Ptr createPlot2d(InputArray data); + /** + * @brief Creates Plot2d object + * + * @param dataX \f$1xN\f$ or \f$Nx1\f$ matrix \f$X\f$ values of points to plot. + * @param dataY \f$1xN\f$ or \f$Nx1\f$ matrix containing \f$Y\f$ values of points to plot. + */ + CV_EXPORTS_W Ptr createPlot2d(InputArray dataX, InputArray dataY); + //! @} + } +} + +#endif +#endif diff --git a/thirdparty/linux/include/opencv2/reg/map.hpp b/thirdparty/linux/include/opencv2/reg/map.hpp new file mode 100644 index 0000000..26b29e3 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/map.hpp @@ -0,0 +1,175 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAP_H_ +#define MAP_H_ + +#include // Basic OpenCV structures (cv::Mat, Scalar) + +/** @defgroup reg Image Registration + +The Registration module implements parametric image registration. The implemented method is direct +alignment, that is, it uses directly the pixel values for calculating the registration between a +pair of images, as opposed to feature-based registration. The implementation follows essentially the +corresponding part of @cite Szeliski06 . + +Feature based methods have some advantages over pixel based methods when we are trying to register +pictures that have been shoot under different lighting conditions or exposition times, or when the +images overlap only partially. On the other hand, the main advantage of pixel-based methods when +compared to feature based methods is their better precision for some pictures (those shoot under +similar lighting conditions and that have a significative overlap), due to the fact that we are +using all the information available in the image, which allows us to achieve subpixel accuracy. This +is particularly important for certain applications like multi-frame denoising or super-resolution. + +In fact, pixel and feature registration methods can complement each other: an application could +first obtain a coarse registration using features and then refine the registration using a pixel +based method on the overlapping area of the images. The code developed allows this use case. + +The module implements classes derived from the abstract classes cv::reg::Map or cv::reg::Mapper. The +former models a coordinate transformation between two reference frames, while the later encapsulates +a way of invoking a method that calculates a Map between two images. Although the objective has been +to implement pixel based methods, the module can be extended to support other methods that can +calculate transformations between images (feature methods, optical flow, etc.). + +Each class derived from Map implements a motion model, as follows: + +- MapShift: Models a simple translation +- MapAffine: Models an affine transformation +- MapProjec: Models a projective transformation + +MapProject can also be used to model affine motion or translations, but some operations on it are +more costly, and that is the reason for defining the other two classes. + +The classes derived from Mapper are + +- MapperGradShift: Gradient based alignment for calculating translations. It produces a MapShift + (two parameters that correspond to the shift vector). +- MapperGradEuclid: Gradient based alignment for euclidean motions, that is, rotations and + translations. It calculates three parameters (angle and shift vector), although the result is + stored in a MapAffine object for convenience. +- MapperGradSimilar: Gradient based alignment for calculating similarities, which adds scaling to + the euclidean motion. It calculates four parameters (two for the anti-symmetric matrix and two + for the shift vector), although the result is stored in a MapAffine object for better + convenience. +- MapperGradAffine: Gradient based alignment for an affine motion model. The number of parameters + is six and the result is stored in a MapAffine object. +- MapperGradProj: Gradient based alignment for calculating projective transformations. The number + of parameters is eight and the result is stored in a MapProject object. +- MapperPyramid: It implements hyerarchical motion estimation using a Gaussian pyramid. Its + constructor accepts as argument any other object that implements the Mapper interface, and it is + that mapper the one called by MapperPyramid for each scale of the pyramid. + +If the motion between the images is not very small, the normal way of using these classes is to +create a MapperGrad\* object and use it as input to create a MapperPyramid, which in turn is called +to perform the calculation. However, if the motion between the images is small enough, we can use +directly the MapperGrad\* classes. Another possibility is to use first a feature based method to +perform a coarse registration and then do a refinement through MapperPyramid or directly a +MapperGrad\* object. The "calculate" method of the mappers accepts an initial estimation of the +motion as input. + +When deciding which MapperGrad to use we must take into account that mappers with more parameters +can handle more complex motions, but involve more calculations and are therefore slower. Also, if we +are confident on the motion model that is followed by the sequence, increasing the number of +parameters beyond what we need will decrease the accuracy: it is better to use the least number of +degrees of freedom that we can. + +In the module tests there are examples that show how to register a pair of images using any of the +implemented mappers. +*/ + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/** @brief Base class for modelling a Map between two images. + +The class is only used to define the common interface for any possible map. + */ +class CV_EXPORTS Map +{ +public: + /*! + * Virtual destructor + */ + virtual ~Map(void); + + /*! + * Warps image to a new coordinate frame. The calculation is img2(x)=img1(T^{-1}(x)), as we + * have to apply the inverse transformation to the points to move them to were the values + * of img2 are. + * \param[in] img1 Original image + * \param[out] img2 Warped image + */ + virtual void warp(const cv::Mat& img1, cv::Mat& img2) const; + + /*! + * Warps image to a new coordinate frame. The calculation is img2(x)=img1(T(x)), so in fact + * this is the inverse warping as we are taking the value of img1 with the forward + * transformation of the points. + * \param[in] img1 Original image + * \param[out] img2 Warped image + */ + virtual void inverseWarp(const cv::Mat& img1, cv::Mat& img2) const = 0; + + /*! + * Calculates the inverse map + * \return Inverse map + */ + virtual cv::Ptr inverseMap(void) const = 0; + + /*! + * Changes the map composing the current transformation with the one provided in the call. + * The order is first the current transformation, then the input argument. + * \param[in] map Transformation to compose with. + */ + virtual void compose(const Map& map) = 0; + + /*! + * Scales the map by a given factor as if the coordinates system is expanded/compressed + * by that factor. + * \param[in] factor Expansion if bigger than one, compression if smaller than one + */ + virtual void scale(double factor) = 0; +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAP_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mapaffine.hpp b/thirdparty/linux/include/opencv2/reg/mapaffine.hpp new file mode 100644 index 0000000..1c91326 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mapaffine.hpp @@ -0,0 +1,105 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPAFFINE_H_ +#define MAPAFFINE_H_ + +#include "map.hpp" + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Defines an affine transformation + */ +class CV_EXPORTS MapAffine : public Map +{ +public: + /*! + * Default constructor builds an identity map + */ + MapAffine(void); + + /*! + * Constructor providing explicit values + * \param[in] linTr Linear part of the affine transformation + * \param[in] shift Displacement part of the affine transformation + */ + MapAffine(const cv::Matx& linTr, const cv::Vec& shift); + + /*! + * Destructor + */ + ~MapAffine(void); + + void inverseWarp(const cv::Mat& img1, cv::Mat& img2) const; + + cv::Ptr inverseMap(void) const; + + void compose(const Map& map); + + void scale(double factor); + + /*! + * Return linear part of the affine transformation + * \return Linear part of the affine transformation + */ + const cv::Matx& getLinTr() const { + return linTr_; + } + + /*! + * Return displacement part of the affine transformation + * \return Displacement part of the affine transformation + */ + const cv::Vec& getShift() const { + return shift_; + } + +private: + cv::Matx linTr_; + cv::Vec shift_; +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPAFFINE_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mapper.hpp b/thirdparty/linux/include/opencv2/reg/mapper.hpp new file mode 100644 index 0000000..8abadd1 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mapper.hpp @@ -0,0 +1,113 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPPER_H_ +#define MAPPER_H_ + +#include // Basic OpenCV structures (cv::Mat, Scalar) +#include "map.hpp" + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/** @brief Base class for modelling an algorithm for calculating a + +The class is only used to define the common interface for any possible mapping algorithm. + */ +class CV_EXPORTS Mapper +{ +public: + virtual ~Mapper(void) {} + + /* + * Calculate mapping between two images + * \param[in] img1 Reference image + * \param[in] img2 Warped image + * \param[in,out] res Map from img1 to img2, stored in a smart pointer. If present as input, + * it is an initial rough estimation that the mapper will try to refine. + */ + virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const = 0; + + /* + * Returns a map compatible with the Mapper class + * \return Pointer to identity Map + */ + virtual cv::Ptr getMap(void) const = 0; + +protected: + /* + * Calculates gradient and difference between images + * \param[in] img1 Image one + * \param[in] img2 Image two + * \param[out] Ix Gradient x-coordinate + * \param[out] Iy Gradient y-coordinate + * \param[out] It Difference of images + */ + void gradient(const cv::Mat& img1, const cv::Mat& img2, + cv::Mat& Ix, cv::Mat& Iy, cv::Mat& It) const; + + /* + * Fills matrices with pixel coordinates of an image + * \param[in] img Image + * \param[out] grid_r Row (y-coordinate) + * \param[out] grid_c Column (x-coordinate) + */ + void grid(const Mat& img, Mat& grid_r, Mat& grid_c) const; + + /* + * Per-element square of a matrix + * \param[in] mat1 Input matrix + * \return mat1[i,j]^2 + */ + cv::Mat sqr(const cv::Mat& mat1) const + { + cv::Mat res; + res.create(mat1.size(), mat1.type()); + res = mat1.mul(mat1); + return res; + } +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPPER_H_ + diff --git a/thirdparty/linux/include/opencv2/reg/mappergradaffine.hpp b/thirdparty/linux/include/opencv2/reg/mappergradaffine.hpp new file mode 100644 index 0000000..08d5397 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mappergradaffine.hpp @@ -0,0 +1,67 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPPERGRADAFFINE_H_ +#define MAPPERGRADAFFINE_H_ + +#include "mapper.hpp" + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Mapper for affine motion + */ +class CV_EXPORTS MapperGradAffine: public Mapper +{ +public: + MapperGradAffine(void); + ~MapperGradAffine(void); + + virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; + + cv::Ptr getMap(void) const; +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPPERGRADAFFINE_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mappergradeuclid.hpp b/thirdparty/linux/include/opencv2/reg/mappergradeuclid.hpp new file mode 100644 index 0000000..29c49cb --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mappergradeuclid.hpp @@ -0,0 +1,67 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPPERGRADEUCLID_H_ +#define MAPPERGRADEUCLID_H_ + +#include "mapper.hpp" + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Mapper for euclidean motion: rotation plus shift + */ +class CV_EXPORTS MapperGradEuclid: public Mapper +{ +public: + MapperGradEuclid(void); + ~MapperGradEuclid(void); + + virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; + + cv::Ptr getMap(void) const; +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPPERGRADEUCLID_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mappergradproj.hpp b/thirdparty/linux/include/opencv2/reg/mappergradproj.hpp new file mode 100644 index 0000000..f1721e8 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mappergradproj.hpp @@ -0,0 +1,67 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPPERGRADPROJ_H_ +#define MAPPERGRADPROJ_H_ + +#include "mapper.hpp" + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Gradient mapper for a projective transformation + */ +class CV_EXPORTS MapperGradProj: public Mapper +{ +public: + MapperGradProj(void); + ~MapperGradProj(void); + + virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; + + cv::Ptr getMap(void) const; +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPPERGRADPROJ_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mappergradshift.hpp b/thirdparty/linux/include/opencv2/reg/mappergradshift.hpp new file mode 100644 index 0000000..a9f75b3 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mappergradshift.hpp @@ -0,0 +1,67 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPPERGRADSHIFT_H_ +#define MAPPERGRADSHIFT_H_ + +#include "mapper.hpp" + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Gradient mapper for a translation + */ +class CV_EXPORTS MapperGradShift: public Mapper +{ +public: + MapperGradShift(void); + virtual ~MapperGradShift(void); + + virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; + + cv::Ptr getMap(void) const; +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPPERGRADSHIFT_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mappergradsimilar.hpp b/thirdparty/linux/include/opencv2/reg/mappergradsimilar.hpp new file mode 100644 index 0000000..ea45ab9 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mappergradsimilar.hpp @@ -0,0 +1,67 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPPERGRADSIMILAR_H_ +#define MAPPERGRADSIMILAR_H_ + +#include "mapper.hpp" + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Calculates a similarity transformation between to images (scale, rotation, and shift) + */ +class CV_EXPORTS MapperGradSimilar: public Mapper +{ +public: + MapperGradSimilar(void); + ~MapperGradSimilar(void); + + virtual void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; + + cv::Ptr getMap(void) const; +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPPERGRADSIMILAR_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mapperpyramid.hpp b/thirdparty/linux/include/opencv2/reg/mapperpyramid.hpp new file mode 100644 index 0000000..33440bd --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mapperpyramid.hpp @@ -0,0 +1,78 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPPERPYRAMID_H_ +#define MAPPERPYRAMID_H_ + +#include "mapper.hpp" + + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Calculates a map using a gaussian pyramid + */ +class CV_EXPORTS MapperPyramid: public Mapper +{ +public: + /* + * Constructor + * \param[in] baseMapper Base mapper used for the refinements + */ + MapperPyramid(const Mapper& baseMapper); + + void calculate(const cv::Mat& img1, const cv::Mat& img2, cv::Ptr& res) const; + + cv::Ptr getMap(void) const; + + unsigned numLev_; /*!< Number of levels of the pyramid */ + unsigned numIterPerScale_; /*!< Number of iterations at a given scale of the pyramid */ + +private: + MapperPyramid& operator=(const MapperPyramid&); + const Mapper& baseMapper_; /*!< Mapper used in inner level */ +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPPERPYRAMID_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mapprojec.hpp b/thirdparty/linux/include/opencv2/reg/mapprojec.hpp new file mode 100644 index 0000000..57ef146 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mapprojec.hpp @@ -0,0 +1,105 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPPROJEC_H_ +#define MAPPROJEC_H_ + +#include "map.hpp" + + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Defines an transformation that consists on a projective transformation + */ +class CV_EXPORTS MapProjec : public Map +{ +public: + /*! + * Default constructor builds an identity map + */ + MapProjec(void); + + /*! + * Constructor providing explicit values + * \param[in] projTr Projective transformation + */ + MapProjec(const cv::Matx& projTr); + + /*! + * Destructor + */ + ~MapProjec(void); + + void inverseWarp(const cv::Mat& img1, cv::Mat& img2) const; + + cv::Ptr inverseMap(void) const; + + void compose(const Map& map); + + void scale(double factor); + + /*! + * Returns projection matrix + * \return Projection matrix + */ + const cv::Matx& getProjTr() const { + return projTr_; + } + + /*! + * Normalizes object's homography + */ + void normalize(void) { + double z = 1./projTr_(2, 2); + for(size_t v_i = 0; v_i < sizeof(projTr_.val)/sizeof(projTr_.val[0]); ++v_i) + projTr_.val[v_i] *= z; + } + +private: + cv::Matx projTr_; /*< Projection matrix */ +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPPROJEC_H_ diff --git a/thirdparty/linux/include/opencv2/reg/mapshift.hpp b/thirdparty/linux/include/opencv2/reg/mapshift.hpp new file mode 100644 index 0000000..e5f54a4 --- /dev/null +++ b/thirdparty/linux/include/opencv2/reg/mapshift.hpp @@ -0,0 +1,96 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// Copyright (C) 2013, Alfonso Sanchez-Beato, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef MAPSHIFT_H_ +#define MAPSHIFT_H_ + +#include "map.hpp" + + +namespace cv { +namespace reg { + +//! @addtogroup reg +//! @{ + +/*! + * Defines an transformation that consists on a simple displacement + */ +class CV_EXPORTS MapShift : public Map +{ +public: + /*! + * Default constructor builds an identity map + */ + MapShift(void); + + /*! + * Constructor providing explicit values + * \param[in] shift Displacement + */ + MapShift(const cv::Vec& shift); + + /*! + * Destructor + */ + ~MapShift(void); + + void inverseWarp(const cv::Mat& img1, cv::Mat& img2) const; + + cv::Ptr inverseMap(void) const; + + void compose(const Map& map); + + void scale(double factor); + + /*! + * Return displacement + * \return Displacement + */ + const cv::Vec& getShift() const { + return shift_; + } + +private: + cv::Vec shift_; /*< Displacement */ +}; + +//! @} + +}} // namespace cv::reg + +#endif // MAPSHIFT_H_ diff --git a/thirdparty/linux/include/opencv2/rgbd.hpp b/thirdparty/linux/include/opencv2/rgbd.hpp new file mode 100644 index 0000000..b25bd3d --- /dev/null +++ b/thirdparty/linux/include/opencv2/rgbd.hpp @@ -0,0 +1,1049 @@ +/* + * Software License Agreement (BSD License) + * + * Copyright (c) 2009, Willow Garage, Inc. + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of Willow Garage, Inc. nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + * + */ + +#ifndef __OPENCV_RGBD_HPP__ +#define __OPENCV_RGBD_HPP__ + +#ifdef __cplusplus + +#include +#include + +/** @defgroup rgbd RGB-Depth Processing +*/ + +namespace cv +{ +namespace rgbd +{ + +//! @addtogroup rgbd +//! @{ + + /** Checks if the value is a valid depth. For CV_16U or CV_16S, the convention is to be invalid if it is + * a limit. For a float/double, we just check if it is a NaN + * @param depth the depth to check for validity + */ + CV_EXPORTS + inline bool + isValidDepth(const float & depth) + { + return !cvIsNaN(depth); + } + CV_EXPORTS + inline bool + isValidDepth(const double & depth) + { + return !cvIsNaN(depth); + } + CV_EXPORTS + inline bool + isValidDepth(const short int & depth) + { + return (depth != std::numeric_limits::min()) && (depth != std::numeric_limits::max()); + } + CV_EXPORTS + inline bool + isValidDepth(const unsigned short int & depth) + { + return (depth != std::numeric_limits::min()) + && (depth != std::numeric_limits::max()); + } + CV_EXPORTS + inline bool + isValidDepth(const int & depth) + { + return (depth != std::numeric_limits::min()) && (depth != std::numeric_limits::max()); + } + CV_EXPORTS + inline bool + isValidDepth(const unsigned int & depth) + { + return (depth != std::numeric_limits::min()) && (depth != std::numeric_limits::max()); + } + + /** Object that can compute the normals in an image. + * It is an object as it can cache data for speed efficiency + * The implemented methods are either: + * - FALS (the fastest) and SRI from + * ``Fast and Accurate Computation of Surface Normals from Range Images`` + * by H. Badino, D. Huber, Y. Park and T. Kanade + * - the normals with bilateral filtering on a depth image from + * ``Gradient Response Maps for Real-Time Detection of Texture-Less Objects`` + * by S. Hinterstoisser, C. Cagniart, S. Ilic, P. Sturm, N. Navab, P. Fua, and V. Lepetit + */ + class CV_EXPORTS RgbdNormals: public Algorithm + { + public: + enum RGBD_NORMALS_METHOD + { + RGBD_NORMALS_METHOD_FALS, RGBD_NORMALS_METHOD_LINEMOD, RGBD_NORMALS_METHOD_SRI + }; + + RgbdNormals() + : + rows_(0), + cols_(0), + depth_(0), + K_(Mat()), + window_size_(0), + method_(RGBD_NORMALS_METHOD_FALS), + rgbd_normals_impl_(0) + { + } + + /** Constructor + * @param rows the number of rows of the depth image normals will be computed on + * @param cols the number of cols of the depth image normals will be computed on + * @param depth the depth of the normals (only CV_32F or CV_64F) + * @param K the calibration matrix to use + * @param window_size the window size to compute the normals: can only be 1,3,5 or 7 + * @param method one of the methods to use: RGBD_NORMALS_METHOD_SRI, RGBD_NORMALS_METHOD_FALS + */ + RgbdNormals(int rows, int cols, int depth, InputArray K, int window_size = 5, int method = + RGBD_NORMALS_METHOD_FALS); + + ~RgbdNormals(); + + /** Given a set of 3d points in a depth image, compute the normals at each point. + * @param points a rows x cols x 3 matrix of CV_32F/CV64F or a rows x cols x 1 CV_U16S + * @param normals a rows x cols x 3 matrix + */ + void + operator()(InputArray points, OutputArray normals) const; + + /** Initializes some data that is cached for later computation + * If that function is not called, it will be called the first time normals are computed + */ + void + initialize() const; + + int getRows() const + { + return rows_; + } + void setRows(int val) + { + rows_ = val; + } + int getCols() const + { + return cols_; + } + void setCols(int val) + { + cols_ = val; + } + int getWindowSize() const + { + return window_size_; + } + void setWindowSize(int val) + { + window_size_ = val; + } + int getDepth() const + { + return depth_; + } + void setDepth(int val) + { + depth_ = val; + } + cv::Mat getK() const + { + return K_; + } + void setK(const cv::Mat &val) + { + K_ = val; + } + int getMethod() const + { + return method_; + } + void setMethod(int val) + { + method_ = val; + } + + protected: + void + initialize_normals_impl(int rows, int cols, int depth, const Mat & K, int window_size, int method) const; + + int rows_, cols_, depth_; + Mat K_; + int window_size_; + int method_; + mutable void* rgbd_normals_impl_; + }; + + /** Object that can clean a noisy depth image + */ + class CV_EXPORTS DepthCleaner: public Algorithm + { + public: + /** NIL method is from + * ``Modeling Kinect Sensor Noise for Improved 3d Reconstruction and Tracking`` + * by C. Nguyen, S. Izadi, D. Lovel + */ + enum DEPTH_CLEANER_METHOD + { + DEPTH_CLEANER_NIL + }; + + DepthCleaner() + : + depth_(0), + window_size_(0), + method_(DEPTH_CLEANER_NIL), + depth_cleaner_impl_(0) + { + } + + /** Constructor + * @param depth the depth of the normals (only CV_32F or CV_64F) + * @param window_size the window size to compute the normals: can only be 1,3,5 or 7 + * @param method one of the methods to use: RGBD_NORMALS_METHOD_SRI, RGBD_NORMALS_METHOD_FALS + */ + DepthCleaner(int depth, int window_size = 5, int method = DEPTH_CLEANER_NIL); + + ~DepthCleaner(); + + /** Given a set of 3d points in a depth image, compute the normals at each point. + * @param points a rows x cols x 3 matrix of CV_32F/CV64F or a rows x cols x 1 CV_U16S + * @param depth a rows x cols matrix of the cleaned up depth + */ + void + operator()(InputArray points, OutputArray depth) const; + + /** Initializes some data that is cached for later computation + * If that function is not called, it will be called the first time normals are computed + */ + void + initialize() const; + + int getWindowSize() const + { + return window_size_; + } + void setWindowSize(int val) + { + window_size_ = val; + } + int getDepth() const + { + return depth_; + } + void setDepth(int val) + { + depth_ = val; + } + int getMethod() const + { + return method_; + } + void setMethod(int val) + { + method_ = val; + } + + protected: + void + initialize_cleaner_impl() const; + + int depth_; + int window_size_; + int method_; + mutable void* depth_cleaner_impl_; + }; + + + /** Registers depth data to an external camera + * Registration is performed by creating a depth cloud, transforming the cloud by + * the rigid body transformation between the cameras, and then projecting the + * transformed points into the RGB camera. + * + * uv_rgb = K_rgb * [R | t] * z * inv(K_ir) * uv_ir + * + * Currently does not check for negative depth values. + * + * @param unregisteredCameraMatrix the camera matrix of the depth camera + * @param registeredCameraMatrix the camera matrix of the external camera + * @param registeredDistCoeffs the distortion coefficients of the external camera + * @param Rt the rigid body transform between the cameras. Transforms points from depth camera frame to external camera frame. + * @param unregisteredDepth the input depth data + * @param outputImagePlaneSize the image plane dimensions of the external camera (width, height) + * @param registeredDepth the result of transforming the depth into the external camera + * @param depthDilation whether or not the depth is dilated to avoid holes and occlusion errors (optional) + */ + CV_EXPORTS + void + registerDepth(InputArray unregisteredCameraMatrix, InputArray registeredCameraMatrix, InputArray registeredDistCoeffs, + InputArray Rt, InputArray unregisteredDepth, const Size& outputImagePlaneSize, + OutputArray registeredDepth, bool depthDilation=false); + + /** + * @param depth the depth image + * @param in_K + * @param in_points the list of xy coordinates + * @param points3d the resulting 3d points + */ + CV_EXPORTS + void + depthTo3dSparse(InputArray depth, InputArray in_K, InputArray in_points, OutputArray points3d); + + /** Converts a depth image to an organized set of 3d points. + * The coordinate system is x pointing left, y down and z away from the camera + * @param depth the depth image (if given as short int CV_U, it is assumed to be the depth in millimeters + * (as done with the Microsoft Kinect), otherwise, if given as CV_32F or CV_64F, it is assumed in meters) + * @param K The calibration matrix + * @param points3d the resulting 3d points. They are of depth the same as `depth` if it is CV_32F or CV_64F, and the + * depth of `K` if `depth` is of depth CV_U + * @param mask the mask of the points to consider (can be empty) + */ + CV_EXPORTS + void + depthTo3d(InputArray depth, InputArray K, OutputArray points3d, InputArray mask = noArray()); + + /** If the input image is of type CV_16UC1 (like the Kinect one), the image is converted to floats, divided + * by 1000 to get a depth in meters, and the values 0 are converted to std::numeric_limits::quiet_NaN() + * Otherwise, the image is simply converted to floats + * @param in the depth image (if given as short int CV_U, it is assumed to be the depth in millimeters + * (as done with the Microsoft Kinect), it is assumed in meters) + * @param depth the desired output depth (floats or double) + * @param out The rescaled float depth image + */ + CV_EXPORTS + void + rescaleDepth(InputArray in, int depth, OutputArray out); + + /** Object that can compute planes in an image + */ + class CV_EXPORTS RgbdPlane: public Algorithm + { + public: + enum RGBD_PLANE_METHOD + { + RGBD_PLANE_METHOD_DEFAULT + }; + + RgbdPlane(RGBD_PLANE_METHOD method = RGBD_PLANE_METHOD_DEFAULT) + : + method_(method), + block_size_(40), + min_size_(block_size_*block_size_), + threshold_(0.01), + sensor_error_a_(0), + sensor_error_b_(0), + sensor_error_c_(0) + { + } + + /** Find The planes in a depth image + * @param points3d the 3d points organized like the depth image: rows x cols with 3 channels + * @param normals the normals for every point in the depth image + * @param mask An image where each pixel is labeled with the plane it belongs to + * and 255 if it does not belong to any plane + * @param plane_coefficients the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0, norm(a,b,c)=1 + * and c < 0 (so that the normal points towards the camera) + */ + void + operator()(InputArray points3d, InputArray normals, OutputArray mask, + OutputArray plane_coefficients); + + /** Find The planes in a depth image but without doing a normal check, which is faster but less accurate + * @param points3d the 3d points organized like the depth image: rows x cols with 3 channels + * @param mask An image where each pixel is labeled with the plane it belongs to + * and 255 if it does not belong to any plane + * @param plane_coefficients the coefficients of the corresponding planes (a,b,c,d) such that ax+by+cz+d=0 + */ + void + operator()(InputArray points3d, OutputArray mask, OutputArray plane_coefficients); + + int getBlockSize() const + { + return block_size_; + } + void setBlockSize(int val) + { + block_size_ = val; + } + int getMinSize() const + { + return min_size_; + } + void setMinSize(int val) + { + min_size_ = val; + } + int getMethod() const + { + return method_; + } + void setMethod(int val) + { + method_ = val; + } + double getThreshold() const + { + return threshold_; + } + void setThreshold(double val) + { + threshold_ = val; + } + double getSensorErrorA() const + { + return sensor_error_a_; + } + void setSensorErrorA(double val) + { + sensor_error_a_ = val; + } + double getSensorErrorB() const + { + return sensor_error_b_; + } + void setSensorErrorB(double val) + { + sensor_error_b_ = val; + } + double getSensorErrorC() const + { + return sensor_error_c_; + } + void setSensorErrorC(double val) + { + sensor_error_c_ = val; + } + + private: + /** The method to use to compute the planes */ + int method_; + /** The size of the blocks to look at for a stable MSE */ + int block_size_; + /** The minimum size of a cluster to be considered a plane */ + int min_size_; + /** How far a point can be from a plane to belong to it (in meters) */ + double threshold_; + /** coefficient of the sensor error with respect to the. All 0 by default but you want a=0.0075 for a Kinect */ + double sensor_error_a_, sensor_error_b_, sensor_error_c_; + }; + + /** Object that contains a frame data. + */ + struct CV_EXPORTS RgbdFrame + { + RgbdFrame(); + RgbdFrame(const Mat& image, const Mat& depth, const Mat& mask=Mat(), const Mat& normals=Mat(), int ID=-1); + virtual ~RgbdFrame(); + + virtual void + release(); + + int ID; + Mat image; + Mat depth; + Mat mask; + Mat normals; + }; + + /** Object that contains a frame data that is possibly needed for the Odometry. + * It's used for the efficiency (to pass precomputed/cached data of the frame that participates + * in the Odometry processing several times). + */ + struct CV_EXPORTS OdometryFrame : public RgbdFrame + { + /** These constants are used to set a type of cache which has to be prepared depending on the frame role: + * srcFrame or dstFrame (see compute method of the Odometry class). For the srcFrame and dstFrame different cache data may be required, + * some part of a cache may be common for both frame roles. + * @param CACHE_SRC The cache data for the srcFrame will be prepared. + * @param CACHE_DST The cache data for the dstFrame will be prepared. + * @param CACHE_ALL The cache data for both srcFrame and dstFrame roles will be computed. + */ + enum + { + CACHE_SRC = 1, CACHE_DST = 2, CACHE_ALL = CACHE_SRC + CACHE_DST + }; + + OdometryFrame(); + OdometryFrame(const Mat& image, const Mat& depth, const Mat& mask=Mat(), const Mat& normals=Mat(), int ID=-1); + + virtual void + release(); + + void + releasePyramids(); + + std::vector pyramidImage; + std::vector pyramidDepth; + std::vector pyramidMask; + + std::vector pyramidCloud; + + std::vector pyramid_dI_dx; + std::vector pyramid_dI_dy; + std::vector pyramidTexturedMask; + + std::vector pyramidNormals; + std::vector pyramidNormalsMask; + }; + + /** Base class for computation of odometry. + */ + class CV_EXPORTS Odometry: public Algorithm + { + public: + + /** A class of transformation*/ + enum + { + ROTATION = 1, TRANSLATION = 2, RIGID_BODY_MOTION = 4 + }; + + static inline float + DEFAULT_MIN_DEPTH() + { + return 0.f; // in meters + } + static inline float + DEFAULT_MAX_DEPTH() + { + return 4.f; // in meters + } + static inline float + DEFAULT_MAX_DEPTH_DIFF() + { + return 0.07f; // in meters + } + static inline float + DEFAULT_MAX_POINTS_PART() + { + return 0.07f; // in [0, 1] + } + static inline float + DEFAULT_MAX_TRANSLATION() + { + return 0.15f; // in meters + } + static inline float + DEFAULT_MAX_ROTATION() + { + return 15; // in degrees + } + + /** Method to compute a transformation from the source frame to the destination one. + * Some odometry algorithms do not used some data of frames (eg. ICP does not use images). + * In such case corresponding arguments can be set as empty Mat. + * The method returns true if all internal computions were possible (e.g. there were enough correspondences, + * system of equations has a solution, etc) and resulting transformation satisfies some test if it's provided + * by the Odometry inheritor implementation (e.g. thresholds for maximum translation and rotation). + * @param srcImage Image data of the source frame (CV_8UC1) + * @param srcDepth Depth data of the source frame (CV_32FC1, in meters) + * @param srcMask Mask that sets which pixels have to be used from the source frame (CV_8UC1) + * @param dstImage Image data of the destination frame (CV_8UC1) + * @param dstDepth Depth data of the destination frame (CV_32FC1, in meters) + * @param dstMask Mask that sets which pixels have to be used from the destination frame (CV_8UC1) + * @param Rt Resulting transformation from the source frame to the destination one (rigid body motion): + dst_p = Rt * src_p, where dst_p is a homogeneous point in the destination frame and src_p is + homogeneous point in the source frame, + Rt is 4x4 matrix of CV_64FC1 type. + * @param initRt Initial transformation from the source frame to the destination one (optional) + */ + bool + compute(const Mat& srcImage, const Mat& srcDepth, const Mat& srcMask, const Mat& dstImage, const Mat& dstDepth, + const Mat& dstMask, Mat& Rt, const Mat& initRt = Mat()) const; + + /** One more method to compute a transformation from the source frame to the destination one. + * It is designed to save on computing the frame data (image pyramids, normals, etc.). + */ + bool + compute(Ptr& srcFrame, Ptr& dstFrame, Mat& Rt, const Mat& initRt = Mat()) const; + + /** Prepare a cache for the frame. The function checks the precomputed/passed data (throws the error if this data + * does not satisfy) and computes all remaining cache data needed for the frame. Returned size is a resolution + * of the prepared frame. + * @param frame The odometry which will process the frame. + * @param cacheType The cache type: CACHE_SRC, CACHE_DST or CACHE_ALL. + */ + virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; + + static Ptr create(const String & odometryType); + + /** @see setCameraMatrix */ + virtual cv::Mat getCameraMatrix() const = 0; + /** @copybrief getCameraMatrix @see getCameraMatrix */ + virtual void setCameraMatrix(const cv::Mat &val) = 0; + /** @see setTransformType */ + virtual int getTransformType() const = 0; + /** @copybrief getTransformType @see getTransformType */ + virtual void setTransformType(int val) = 0; + + protected: + virtual void + checkParams() const = 0; + + virtual bool + computeImpl(const Ptr& srcFrame, const Ptr& dstFrame, Mat& Rt, + const Mat& initRt) const = 0; + }; + + /** Odometry based on the paper "Real-Time Visual Odometry from Dense RGB-D Images", + * F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011. + */ + class CV_EXPORTS RgbdOdometry: public Odometry + { + public: + RgbdOdometry(); + /** Constructor. + * @param cameraMatrix Camera matrix + * @param minDepth Pixels with depth less than minDepth will not be used (in meters) + * @param maxDepth Pixels with depth larger than maxDepth will not be used (in meters) + * @param maxDepthDiff Correspondences between pixels of two given frames will be filtered out + * if their depth difference is larger than maxDepthDiff (in meters) + * @param iterCounts Count of iterations on each pyramid level. + * @param minGradientMagnitudes For each pyramid level the pixels will be filtered out + * if they have gradient magnitude less than minGradientMagnitudes[level]. + * @param maxPointsPart The method uses a random pixels subset of size frameWidth x frameHeight x pointsPart + * @param transformType Class of transformation + */ + RgbdOdometry(const Mat& cameraMatrix, float minDepth = DEFAULT_MIN_DEPTH(), float maxDepth = DEFAULT_MAX_DEPTH(), + float maxDepthDiff = DEFAULT_MAX_DEPTH_DIFF(), const std::vector& iterCounts = std::vector(), + const std::vector& minGradientMagnitudes = std::vector(), float maxPointsPart = DEFAULT_MAX_POINTS_PART(), + int transformType = RIGID_BODY_MOTION); + + virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; + + cv::Mat getCameraMatrix() const + { + return cameraMatrix; + } + void setCameraMatrix(const cv::Mat &val) + { + cameraMatrix = val; + } + double getMinDepth() const + { + return minDepth; + } + void setMinDepth(double val) + { + minDepth = val; + } + double getMaxDepth() const + { + return maxDepth; + } + void setMaxDepth(double val) + { + maxDepth = val; + } + double getMaxDepthDiff() const + { + return maxDepthDiff; + } + void setMaxDepthDiff(double val) + { + maxDepthDiff = val; + } + cv::Mat getIterationCounts() const + { + return iterCounts; + } + void setIterationCounts(const cv::Mat &val) + { + iterCounts = val; + } + cv::Mat getMinGradientMagnitudes() const + { + return minGradientMagnitudes; + } + void setMinGradientMagnitudes(const cv::Mat &val) + { + minGradientMagnitudes = val; + } + double getMaxPointsPart() const + { + return maxPointsPart; + } + void setMaxPointsPart(double val) + { + maxPointsPart = val; + } + int getTransformType() const + { + return transformType; + } + void setTransformType(int val) + { + transformType = val; + } + double getMaxTranslation() const + { + return maxTranslation; + } + void setMaxTranslation(double val) + { + maxTranslation = val; + } + double getMaxRotation() const + { + return maxRotation; + } + void setMaxRotation(double val) + { + maxRotation = val; + } + + protected: + virtual void + checkParams() const; + + virtual bool + computeImpl(const Ptr& srcFrame, const Ptr& dstFrame, Mat& Rt, + const Mat& initRt) const; + + // Some params have commented desired type. It's due to AlgorithmInfo::addParams does not support it now. + /*float*/ + double minDepth, maxDepth, maxDepthDiff; + /*vector*/ + Mat iterCounts; + /*vector*/ + Mat minGradientMagnitudes; + double maxPointsPart; + + Mat cameraMatrix; + int transformType; + + double maxTranslation, maxRotation; + }; + + /** Odometry based on the paper "KinectFusion: Real-Time Dense Surface Mapping and Tracking", + * Richard A. Newcombe, Andrew Fitzgibbon, at al, SIGGRAPH, 2011. + */ + class ICPOdometry: public Odometry + { + public: + ICPOdometry(); + /** Constructor. + * @param cameraMatrix Camera matrix + * @param minDepth Pixels with depth less than minDepth will not be used + * @param maxDepth Pixels with depth larger than maxDepth will not be used + * @param maxDepthDiff Correspondences between pixels of two given frames will be filtered out + * if their depth difference is larger than maxDepthDiff + * @param maxPointsPart The method uses a random pixels subset of size frameWidth x frameHeight x pointsPart + * @param iterCounts Count of iterations on each pyramid level. + * @param transformType Class of trasformation + */ + ICPOdometry(const Mat& cameraMatrix, float minDepth = DEFAULT_MIN_DEPTH(), float maxDepth = DEFAULT_MAX_DEPTH(), + float maxDepthDiff = DEFAULT_MAX_DEPTH_DIFF(), float maxPointsPart = DEFAULT_MAX_POINTS_PART(), + const std::vector& iterCounts = std::vector(), int transformType = RIGID_BODY_MOTION); + + virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; + + cv::Mat getCameraMatrix() const + { + return cameraMatrix; + } + void setCameraMatrix(const cv::Mat &val) + { + cameraMatrix = val; + } + double getMinDepth() const + { + return minDepth; + } + void setMinDepth(double val) + { + minDepth = val; + } + double getMaxDepth() const + { + return maxDepth; + } + void setMaxDepth(double val) + { + maxDepth = val; + } + double getMaxDepthDiff() const + { + return maxDepthDiff; + } + void setMaxDepthDiff(double val) + { + maxDepthDiff = val; + } + cv::Mat getIterationCounts() const + { + return iterCounts; + } + void setIterationCounts(const cv::Mat &val) + { + iterCounts = val; + } + double getMaxPointsPart() const + { + return maxPointsPart; + } + void setMaxPointsPart(double val) + { + maxPointsPart = val; + } + int getTransformType() const + { + return transformType; + } + void setTransformType(int val) + { + transformType = val; + } + double getMaxTranslation() const + { + return maxTranslation; + } + void setMaxTranslation(double val) + { + maxTranslation = val; + } + double getMaxRotation() const + { + return maxRotation; + } + void setMaxRotation(double val) + { + maxRotation = val; + } + Ptr getNormalsComputer() const + { + return normalsComputer; + } + + protected: + virtual void + checkParams() const; + + virtual bool + computeImpl(const Ptr& srcFrame, const Ptr& dstFrame, Mat& Rt, + const Mat& initRt) const; + + // Some params have commented desired type. It's due to AlgorithmInfo::addParams does not support it now. + /*float*/ + double minDepth, maxDepth, maxDepthDiff; + /*float*/ + double maxPointsPart; + /*vector*/ + Mat iterCounts; + + Mat cameraMatrix; + int transformType; + + double maxTranslation, maxRotation; + + mutable Ptr normalsComputer; + }; + + /** Odometry that merges RgbdOdometry and ICPOdometry by minimize sum of their energy functions. + */ + + class RgbdICPOdometry: public Odometry + { + public: + RgbdICPOdometry(); + /** Constructor. + * @param cameraMatrix Camera matrix + * @param minDepth Pixels with depth less than minDepth will not be used + * @param maxDepth Pixels with depth larger than maxDepth will not be used + * @param maxDepthDiff Correspondences between pixels of two given frames will be filtered out + * if their depth difference is larger than maxDepthDiff + * @param maxPointsPart The method uses a random pixels subset of size frameWidth x frameHeight x pointsPart + * @param iterCounts Count of iterations on each pyramid level. + * @param minGradientMagnitudes For each pyramid level the pixels will be filtered out + * if they have gradient magnitude less than minGradientMagnitudes[level]. + * @param transformType Class of trasformation + */ + RgbdICPOdometry(const Mat& cameraMatrix, float minDepth = DEFAULT_MIN_DEPTH(), float maxDepth = DEFAULT_MAX_DEPTH(), + float maxDepthDiff = DEFAULT_MAX_DEPTH_DIFF(), float maxPointsPart = DEFAULT_MAX_POINTS_PART(), + const std::vector& iterCounts = std::vector(), + const std::vector& minGradientMagnitudes = std::vector(), + int transformType = RIGID_BODY_MOTION); + + virtual Size prepareFrameCache(Ptr& frame, int cacheType) const; + + cv::Mat getCameraMatrix() const + { + return cameraMatrix; + } + void setCameraMatrix(const cv::Mat &val) + { + cameraMatrix = val; + } + double getMinDepth() const + { + return minDepth; + } + void setMinDepth(double val) + { + minDepth = val; + } + double getMaxDepth() const + { + return maxDepth; + } + void setMaxDepth(double val) + { + maxDepth = val; + } + double getMaxDepthDiff() const + { + return maxDepthDiff; + } + void setMaxDepthDiff(double val) + { + maxDepthDiff = val; + } + double getMaxPointsPart() const + { + return maxPointsPart; + } + void setMaxPointsPart(double val) + { + maxPointsPart = val; + } + cv::Mat getIterationCounts() const + { + return iterCounts; + } + void setIterationCounts(const cv::Mat &val) + { + iterCounts = val; + } + cv::Mat getMinGradientMagnitudes() const + { + return minGradientMagnitudes; + } + void setMinGradientMagnitudes(const cv::Mat &val) + { + minGradientMagnitudes = val; + } + int getTransformType() const + { + return transformType; + } + void setTransformType(int val) + { + transformType = val; + } + double getMaxTranslation() const + { + return maxTranslation; + } + void setMaxTranslation(double val) + { + maxTranslation = val; + } + double getMaxRotation() const + { + return maxRotation; + } + void setMaxRotation(double val) + { + maxRotation = val; + } + Ptr getNormalsComputer() const + { + return normalsComputer; + } + + protected: + virtual void + checkParams() const; + + virtual bool + computeImpl(const Ptr& srcFrame, const Ptr& dstFrame, Mat& Rt, + const Mat& initRt) const; + + // Some params have commented desired type. It's due to AlgorithmInfo::addParams does not support it now. + /*float*/ + double minDepth, maxDepth, maxDepthDiff; + /*float*/ + double maxPointsPart; + /*vector*/ + Mat iterCounts; + /*vector*/ + Mat minGradientMagnitudes; + + Mat cameraMatrix; + int transformType; + + double maxTranslation, maxRotation; + + mutable Ptr normalsComputer; + }; + + /** Warp the image: compute 3d points from the depth, transform them using given transformation, + * then project color point cloud to an image plane. + * This function can be used to visualize results of the Odometry algorithm. + * @param image The image (of CV_8UC1 or CV_8UC3 type) + * @param depth The depth (of type used in depthTo3d fuction) + * @param mask The mask of used pixels (of CV_8UC1), it can be empty + * @param Rt The transformation that will be applied to the 3d points computed from the depth + * @param cameraMatrix Camera matrix + * @param distCoeff Distortion coefficients + * @param warpedImage The warped image. + * @param warpedDepth The warped depth. + * @param warpedMask The warped mask. + */ + CV_EXPORTS + void + warpFrame(const Mat& image, const Mat& depth, const Mat& mask, const Mat& Rt, const Mat& cameraMatrix, + const Mat& distCoeff, Mat& warpedImage, Mat* warpedDepth = 0, Mat* warpedMask = 0); + +// TODO Depth interpolation +// Curvature +// Get rescaleDepth return dubles if asked for + +//! @} + +} /* namespace rgbd */ +} /* namespace cv */ + +#include "opencv2/rgbd/linemod.hpp" + +#endif /* __cplusplus */ +#endif + +/* End of file. */ + diff --git a/thirdparty/linux/include/opencv2/rgbd/linemod.hpp b/thirdparty/linux/include/opencv2/rgbd/linemod.hpp new file mode 100644 index 0000000..ac56291 --- /dev/null +++ b/thirdparty/linux/include/opencv2/rgbd/linemod.hpp @@ -0,0 +1,458 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_OBJDETECT_LINEMOD_HPP__ +#define __OPENCV_OBJDETECT_LINEMOD_HPP__ + +#include "opencv2/core.hpp" +#include + +/****************************************************************************************\ +* LINE-MOD * +\****************************************************************************************/ + +namespace cv { +namespace linemod { + +//! @addtogroup rgbd +//! @{ + +/** + * \brief Discriminant feature described by its location and label. + */ +struct CV_EXPORTS Feature +{ + int x; ///< x offset + int y; ///< y offset + int label; ///< Quantization + + Feature() : x(0), y(0), label(0) {} + Feature(int x, int y, int label); + + void read(const FileNode& fn); + void write(FileStorage& fs) const; +}; + +inline Feature::Feature(int _x, int _y, int _label) : x(_x), y(_y), label(_label) {} + +struct CV_EXPORTS Template +{ + int width; + int height; + int pyramid_level; + std::vector features; + + void read(const FileNode& fn); + void write(FileStorage& fs) const; +}; + +/** + * \brief Represents a modality operating over an image pyramid. + */ +class QuantizedPyramid +{ +public: + // Virtual destructor + virtual ~QuantizedPyramid() {} + + /** + * \brief Compute quantized image at current pyramid level for online detection. + * + * \param[out] dst The destination 8-bit image. For each pixel at most one bit is set, + * representing its classification. + */ + virtual void quantize(Mat& dst) const =0; + + /** + * \brief Extract most discriminant features at current pyramid level to form a new template. + * + * \param[out] templ The new template. + */ + virtual bool extractTemplate(Template& templ) const =0; + + /** + * \brief Go to the next pyramid level. + * + * \todo Allow pyramid scale factor other than 2 + */ + virtual void pyrDown() =0; + +protected: + /// Candidate feature with a score + struct Candidate + { + Candidate(int x, int y, int label, float score); + + /// Sort candidates with high score to the front + bool operator<(const Candidate& rhs) const + { + return score > rhs.score; + } + + Feature f; + float score; + }; + + /** + * \brief Choose candidate features so that they are not bunched together. + * + * \param[in] candidates Candidate features sorted by score. + * \param[out] features Destination vector of selected features. + * \param[in] num_features Number of candidates to select. + * \param[in] distance Hint for desired distance between features. + */ + static void selectScatteredFeatures(const std::vector& candidates, + std::vector& features, + size_t num_features, float distance); +}; + +inline QuantizedPyramid::Candidate::Candidate(int x, int y, int label, float _score) : f(x, y, label), score(_score) {} + +/** + * \brief Interface for modalities that plug into the LINE template matching representation. + * + * \todo Max response, to allow optimization of summing (255/MAX) features as uint8 + */ +class CV_EXPORTS Modality +{ +public: + // Virtual destructor + virtual ~Modality() {} + + /** + * \brief Form a quantized image pyramid from a source image. + * + * \param[in] src The source image. Type depends on the modality. + * \param[in] mask Optional mask. If not empty, unmasked pixels are set to zero + * in quantized image and cannot be extracted as features. + */ + Ptr process(const Mat& src, + const Mat& mask = Mat()) const + { + return processImpl(src, mask); + } + + virtual String name() const =0; + + virtual void read(const FileNode& fn) =0; + virtual void write(FileStorage& fs) const =0; + + /** + * \brief Create modality by name. + * + * The following modality types are supported: + * - "ColorGradient" + * - "DepthNormal" + */ + static Ptr create(const String& modality_type); + + /** + * \brief Load a modality from file. + */ + static Ptr create(const FileNode& fn); + +protected: + // Indirection is because process() has a default parameter. + virtual Ptr processImpl(const Mat& src, + const Mat& mask) const =0; +}; + +/** + * \brief Modality that computes quantized gradient orientations from a color image. + */ +class CV_EXPORTS ColorGradient : public Modality +{ +public: + /** + * \brief Default constructor. Uses reasonable default parameter values. + */ + ColorGradient(); + + /** + * \brief Constructor. + * + * \param weak_threshold When quantizing, discard gradients with magnitude less than this. + * \param num_features How many features a template must contain. + * \param strong_threshold Consider as candidate features only gradients whose norms are + * larger than this. + */ + ColorGradient(float weak_threshold, size_t num_features, float strong_threshold); + + virtual String name() const; + + virtual void read(const FileNode& fn); + virtual void write(FileStorage& fs) const; + + float weak_threshold; + size_t num_features; + float strong_threshold; + +protected: + virtual Ptr processImpl(const Mat& src, + const Mat& mask) const; +}; + +/** + * \brief Modality that computes quantized surface normals from a dense depth map. + */ +class CV_EXPORTS DepthNormal : public Modality +{ +public: + /** + * \brief Default constructor. Uses reasonable default parameter values. + */ + DepthNormal(); + + /** + * \brief Constructor. + * + * \param distance_threshold Ignore pixels beyond this distance. + * \param difference_threshold When computing normals, ignore contributions of pixels whose + * depth difference with the central pixel is above this threshold. + * \param num_features How many features a template must contain. + * \param extract_threshold Consider as candidate feature only if there are no differing + * orientations within a distance of extract_threshold. + */ + DepthNormal(int distance_threshold, int difference_threshold, size_t num_features, + int extract_threshold); + + virtual String name() const; + + virtual void read(const FileNode& fn); + virtual void write(FileStorage& fs) const; + + int distance_threshold; + int difference_threshold; + size_t num_features; + int extract_threshold; + +protected: + virtual Ptr processImpl(const Mat& src, + const Mat& mask) const; +}; + +/** + * \brief Debug function to colormap a quantized image for viewing. + */ +void colormap(const Mat& quantized, Mat& dst); + +/** + * \brief Represents a successful template match. + */ +struct CV_EXPORTS Match +{ + Match() + { + } + + Match(int x, int y, float similarity, const String& class_id, int template_id); + + /// Sort matches with high similarity to the front + bool operator<(const Match& rhs) const + { + // Secondarily sort on template_id for the sake of duplicate removal + if (similarity != rhs.similarity) + return similarity > rhs.similarity; + else + return template_id < rhs.template_id; + } + + bool operator==(const Match& rhs) const + { + return x == rhs.x && y == rhs.y && similarity == rhs.similarity && class_id == rhs.class_id; + } + + int x; + int y; + float similarity; + String class_id; + int template_id; +}; + +inline +Match::Match(int _x, int _y, float _similarity, const String& _class_id, int _template_id) + : x(_x), y(_y), similarity(_similarity), class_id(_class_id), template_id(_template_id) +{} + +/** + * \brief Object detector using the LINE template matching algorithm with any set of + * modalities. + */ +class CV_EXPORTS Detector +{ +public: + /** + * \brief Empty constructor, initialize with read(). + */ + Detector(); + + /** + * \brief Constructor. + * + * \param modalities Modalities to use (color gradients, depth normals, ...). + * \param T_pyramid Value of the sampling step T at each pyramid level. The + * number of pyramid levels is T_pyramid.size(). + */ + Detector(const std::vector< Ptr >& modalities, const std::vector& T_pyramid); + + /** + * \brief Detect objects by template matching. + * + * Matches globally at the lowest pyramid level, then refines locally stepping up the pyramid. + * + * \param sources Source images, one for each modality. + * \param threshold Similarity threshold, a percentage between 0 and 100. + * \param[out] matches Template matches, sorted by similarity score. + * \param class_ids If non-empty, only search for the desired object classes. + * \param[out] quantized_images Optionally return vector of quantized images. + * \param masks The masks for consideration during matching. The masks should be CV_8UC1 + * where 255 represents a valid pixel. If non-empty, the vector must be + * the same size as sources. Each element must be + * empty or the same size as its corresponding source. + */ + void match(const std::vector& sources, float threshold, std::vector& matches, + const std::vector& class_ids = std::vector(), + OutputArrayOfArrays quantized_images = noArray(), + const std::vector& masks = std::vector()) const; + + /** + * \brief Add new object template. + * + * \param sources Source images, one for each modality. + * \param class_id Object class ID. + * \param object_mask Mask separating object from background. + * \param[out] bounding_box Optionally return bounding box of the extracted features. + * + * \return Template ID, or -1 if failed to extract a valid template. + */ + int addTemplate(const std::vector& sources, const String& class_id, + const Mat& object_mask, Rect* bounding_box = NULL); + + /** + * \brief Add a new object template computed by external means. + */ + int addSyntheticTemplate(const std::vector