diff options
Diffstat (limited to 'thirdparty1/linux/include/opencv2/stereo')
| -rw-r--r-- | thirdparty1/linux/include/opencv2/stereo/descriptor.hpp | 452 | ||||
| -rw-r--r-- | thirdparty1/linux/include/opencv2/stereo/matching.hpp | 624 | ||||
| -rw-r--r-- | thirdparty1/linux/include/opencv2/stereo/stereo.hpp | 49 |
3 files changed, 1125 insertions, 0 deletions
diff --git a/thirdparty1/linux/include/opencv2/stereo/descriptor.hpp b/thirdparty1/linux/include/opencv2/stereo/descriptor.hpp new file mode 100644 index 0000000..bdbd7ce --- /dev/null +++ b/thirdparty1/linux/include/opencv2/stereo/descriptor.hpp @@ -0,0 +1,452 @@ +//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. + +/*****************************************************************************************************************\ +* The interface contains the main descriptors that will be implemented in the descriptor class * +\*****************************************************************************************************************/ + +#include <stdint.h> +#ifndef _OPENCV_DESCRIPTOR_HPP_ +#define _OPENCV_DESCRIPTOR_HPP_ +#ifdef __cplusplus + +namespace cv +{ + namespace stereo + { + //types of supported kernels + enum { + CV_DENSE_CENSUS, CV_SPARSE_CENSUS, + CV_CS_CENSUS, CV_MODIFIED_CS_CENSUS, CV_MODIFIED_CENSUS_TRANSFORM, + CV_MEAN_VARIATION, CV_STAR_KERNEL + }; + //!Mean Variation is a robust kernel that compares a pixel + //!not just with the center but also with the mean of the window + template<int num_images> + struct MVKernel + { + uint8_t *image[num_images]; + int *integralImage[num_images]; + int stop; + MVKernel(){} + MVKernel(uint8_t **images, int **integral) + { + for(int i = 0; i < num_images; i++) + { + image[i] = images[i]; + integralImage[i] = integral[i]; + } + stop = num_images; + } + void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const + { + (void)w2; + for (int i = 0; i < stop; i++) + { + if (image[i][rrWidth + jj] > image[i][rWidth + j]) + { + c[i] = c[i] + 1; + } + c[i] = c[i] << 1; + if (integralImage[i][rrWidth + jj] > image[i][rWidth + j]) + { + c[i] = c[i] + 1; + } + c[i] = c[i] << 1; + } + } + }; + //!Compares pixels from a patch giving high weights to pixels in which + //!the intensity is higher. The other pixels receive a lower weight + template <int num_images> + struct MCTKernel + { + uint8_t *image[num_images]; + int t,imageStop; + MCTKernel(){} + MCTKernel(uint8_t ** images, int threshold) + { + for(int i = 0; i < num_images; i++) + { + image[i] = images[i]; + } + imageStop = num_images; + t = threshold; + } + void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const + { + (void)w2; + for(int i = 0; i < imageStop; i++) + { + if (image[i][rrWidth + jj] > image[i][rWidth + j] - t) + { + c[i] = c[i] << 1; + c[i] = c[i] + 1; + c[i] = c[i] << 1; + c[i] = c[i] + 1; + } + else if (image[i][rWidth + j] - t < image[i][rrWidth + jj] && image[i][rWidth + j] + t >= image[i][rrWidth + jj]) + { + c[i] = c[i] << 2; + c[i] = c[i] + 1; + } + else + { + c[i] <<= 2; + } + } + } + }; + //!A madified cs census that compares a pixel with the imediat neightbour starting + //!from the center + template<int num_images> + struct ModifiedCsCensus + { + uint8_t *image[num_images]; + int n2; + int imageStop; + ModifiedCsCensus(){} + ModifiedCsCensus(uint8_t **images, int ker) + { + for(int i = 0; i < num_images; i++) + image[i] = images[i]; + imageStop = num_images; + n2 = ker; + } + void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const + { + (void)j; + (void)rWidth; + for(int i = 0; i < imageStop; i++) + { + if (image[i][(rrWidth + jj)] > image[i][(w2 + (jj + n2))]) + { + c[i] = c[i] + 1; + } + c[i] = c[i] * 2; + } + } + }; + //!A kernel in which a pixel is compared with the center of the window + template<int num_images> + struct CensusKernel + { + uint8_t *image[num_images]; + int imageStop; + CensusKernel(){} + CensusKernel(uint8_t **images) + { + for(int i = 0; i < num_images; i++) + image[i] = images[i]; + imageStop = num_images; + } + void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const + { + (void)w2; + for(int i = 0; i < imageStop; i++) + { + ////compare a pixel with the center from the kernel + if (image[i][rrWidth + jj] > image[i][rWidth + j]) + { + c[i] += 1; + } + c[i] <<= 1; + } + } + }; + //template clas which efficiently combines the descriptors + template <int step_start, int step_end, int step_inc,int nr_img, typename Kernel> + class CombinedDescriptor:public ParallelLoopBody + { + private: + int width, height,n2; + int stride_; + int *dst[nr_img]; + Kernel kernel_; + int n2_stop; + public: + CombinedDescriptor(int w, int h,int stride, int k2, int **distance, Kernel kernel,int k2Stop) + { + width = w; + height = h; + n2 = k2; + stride_ = stride; + for(int i = 0; i < nr_img; i++) + dst[i] = distance[i]; + kernel_ = kernel; + n2_stop = k2Stop; + } + void operator()(const cv::Range &r) const { + for (int i = r.start; i <= r.end ; i++) + { + int rWidth = i * stride_; + for (int j = n2 + 2; j <= width - n2 - 2; j++) + { + int c[nr_img]; + memset(c,0,nr_img); + for(int step = step_start; step <= step_end; step += step_inc) + { + for (int ii = - n2; ii <= + n2_stop; ii += step) + { + int rrWidth = (ii + i) * stride_; + int rrWidthC = (ii + i + n2) * stride_; + for (int jj = j - n2; jj <= j + n2; jj += step) + { + if (ii != i || jj != j) + { + kernel_(rrWidth,rrWidthC, rWidth, jj, j,c); + } + } + } + } + for(int l = 0; l < nr_img; l++) + dst[l][rWidth + j] = c[l]; + } + } + } + }; + //!calculate the mean of every windowSizexWindwoSize block from the integral Image + //!this is a preprocessing for MV kernel + class MeanKernelIntegralImage : public ParallelLoopBody + { + private: + int *img; + int windowSize,width; + float scalling; + int *c; + public: + MeanKernelIntegralImage(const cv::Mat &image, int window,float scale, int *cost): + img((int *)image.data),windowSize(window) ,width(image.cols) ,scalling(scale) , c(cost){}; + void operator()(const cv::Range &r) const{ + for (int i = r.start; i <= r.end; i++) + { + int iw = i * width; + for (int j = windowSize + 1; j <= width - windowSize - 1; j++) + { + c[iw + j] = (int)((img[(i + windowSize - 1) * width + j + windowSize - 1] + img[(i - windowSize - 1) * width + j - windowSize - 1] + - img[(i + windowSize) * width + j - windowSize] - img[(i - windowSize) * width + j + windowSize]) * scalling); + } + } + } + }; + //!implementation for the star kernel descriptor + template<int num_images> + class StarKernelCensus:public ParallelLoopBody + { + private: + uint8_t *image[num_images]; + int *dst[num_images]; + int n2, width, height, im_num,stride_; + public: + StarKernelCensus(const cv::Mat *img, int k2, int **distance) + { + for(int i = 0; i < num_images; i++) + { + image[i] = img[i].data; + dst[i] = distance[i]; + } + n2 = k2; + width = img[0].cols; + height = img[0].rows; + im_num = num_images; + stride_ = (int)img[0].step; + } + void operator()(const cv::Range &r) const { + for (int i = r.start; i <= r.end ; i++) + { + int rWidth = i * stride_; + for (int j = n2; j <= width - n2; j++) + { + for(int d = 0 ; d < im_num; d++) + { + int c = 0; + for(int step = 4; step > 0; step--) + { + for (int ii = i - step; ii <= i + step; ii += step) + { + int rrWidth = ii * stride_; + for (int jj = j - step; jj <= j + step; jj += step) + { + if (image[d][rrWidth + jj] > image[d][rWidth + j]) + { + c = c + 1; + } + c = c * 2; + } + } + } + for (int ii = -1; ii <= +1; ii++) + { + int rrWidth = (ii + i) * stride_; + if (i == -1) + { + if (ii + i != i) + { + if (image[d][rrWidth + j] > image[d][rWidth + j]) + { + c = c + 1; + } + c = c * 2; + } + } + else if (i == 0) + { + for (int j2 = -1; j2 <= 1; j2 += 2) + { + if (ii + i != i) + { + if (image[d][rrWidth + j + j2] > image[d][rWidth + j]) + { + c = c + 1; + } + c = c * 2; + } + } + } + else + { + if (ii + i != i) + { + if (image[d][rrWidth + j] > image[d][rWidth + j]) + { + c = c + 1; + } + c = c * 2; + } + } + } + dst[d][rWidth + j] = c; + } + } + } + } + }; + //!paralel implementation of the center symetric census + template <int num_images> + class SymetricCensus:public ParallelLoopBody + { + private: + uint8_t *image[num_images]; + int *dst[num_images]; + int n2, width, height, im_num,stride_; + public: + SymetricCensus(const cv::Mat *img, int k2, int **distance) + { + for(int i = 0; i < num_images; i++) + { + image[i] = img[i].data; + dst[i] = distance[i]; + } + n2 = k2; + width = img[0].cols; + height = img[0].rows; + im_num = num_images; + stride_ = (int)img[0].step; + } + void operator()(const cv::Range &r) const { + for (int i = r.start; i <= r.end ; i++) + { + int distV = i*stride_; + for (int j = n2; j <= width - n2; j++) + { + for(int d = 0; d < im_num; d++) + { + int c = 0; + //the classic center symetric census which compares the curent pixel with its symetric not its center. + for (int ii = -n2; ii <= 0; ii++) + { + int rrWidth = (ii + i) * stride_; + for (int jj = -n2; jj <= +n2; jj++) + { + if (image[d][(rrWidth + (jj + j))] > image[d][((ii * (-1) + i) * width + (-1 * jj) + j)]) + { + c = c + 1; + } + c = c * 2; + if(ii == 0 && jj < 0) + { + if (image[d][(i * width + (jj + j))] > image[d][(i * width + (-1 * jj) + j)]) + { + c = c + 1; + } + c = c * 2; + } + } + } + dst[d][(distV + j)] = c; + } + } + } + } + }; + /** + Two variations of census applied on input images + Implementation of a census transform which is taking into account just the some pixels from the census kernel thus allowing for larger block sizes + **/ + //void applyCensusOnImages(const cv::Mat &im1,const cv::Mat &im2, int kernelSize, cv::Mat &dist, cv::Mat &dist2, const int type); + CV_EXPORTS void censusTransform(const cv::Mat &image1, const cv::Mat &image2, int kernelSize, cv::Mat &dist1, cv::Mat &dist2, const int type); + //single image census transform + CV_EXPORTS void censusTransform(const cv::Mat &image1, int kernelSize, cv::Mat &dist1, const int type); + /** + STANDARD_MCT - Modified census which is memorizing for each pixel 2 bits and includes a tolerance to the pixel comparison + MCT_MEAN_VARIATION - Implementation of a modified census transform which is also taking into account the variation to the mean of the window not just the center pixel + **/ + CV_EXPORTS void modifiedCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1,cv::Mat &dist2, const int type, int t = 0 , const cv::Mat &IntegralImage1 = cv::Mat::zeros(100,100,CV_8UC1), const cv::Mat &IntegralImage2 = cv::Mat::zeros(100,100,CV_8UC1)); + //single version of modified census transform descriptor + CV_EXPORTS void modifiedCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist, const int type, int t = 0 ,const cv::Mat &IntegralImage = cv::Mat::zeros(100,100,CV_8UC1)); + /**The classical center symetric census + A modified version of cs census which is comparing a pixel with its correspondent after the center + **/ + CV_EXPORTS void symetricCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1, cv::Mat &dist2, const int type); + //single version of census transform + CV_EXPORTS void symetricCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist1, const int type); + //in a 9x9 kernel only certain positions are choosen + CV_EXPORTS void starCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1,cv::Mat &dist2); + //single image version of star kernel + CV_EXPORTS void starCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist); + //integral image computation used in the Mean Variation Census Transform + void imageMeanKernelSize(const cv::Mat &img, int windowSize, cv::Mat &c); + } +} +#endif +#endif +/*End of file*/ diff --git a/thirdparty1/linux/include/opencv2/stereo/matching.hpp b/thirdparty1/linux/include/opencv2/stereo/matching.hpp new file mode 100644 index 0000000..2238961 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/stereo/matching.hpp @@ -0,0 +1,624 @@ +//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. + +/*****************************************************************************************************************\ +* The interface contains the main methods for computing the matching between the left and right images * +* * +\******************************************************************************************************************/ +#include <stdint.h> + +#ifndef _OPENCV_MATCHING_HPP_ +#define _OPENCV_MATCHING_HPP_ +#ifdef __cplusplus + +namespace cv +{ + namespace stereo + { + class Matching + { + private: + //!The maximum disparity + int maxDisparity; + //!the factor by which we are multiplying the disparity + int scallingFactor; + //!the confidence to which a min disparity found is good or not + double confidenceCheck; + //!the LUT used in case SSE is not available + int hamLut[65537]; + //!function used for getting the minimum disparity from the cost volume" + static int minim(short *c, int iwpj, int widthDisp,const double confidence, const int search_region) + { + double mini, mini2, mini3; + mini = mini2 = mini3 = DBL_MAX; + int index = 0; + int iw = iwpj; + int widthDisp2; + widthDisp2 = widthDisp; + widthDisp -= 1; + for (int i = 0; i <= widthDisp; i++) + { + if (c[(iw + i * search_region) * widthDisp2 + i] < mini) + { + mini3 = mini2; + mini2 = mini; + mini = c[(iw + i * search_region) * widthDisp2 + i]; + index = i; + } + else if (c[(iw + i * search_region) * widthDisp2 + i] < mini2) + { + mini3 = mini2; + mini2 = c[(iw + i * search_region) * widthDisp2 + i]; + } + else if (c[(iw + i * search_region) * widthDisp2 + i] < mini3) + { + mini3 = c[(iw + i * search_region) * widthDisp2 + i]; + } + } + if(mini != 0) + { + if (mini3 / mini <= confidence) + return index; + } + return -1; + } + //!Interpolate in order to obtain better results + //!function for refining the disparity at sub pixel using simetric v + static double symetricVInterpolation(short *c, int iwjp, int widthDisp, int winDisp,const int search_region) + { + if (winDisp == 0 || winDisp == widthDisp - 1) + return winDisp; + double m2m1, m3m1, m3, m2, m1; + m2 = c[(iwjp + (winDisp - 1) * search_region) * widthDisp + winDisp - 1]; + m3 = c[(iwjp + (winDisp + 1) * search_region)* widthDisp + winDisp + 1]; + m1 = c[(iwjp + winDisp * search_region) * widthDisp + winDisp]; + m2m1 = m2 - m1; + m3m1 = m3 - m1; + if (m2m1 == 0 || m3m1 == 0) return winDisp; + double p; + p = 0; + if (m2 > m3) + { + p = (0.5 - 0.25 * ((m3m1 * m3m1) / (m2m1 * m2m1) + (m3m1 / m2m1))); + } + else + { + p = -1 * (0.5 - 0.25 * ((m2m1 * m2m1) / (m3m1 * m3m1) + (m2m1 / m3m1))); + } + if (p >= -0.5 && p <= 0.5) + p = winDisp + p; + return p; + } + //!a pre processing function that generates the Hamming LUT in case the algorithm will ever be used on platform where SSE is not available + void hammingLut() + { + for (int i = 0; i <= 65536; i++) + { + int dist = 0; + int j = i; + //we number the bits from our number + while (j) + { + dist = dist + 1; + j = j & (j - 1); + } + hamLut[i] = dist; + } + } + //!the class used in computing the hamming distance + class hammingDistance : public ParallelLoopBody + { + private: + int *left, *right; + short *c; + int v,kernelSize, width; + int MASK; + int *hammLut; + public : + hammingDistance(const Mat &leftImage, const Mat &rightImage, short *cost, int maxDisp, int kerSize, int *hammingLUT): + left((int *)leftImage.data), right((int *)rightImage.data), c(cost), v(maxDisp),kernelSize(kerSize),width(leftImage.cols), MASK(65535), hammLut(hammingLUT){} + void operator()(const cv::Range &r) const { + for (int i = r.start; i <= r.end ; i++) + { + int iw = i * width; + for (int j = kernelSize; j < width - kernelSize; j++) + { + int j2; + int xorul; + int iwj; + iwj = iw + j; + for (int d = 0; d <= v; d++) + { + j2 = (0 > j - d) ? (0) : (j - d); + xorul = left[(iwj)] ^ right[(iw + j2)]; +#if CV_POPCNT + if (checkHardwareSupport(CV_CPU_POPCNT)) + { + c[(iwj)* (v + 1) + d] = (short)_mm_popcnt_u32(xorul); + } + else +#endif + { + c[(iwj)* (v + 1) + d] = (short)(hammLut[xorul & MASK] + hammLut[(xorul >> 16) & MASK]); + } + } + } + } + } + }; + //!cost aggregation + class agregateCost:public ParallelLoopBody + { + private: + int win; + short *c, *parSum; + int maxDisp,width, height; + public: + agregateCost(const Mat &partialSums, int windowSize, int maxDispa, Mat &cost) + { + win = windowSize / 2; + c = (short *)cost.data; + maxDisp = maxDispa; + width = cost.cols / ( maxDisp + 1) - 1; + height = cost.rows - 1; + parSum = (short *)partialSums.data; + } + void operator()(const cv::Range &r) const { + for (int i = r.start; i <= r.end; i++) + { + int iwi = (i - 1) * width; + for (int j = win + 1; j <= width - win - 1; j++) + { + int w1 = ((i + win + 1) * width + j + win) * (maxDisp + 1); + int w2 = ((i - win) * width + j - win - 1) * (maxDisp + 1); + int w3 = ((i + win + 1) * width + j - win - 1) * (maxDisp + 1); + int w4 = ((i - win) * width + j + win) * (maxDisp + 1); + int w = (iwi + j - 1) * (maxDisp + 1); + for (int d = 0; d <= maxDisp; d++) + { + c[w + d] = parSum[w1 + d] + parSum[w2 + d] + - parSum[w3 + d] - parSum[w4 + d]; + } + } + } + } + }; + //!class that is responsable for generating the disparity map + class makeMap:public ParallelLoopBody + { + private: + //enum used to notify wether we are searching on the vertical ie (lr) or diagonal (rl) + enum {CV_VERTICAL_SEARCH, CV_DIAGONAL_SEARCH}; + int width,disparity,scallingFact,th; + double confCheck; + uint8_t *map; + short *c; + public: + makeMap(const Mat &costVolume, int threshold, int maxDisp, double confidence,int scale, Mat &mapFinal) + { + c = (short *)costVolume.data; + map = mapFinal.data; + disparity = maxDisp; + width = costVolume.cols / ( disparity + 1) - 1; + th = threshold; + scallingFact = scale; + confCheck = confidence; + } + void operator()(const cv::Range &r) const { + for (int i = r.start; i <= r.end ; i++) + { + int lr; + int v = -1; + double p1, p2; + int iw = i * width; + for (int j = 0; j < width; j++) + { + lr = Matching:: minim(c, iw + j, disparity + 1, confCheck,CV_VERTICAL_SEARCH); + if (lr != -1) + { + v = Matching::minim(c, iw + j - lr, disparity + 1, confCheck,CV_DIAGONAL_SEARCH); + if (v != -1) + { + p1 = Matching::symetricVInterpolation(c, iw + j - lr, disparity + 1, v,CV_DIAGONAL_SEARCH); + p2 = Matching::symetricVInterpolation(c, iw + j, disparity + 1, lr,CV_VERTICAL_SEARCH); + if (abs(p1 - p2) <= th) + map[iw + j] = (uint8_t)((p2)* scallingFact); + else + { + map[iw + j] = 0; + } + } + else + { + if (width - j <= disparity) + { + p2 = Matching::symetricVInterpolation(c, iw + j, disparity + 1, lr,CV_VERTICAL_SEARCH); + map[iw + j] = (uint8_t)(p2* scallingFact); + } + } + } + else + { + map[iw + j] = 0; + } + } + } + } + }; + //!median 1x9 paralelized filter + template <typename T> + class Median1x9:public ParallelLoopBody + { + private: + T *original; + T *filtered; + int height, width; + public: + Median1x9(const Mat &originalImage, Mat &filteredImage) + { + original = (T *)originalImage.data; + filtered = (T *)filteredImage.data; + height = originalImage.rows; + width = originalImage.cols; + } + void operator()(const cv::Range &r) const{ + for (int m = r.start; m <= r.end; m++) + { + for (int n = 4; n < width - 4; ++n) + { + int k = 0; + T window[9]; + for (int i = n - 4; i <= n + 4; ++i) + window[k++] = original[m * width + i]; + for (int j = 0; j < 5; ++j) + { + int min = j; + for (int l = j + 1; l < 9; ++l) + if (window[l] < window[min]) + min = l; + const T temp = window[j]; + window[j] = window[min]; + window[min] = temp; + } + filtered[m * width + n] = window[4]; + } + } + } + }; + //!median 9x1 paralelized filter + template <typename T> + class Median9x1:public ParallelLoopBody + { + private: + T *original; + T *filtered; + int height, width; + public: + Median9x1(const Mat &originalImage, Mat &filteredImage) + { + original = (T *)originalImage.data; + filtered = (T *)filteredImage.data; + height = originalImage.rows; + width = originalImage.cols; + } + void operator()(const Range &r) const{ + for (int n = r.start; n <= r.end; ++n) + { + for (int m = 4; m < height - 4; ++m) + { + int k = 0; + T window[9]; + for (int i = m - 4; i <= m + 4; ++i) + window[k++] = original[i * width + n]; + for (int j = 0; j < 5; j++) + { + int min = j; + for (int l = j + 1; l < 9; ++l) + if (window[l] < window[min]) + min = l; + const T temp = window[j]; + window[j] = window[min]; + window[min] = temp; + } + filtered[m * width + n] = window[4]; + } + } + } + }; + protected: + //arrays used in the region removal + Mat speckleY; + Mat speckleX; + Mat puss; + //int *specklePointX; + //int *specklePointY; + //long long *pus; + int previous_size; + //!method for setting the maximum disparity + void setMaxDisparity(int val) + { + CV_Assert(val > 10); + this->maxDisparity = val; + } + //!method for getting the disparity + int getMaxDisparity() + { + return this->maxDisparity; + } + //! a number by which the disparity will be multiplied for better display + void setScallingFactor(int val) + { + CV_Assert(val > 0); + this->scallingFactor = val; + } + //!method for getting the scalling factor + int getScallingFactor() + { + return scallingFactor; + } + //!setter for the confidence check + void setConfidence(double val) + { + CV_Assert(val >= 1); + this->confidenceCheck = val; + } + //getter for confidence check + double getConfidence() + { + return confidenceCheck; + } + //! Hamming distance computation method + //! leftImage and rightImage are the two transformed images + //! the cost is the resulted cost volume and kernel Size is the size of the matching window + void hammingDistanceBlockMatching(const Mat &leftImage, const Mat &rightImage, Mat &cost, const int kernelSize= 9) + { + CV_Assert(leftImage.cols == rightImage.cols); + CV_Assert(leftImage.rows == rightImage.rows); + CV_Assert(kernelSize % 2 != 0); + CV_Assert(cost.rows == leftImage.rows); + CV_Assert(cost.cols / (maxDisparity + 1) == leftImage.cols); + short *c = (short *)cost.data; + memset(c, 0, sizeof(c[0]) * leftImage.cols * leftImage.rows * (maxDisparity + 1)); + parallel_for_(cv::Range(kernelSize / 2,leftImage.rows - kernelSize / 2), hammingDistance(leftImage,rightImage,(short *)cost.data,maxDisparity,kernelSize / 2,hamLut)); + } + //preprocessing the cost volume in order to get it ready for aggregation + void costGathering(const Mat &hammingDistanceCost, Mat &cost) + { + CV_Assert(hammingDistanceCost.rows == hammingDistanceCost.rows); + CV_Assert(hammingDistanceCost.type() == CV_16S); + CV_Assert(cost.type() == CV_16S); + int maxDisp = maxDisparity; + int width = cost.cols / ( maxDisp + 1) - 1; + int height = cost.rows - 1; + short *c = (short *)cost.data; + short *ham = (short *)hammingDistanceCost.data; + memset(c, 0, sizeof(c[0]) * (width + 1) * (height + 1) * (maxDisp + 1)); + for (int i = 1; i <= height; i++) + { + int iw = i * width; + int iwi = (i - 1) * width; + for (int j = 1; j <= width; j++) + { + int iwj = (iw + j) * (maxDisp + 1); + int iwjmu = (iw + j - 1) * (maxDisp + 1); + int iwijmu = (iwi + j - 1) * (maxDisp + 1); + for (int d = 0; d <= maxDisp; d++) + { + c[iwj + d] = ham[iwijmu + d] + c[iwjmu + d]; + } + } + } + for (int i = 1; i <= height; i++) + { + for (int j = 1; j <= width; j++) + { + int iwj = (i * width + j) * (maxDisp + 1); + int iwjmu = ((i - 1) * width + j) * (maxDisp + 1); + for (int d = 0; d <= maxDisp; d++) + { + c[iwj + d] += c[iwjmu + d]; + } + } + } + } + //!The aggregation on the cost volume + void blockAgregation(const Mat &partialSums, int windowSize, Mat &cost) + { + CV_Assert(windowSize % 2 != 0); + CV_Assert(partialSums.rows == cost.rows); + CV_Assert(partialSums.cols == cost.cols); + int win = windowSize / 2; + short *c = (short *)cost.data; + int maxDisp = maxDisparity; + int width = cost.cols / ( maxDisp + 1) - 1; + int height = cost.rows - 1; + memset(c, 0, sizeof(c[0]) * width * height * (maxDisp + 1)); + parallel_for_(cv::Range(win + 1,height - win - 1), agregateCost(partialSums,windowSize,maxDisp,cost)); + } + //!remove small regions that have an area smaller than t, we fill the region with the average of the good pixels around it + template <typename T> + void smallRegionRemoval(const Mat ¤tMap, int t, Mat &out) + { + CV_Assert(currentMap.cols == out.cols); + CV_Assert(currentMap.rows == out.rows); + CV_Assert(t >= 0); + int *pus = (int *)puss.data; + int *specklePointX = (int *)speckleX.data; + int *specklePointY = (int *)speckleY.data; + memset(pus, 0, previous_size * sizeof(pus[0])); + T *map = (T *)currentMap.data; + T *outputMap = (T *)out.data; + int height = currentMap.rows; + int width = currentMap.cols; + T k = 1; + int st, dr; + int di[] = { -1, -1, -1, 0, 1, 1, 1, 0 }, + dj[] = { -1, 0, 1, 1, 1, 0, -1, -1 }; + int speckle_size = 0; + st = 0; + dr = 0; + for (int i = 1; i < height - 1; i++) + { + int iw = i * width; + for (int j = 1; j < width - 1; j++) + { + if (map[iw + j] != 0) + { + outputMap[iw + j] = map[iw + j]; + } + else if (map[iw + j] == 0) + { + T nr = 1; + T avg = 0; + speckle_size = dr; + specklePointX[dr] = i; + specklePointY[dr] = j; + pus[i * width + j] = 1; + dr++; + map[iw + j] = k; + while (st < dr) + { + int ii = specklePointX[st]; + int jj = specklePointY[st]; + //going on 8 directions + for (int d = 0; d < 8; d++) + {//if insisde + if (ii + di[d] >= 0 && ii + di[d] < height && jj + dj[d] >= 0 && jj + dj[d] < width && + pus[(ii + di[d]) * width + jj + dj[d]] == 0) + { + T val = map[(ii + di[d]) * width + jj + dj[d]]; + if (val == 0) + { + map[(ii + di[d]) * width + jj + dj[d]] = k; + specklePointX[dr] = (ii + di[d]); + specklePointY[dr] = (jj + dj[d]); + dr++; + pus[(ii + di[d]) * width + jj + dj[d]] = 1; + }//this means that my point is a good point to be used in computing the final filling value + else if (val >= 1 && val < 250) + { + avg += val; + nr++; + } + } + } + st++; + }//if hole size is smaller than a specified threshold we fill the respective hole with the average of the good neighbours + if (st - speckle_size <= t) + { + T fillValue = (T)(avg / nr); + while (speckle_size < st) + { + int ii = specklePointX[speckle_size]; + int jj = specklePointY[speckle_size]; + outputMap[ii * width + jj] = fillValue; + speckle_size++; + } + } + } + } + } + } + //!Method responsible for generating the disparity map + //!function for generating disparity maps at sub pixel level + /* costVolume - represents the cost volume + * width, height - represent the width and height of the iage + *disparity - represents the maximum disparity + *map - is the disparity map that will result + *th - is the LR threshold + */ + void dispartyMapFormation(const Mat &costVolume, Mat &mapFinal, int th) + { + uint8_t *map = mapFinal.data; + int disparity = maxDisparity; + int width = costVolume.cols / ( disparity + 1) - 1; + int height = costVolume.rows - 1; + memset(map, 0, sizeof(map[0]) * width * height); + parallel_for_(Range(0,height - 1), makeMap(costVolume,th,disparity,confidenceCheck,scallingFactor,mapFinal)); + } + public: + //!a median filter of 1x9 and 9x1 + //!1x9 median filter + template<typename T> + void Median1x9Filter(const Mat &originalImage, Mat &filteredImage) + { + CV_Assert(originalImage.rows == filteredImage.rows); + CV_Assert(originalImage.cols == filteredImage.cols); + parallel_for_(Range(1,originalImage.rows - 2), Median1x9<T>(originalImage,filteredImage)); + } + //!9x1 median filter + template<typename T> + void Median9x1Filter(const Mat &originalImage, Mat &filteredImage) + { + CV_Assert(originalImage.cols == filteredImage.cols); + CV_Assert(originalImage.cols == filteredImage.cols); + parallel_for_(Range(1,originalImage.cols - 2), Median9x1<T>(originalImage,filteredImage)); + } + //!constructor for the matching class + //!maxDisp - represents the maximum disparity + Matching(void) + { + hammingLut(); + } + ~Matching(void) + { + } + //constructor for the matching class + //maxDisp - represents the maximum disparity + //confidence - represents the confidence check + Matching(int maxDisp, int scalling = 4, int confidence = 6) + { + //set the maximum disparity + setMaxDisparity(maxDisp); + //set scalling factor + setScallingFactor(scalling); + //set the value for the confidence + setConfidence(confidence); + //generate the hamming lut in case SSE is not available + hammingLut(); + } + }; + } +} +#endif +#endif +/*End of file*/ diff --git a/thirdparty1/linux/include/opencv2/stereo/stereo.hpp b/thirdparty1/linux/include/opencv2/stereo/stereo.hpp new file mode 100644 index 0000000..bab7c41 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/stereo/stereo.hpp @@ -0,0 +1,49 @@ +/*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/stereo.hpp" + |