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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$\left<g_x, g_y\right>: \texttt{src} \rightarrow +\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping +\f$\left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src}\f$ 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$\left<f_x, f_y\right>\f$ 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 <opencv2/core.hpp> +#include <opencv2/imgproc.hpp> +#include <opencv2/imgcodecs.hpp> +#include <opencv2/highgui.hpp> +using namespace cv; + +#include <iostream> +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. + + + +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|<c ? x^2/2 : c(|x|-c/2), c=1.345 +}; + +//! Mask size for distance transform +enum DistanceTransformMasks { + DIST_MASK_3 = 3, //!< mask=3 + DIST_MASK_5 = 5, //!< mask=5 + DIST_MASK_PRECISE = 0 //!< +}; + +//! type of the threshold operation +//!  +enum ThresholdTypes { + THRESH_BINARY = 0, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{maxval}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{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<Point2f>& 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<Vec4f>& 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<int>& 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<Vec6f>& 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<int>& idx, CV_OUT std::vector<std::vector<Point2f> >& facetList, + CV_OUT std::vector<Point2f>& 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 ) + +  + + @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<Vertex> vtx; + //! All of the edges + std::vector<QuadEdge> 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. + +  + + @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<LineSegmentDetector> 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 +<http://en.wikipedia.org/wiki/Canny_edge_detector> + +@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. + + + +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 <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> 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 <opencv2/imgproc.hpp> + #include <opencv2/highgui.hpp> + + 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<Vec2f> 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<Vec4i> 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: + + + +And this is the output of the above program in case of the probabilistic 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 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 <opencv2/imgproc.hpp> + #include <opencv2/highgui.hpp> + #include <math.h> + + 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<Vec3f> 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 + + +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<X,y<Y} \texttt{image} (x,y)\f] + +\f[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\f] + +\f[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\f] + +Using these integral images, you can calculate sum, mean, and standard deviation over a specific +up-right or rotated rectangular region of the image in a constant time, for example: + +\f[\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\f] + +It makes possible to do a fast blurring or fast block correlation with a variable window size, for +example. In case of multi-channel images, sums for each channel are accumulated independently. + +As a practical example, the next figure shows the calculation of the integral of a straight +rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the +original image are shown, as well as the relative pixels in the integral images sum and tilted . + + + +@param src input image as \f$W \times H\f$, 8-bit or floating-point (32f or 64f). +@param sum integral image as \f$(W+1)\times (H+1)\f$ , 32-bit integer or floating-point (32f or 64f). +@param sqsum integral image for squared pixel values; it is \f$(W+1)\times (H+1)\f$, double-precision +floating-point (64f) array. +@param tilted integral for the image rotated by 45 degrees; it is \f$(W+1)\times (H+1)\f$ array with +the same data type as sum. +@param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or +CV_64F. +@param sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F. + */ +CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum, + OutputArray sqsum, OutputArray tilted, + int sdepth = -1, int sqdepth = -1 ); + +//! @} imgproc_misc + +//! @addtogroup imgproc_motion +//! @{ + +/** @brief Adds an image to the accumulator. + +The function adds src or some of its elements to dst : + +\f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] + +The function supports multi-channel images. Each channel is processed independently. + +The functions accumulate\* can be used, for example, to collect statistics of a scene background +viewed by a still camera and for the further foreground-background segmentation. + +@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point. +@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit +floating-point. +@param mask Optional operation mask. + +@sa accumulateSquare, accumulateProduct, accumulateWeighted + */ +CV_EXPORTS_W void accumulate( InputArray src, InputOutputArray dst, + InputArray mask = noArray() ); + +/** @brief Adds the square of a source image to the accumulator. + +The function adds the input image src or its selected region, raised to a power of 2, to the +accumulator dst : + +\f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y)^2 \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] + +The function supports multi-channel images. Each channel is processed independently. + +@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point. +@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit +floating-point. +@param mask Optional operation mask. + +@sa accumulateSquare, accumulateProduct, accumulateWeighted + */ +CV_EXPORTS_W void accumulateSquare( InputArray src, InputOutputArray dst, + InputArray mask = noArray() ); + +/** @brief Adds the per-element product of two input images to the accumulator. + +The function adds the product of two images or their selected regions to the accumulator dst : + +\f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] + +The function supports multi-channel images. Each channel is processed independently. + +@param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point. +@param src2 Second input image of the same type and the same size as src1 . +@param dst %Accumulator with the same number of channels as input images, 32-bit or 64-bit +floating-point. +@param mask Optional operation mask. + +@sa accumulate, accumulateSquare, accumulateWeighted + */ +CV_EXPORTS_W void accumulateProduct( InputArray src1, InputArray src2, + InputOutputArray dst, InputArray mask=noArray() ); + +/** @brief Updates a running average. + +The function calculates the weighted sum of the input image src and the accumulator dst so that dst +becomes a running average of a frame sequence: + +\f[\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f] + +That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). +The function supports multi-channel images. Each channel is processed independently. + +@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point. +@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit +floating-point. +@param alpha Weight of the input image. +@param mask Optional operation mask. + +@sa accumulate, accumulateSquare, accumulateProduct + */ +CV_EXPORTS_W void accumulateWeighted( InputArray src, InputOutputArray dst, + double alpha, InputArray mask = noArray() ); + +/** @brief The function is used to detect translational shifts that occur between two images. + +The operation takes advantage of the Fourier shift theorem for detecting the translational shift in +the frequency domain. It can be used for fast image registration as well as motion estimation. For +more information please see <http://en.wikipedia.org/wiki/Phase_correlation> + +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 <http://en.wikipedia.org/wiki/Hann_function>) 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 <opencv2/imgproc.hpp> + #include <opencv2/highgui.hpp> + + 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<float>(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<int>& channels, + InputArray mask, OutputArray hist, + const std::vector<int>& histSize, + const std::vector<float>& 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<int>& channels, + InputArray hist, OutputArray dst, + const std::vector<float>& 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 +<http://en.wikipedia.org/wiki/Image_moment>) 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<std::vector<cv::Point> >). +@param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), 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 <http://en.wikipedia.org/wiki/Ramer-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<Point> 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<Point> 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*. + + + +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\<int\> implies returnPoints=false, std::vector\<Point\> 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: + + + +@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 ( <http://en.wikipedia.org/wiki/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: + + + +@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. + + + +@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\<cv::Point2f\> 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<CLAHE> 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<GeneralizedHoughBallard> 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<GeneralizedHoughGuil> 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, //!<  + COLORMAP_BONE = 1, //!<  + COLORMAP_JET = 2, //!<  + COLORMAP_WINTER = 3, //!<  + COLORMAP_RAINBOW = 4, //!<  + COLORMAP_OCEAN = 5, //!<  + COLORMAP_SUMMER = 6, //!<  + COLORMAP_SPRING = 7, //!<  + COLORMAP_COOL = 8, //!<  + COLORMAP_HSV = 9, //!<  + COLORMAP_PINK = 10, //!<  + COLORMAP_HOT = 11, //!<  + COLORMAP_PARULA = 12 //!<  +}; + +/** @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. + + + +@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<vector<Point> > contours; + vector<Vec4i> 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<Point>& 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<Point2d>& 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<Vec3b> 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<Vec3b>(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 |