Renesas
openCV library for Renesas RZ/A
Dependents: RZ_A2M_Mbed_samples
Diff: include/opencv2/imgproc.hpp
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+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
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+//
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+// 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
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+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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+//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 and miss"
+ //!< .- Only supported for CV_8UC1 binary images. Tutorial can be found in [this page](https://web.archive.org/web/20160316070407/http://opencv-code.com/tutorials/hit-or-miss-transform-in-opencv/)
+};
+
+//! 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.
+
+@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