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OpenCV 2.4.3.1
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## org.opencv.imgproc
Class Imgproc

    java.lang.Object
      org.opencv.imgproc.Imgproc

    public class Imgproc

extends java.lang.Object

Field Summary
`static int` | `ADAPTIVE_THRESH_GAUSSIAN_C`
`static int` | `ADAPTIVE_THRESH_MEAN_C`
`static int` | `BORDER_CONSTANT`
`static int` | `BORDER_DEFAULT`
`static int` | `BORDER_ISOLATED`
`static int` | `BORDER_REFLECT`
`static int` | `BORDER_REFLECT_101`
`static int` | `BORDER_REFLECT101`
`static int` | `BORDER_REPLICATE`
`static int` | `BORDER_TRANSPARENT`
`static int` | `BORDER_WRAP`
`static int` | `CHAIN_APPROX_NONE`
`static int` | `CHAIN_APPROX_SIMPLE`
`static int` | `CHAIN_APPROX_TC89_KCOS`
`static int` | `CHAIN_APPROX_TC89_L1`
`static int` | `COLOR_BayerBG2BGR`
`static int` | `COLOR_BayerBG2BGR_VNG`
`static int` | `COLOR_BayerBG2GRAY`
`static int` | `COLOR_BayerBG2RGB`
`static int` | `COLOR_BayerBG2RGB_VNG`
`static int` | `COLOR_BayerGB2BGR`
`static int` | `COLOR_BayerGB2BGR_VNG`
`static int` | `COLOR_BayerGB2GRAY`
`static int` | `COLOR_BayerGB2RGB`
`static int` | `COLOR_BayerGB2RGB_VNG`
`static int` | `COLOR_BayerGR2BGR`
`static int` | `COLOR_BayerGR2BGR_VNG`
`static int` | `COLOR_BayerGR2GRAY`
`static int` | `COLOR_BayerGR2RGB`
`static int` | `COLOR_BayerGR2RGB_VNG`
`static int` | `COLOR_BayerRG2BGR`
`static int` | `COLOR_BayerRG2BGR_VNG`
`static int` | `COLOR_BayerRG2GRAY`
`static int` | `COLOR_BayerRG2RGB`
`static int` | `COLOR_BayerRG2RGB_VNG`
`static int` | `COLOR_BGR2BGR555`
`static int` | `COLOR_BGR2BGR565`
`static int` | `COLOR_BGR2BGRA`
`static int` | `COLOR_BGR2GRAY`
`static int` | `COLOR_BGR2HLS`
`static int` | `COLOR_BGR2HLS_FULL`
`static int` | `COLOR_BGR2HSV`
`static int` | `COLOR_BGR2HSV_FULL`
`static int` | `COLOR_BGR2Lab`
`static int` | `COLOR_BGR2Luv`
`static int` | `COLOR_BGR2RGB`
`static int` | `COLOR_BGR2RGBA`
`static int` | `COLOR_BGR2XYZ`
`static int` | `COLOR_BGR2YCrCb`
`static int` | `COLOR_BGR2YUV`
`static int` | `COLOR_BGR5552BGR`
`static int` | `COLOR_BGR5552BGRA`
`static int` | `COLOR_BGR5552GRAY`
`static int` | `COLOR_BGR5552RGB`
`static int` | `COLOR_BGR5552RGBA`
`static int` | `COLOR_BGR5652BGR`
`static int` | `COLOR_BGR5652BGRA`
`static int` | `COLOR_BGR5652GRAY`
`static int` | `COLOR_BGR5652RGB`
`static int` | `COLOR_BGR5652RGBA`
`static int` | `COLOR_BGRA2BGR`
`static int` | `COLOR_BGRA2BGR555`
`static int` | `COLOR_BGRA2BGR565`
`static int` | `COLOR_BGRA2GRAY`
`static int` | `COLOR_BGRA2RGB`
`static int` | `COLOR_BGRA2RGBA`
`static int` | `COLOR_COLORCVT_MAX`
`static int` | `COLOR_GRAY2BGR`
`static int` | `COLOR_GRAY2BGR555`
`static int` | `COLOR_GRAY2BGR565`
`static int` | `COLOR_GRAY2BGRA`
`static int` | `COLOR_GRAY2RGB`
`static int` | `COLOR_GRAY2RGBA`
`static int` | `COLOR_HLS2BGR`
`static int` | `COLOR_HLS2BGR_FULL`
`static int` | `COLOR_HLS2RGB`
`static int` | `COLOR_HLS2RGB_FULL`
`static int` | `COLOR_HSV2BGR`
`static int` | `COLOR_HSV2BGR_FULL`
`static int` | `COLOR_HSV2RGB`
`static int` | `COLOR_HSV2RGB_FULL`
`static int` | `COLOR_Lab2BGR`
`static int` | `COLOR_Lab2LBGR`
`static int` | `COLOR_Lab2LRGB`
`static int` | `COLOR_Lab2RGB`
`static int` | `COLOR_LBGR2Lab`
`static int` | `COLOR_LBGR2Luv`
`static int` | `COLOR_LRGB2Lab`
`static int` | `COLOR_LRGB2Luv`
`static int` | `COLOR_Luv2BGR`
`static int` | `COLOR_Luv2LBGR`
`static int` | `COLOR_Luv2LRGB`
`static int` | `COLOR_Luv2RGB`
`static int` | `COLOR_mRGBA2RGBA`
`static int` | `COLOR_RGB2BGR`
`static int` | `COLOR_RGB2BGR555`
`static int` | `COLOR_RGB2BGR565`
`static int` | `COLOR_RGB2BGRA`
`static int` | `COLOR_RGB2GRAY`
`static int` | `COLOR_RGB2HLS`
`static int` | `COLOR_RGB2HLS_FULL`
`static int` | `COLOR_RGB2HSV`
`static int` | `COLOR_RGB2HSV_FULL`
`static int` | `COLOR_RGB2Lab`
`static int` | `COLOR_RGB2Luv`
`static int` | `COLOR_RGB2RGBA`
`static int` | `COLOR_RGB2XYZ`
`static int` | `COLOR_RGB2YCrCb`
`static int` | `COLOR_RGB2YUV`
`static int` | `COLOR_RGBA2BGR`
`static int` | `COLOR_RGBA2BGR555`
`static int` | `COLOR_RGBA2BGR565`
`static int` | `COLOR_RGBA2BGRA`
`static int` | `COLOR_RGBA2GRAY`
`static int` | `COLOR_RGBA2mRGBA`
`static int` | `COLOR_RGBA2RGB`
`static int` | `COLOR_XYZ2BGR`
`static int` | `COLOR_XYZ2RGB`
`static int` | `COLOR_YCrCb2BGR`
`static int` | `COLOR_YCrCb2RGB`
`static int` | `COLOR_YUV2BGR`
`static int` | `COLOR_YUV2BGR_I420`
`static int` | `COLOR_YUV2BGR_IYUV`
`static int` | `COLOR_YUV2BGR_NV12`
`static int` | `COLOR_YUV2BGR_NV21`
`static int` | `COLOR_YUV2BGR_UYNV`
`static int` | `COLOR_YUV2BGR_UYVY`
`static int` | `COLOR_YUV2BGR_Y422`
`static int` | `COLOR_YUV2BGR_YUNV`
`static int` | `COLOR_YUV2BGR_YUY2`
`static int` | `COLOR_YUV2BGR_YUYV`
`static int` | `COLOR_YUV2BGR_YV12`
`static int` | `COLOR_YUV2BGR_YVYU`
`static int` | `COLOR_YUV2BGRA_I420`
`static int` | `COLOR_YUV2BGRA_IYUV`
`static int` | `COLOR_YUV2BGRA_NV12`
`static int` | `COLOR_YUV2BGRA_NV21`
`static int` | `COLOR_YUV2BGRA_UYNV`
`static int` | `COLOR_YUV2BGRA_UYVY`
`static int` | `COLOR_YUV2BGRA_Y422`
`static int` | `COLOR_YUV2BGRA_YUNV`
`static int` | `COLOR_YUV2BGRA_YUY2`
`static int` | `COLOR_YUV2BGRA_YUYV`
`static int` | `COLOR_YUV2BGRA_YV12`
`static int` | `COLOR_YUV2BGRA_YVYU`
`static int` | `COLOR_YUV2GRAY_420`
`static int` | `COLOR_YUV2GRAY_I420`
`static int` | `COLOR_YUV2GRAY_IYUV`
`static int` | `COLOR_YUV2GRAY_NV12`
`static int` | `COLOR_YUV2GRAY_NV21`
`static int` | `COLOR_YUV2GRAY_UYNV`
`static int` | `COLOR_YUV2GRAY_UYVY`
`static int` | `COLOR_YUV2GRAY_Y422`
`static int` | `COLOR_YUV2GRAY_YUNV`
`static int` | `COLOR_YUV2GRAY_YUY2`
`static int` | `COLOR_YUV2GRAY_YUYV`
`static int` | `COLOR_YUV2GRAY_YV12`
`static int` | `COLOR_YUV2GRAY_YVYU`
`static int` | `COLOR_YUV2RGB`
`static int` | `COLOR_YUV2RGB_I420`
`static int` | `COLOR_YUV2RGB_IYUV`
`static int` | `COLOR_YUV2RGB_NV12`
`static int` | `COLOR_YUV2RGB_NV21`
`static int` | `COLOR_YUV2RGB_UYNV`
`static int` | `COLOR_YUV2RGB_UYVY`
`static int` | `COLOR_YUV2RGB_Y422`
`static int` | `COLOR_YUV2RGB_YUNV`
`static int` | `COLOR_YUV2RGB_YUY2`
`static int` | `COLOR_YUV2RGB_YUYV`
`static int` | `COLOR_YUV2RGB_YV12`
`static int` | `COLOR_YUV2RGB_YVYU`
`static int` | `COLOR_YUV2RGBA_I420`
`static int` | `COLOR_YUV2RGBA_IYUV`
`static int` | `COLOR_YUV2RGBA_NV12`
`static int` | `COLOR_YUV2RGBA_NV21`
`static int` | `COLOR_YUV2RGBA_UYNV`
`static int` | `COLOR_YUV2RGBA_UYVY`
`static int` | `COLOR_YUV2RGBA_Y422`
`static int` | `COLOR_YUV2RGBA_YUNV`
`static int` | `COLOR_YUV2RGBA_YUY2`
`static int` | `COLOR_YUV2RGBA_YUYV`
`static int` | `COLOR_YUV2RGBA_YV12`
`static int` | `COLOR_YUV2RGBA_YVYU`
`static int` | `COLOR_YUV420p2BGR`
`static int` | `COLOR_YUV420p2BGRA`
`static int` | `COLOR_YUV420p2GRAY`
`static int` | `COLOR_YUV420p2RGB`
`static int` | `COLOR_YUV420p2RGBA`
`static int` | `COLOR_YUV420sp2BGR`
`static int` | `COLOR_YUV420sp2BGRA`
`static int` | `COLOR_YUV420sp2GRAY`
`static int` | `COLOR_YUV420sp2RGB`
`static int` | `COLOR_YUV420sp2RGBA`
`static int` | `CV_BILATERAL`
`static int` | `CV_BLUR`
`static int` | `CV_BLUR_NO_SCALE`
`static int` | `CV_CANNY_L2_GRADIENT`
`static int` | `CV_CHAIN_CODE`
`static int` | `CV_CLOCKWISE`
`static int` | `CV_COMP_BHATTACHARYYA`
`static int` | `CV_COMP_CHISQR`
`static int` | `CV_COMP_CORREL`
`static int` | `CV_COMP_HELLINGER`
`static int` | `CV_COMP_INTERSECT`
`static int` | `CV_CONTOURS_MATCH_I1`
`static int` | `CV_CONTOURS_MATCH_I2`
`static int` | `CV_CONTOURS_MATCH_I3`
`static int` | `CV_COUNTER_CLOCKWISE`
`static int` | `CV_DIST_C`
`static int` | `CV_DIST_FAIR`
`static int` | `CV_DIST_HUBER`
`static int` | `CV_DIST_L1`
`static int` | `CV_DIST_L12`
`static int` | `CV_DIST_L2`
`static int` | `CV_DIST_LABEL_CCOMP`
`static int` | `CV_DIST_LABEL_PIXEL`
`static int` | `CV_DIST_MASK_3`
`static int` | `CV_DIST_MASK_5`
`static int` | `CV_DIST_MASK_PRECISE`
`static int` | `CV_DIST_USER`
`static int` | `CV_DIST_WELSCH`
`static int` | `CV_GAUSSIAN`
`static int` | `CV_GAUSSIAN_5x5`
`static int` | `CV_HOUGH_GRADIENT`
`static int` | `CV_HOUGH_MULTI_SCALE`
`static int` | `CV_HOUGH_PROBABILISTIC`
`static int` | `CV_HOUGH_STANDARD`
`static int` | `CV_LINK_RUNS`
`static int` | `CV_MAX_SOBEL_KSIZE`
`static int` | `CV_MEDIAN`
`static int` | `CV_mRGBA2RGBA`
`static int` | `CV_POLY_APPROX_DP`
`static int` | `CV_RGBA2mRGBA`
`static int` | `CV_SCHARR`
`static int` | `CV_SHAPE_CROSS`
`static int` | `CV_SHAPE_CUSTOM`
`static int` | `CV_SHAPE_ELLIPSE`
`static int` | `CV_SHAPE_RECT`
`static int` | `CV_WARP_FILL_OUTLIERS`
`static int` | `CV_WARP_INVERSE_MAP`
`static int` | `DIST_LABEL_CCOMP`
`static int` | `DIST_LABEL_PIXEL`
`static int` | `FLOODFILL_FIXED_RANGE`
`static int` | `FLOODFILL_MASK_ONLY`
`static int` | `GC_BGD`
`static int` | `GC_EVAL`
`static int` | `GC_FGD`
`static int` | `GC_INIT_WITH_MASK`
`static int` | `GC_INIT_WITH_RECT`
`static int` | `GC_PR_BGD`
`static int` | `GC_PR_FGD`
`static int` | `GHT_POSITION`
`static int` | `GHT_ROTATION`
`static int` | `GHT_SCALE`
`static int` | `INTER_AREA`
`static int` | `INTER_BITS`
`static int` | `INTER_BITS2`
`static int` | `INTER_CUBIC`
`static int` | `INTER_LANCZOS4`
`static int` | `INTER_LINEAR`
`static int` | `INTER_MAX`
`static int` | `INTER_NEAREST`
`static int` | `INTER_TAB_SIZE`
`static int` | `INTER_TAB_SIZE2`
`static int` | `KERNEL_ASYMMETRICAL`
`static int` | `KERNEL_GENERAL`
`static int` | `KERNEL_INTEGER`
`static int` | `KERNEL_SMOOTH`
`static int` | `KERNEL_SYMMETRICAL`
`static int` | `MORPH_BLACKHAT`
`static int` | `MORPH_CLOSE`
`static int` | `MORPH_CROSS`
`static int` | `MORPH_DILATE`
`static int` | `MORPH_ELLIPSE`
`static int` | `MORPH_ERODE`
`static int` | `MORPH_GRADIENT`
`static int` | `MORPH_OPEN`
`static int` | `MORPH_RECT`
`static int` | `MORPH_TOPHAT`
`static int` | `PROJ_SPHERICAL_EQRECT`
`static int` | `PROJ_SPHERICAL_ORTHO`
`static int` | `RETR_CCOMP`
`static int` | `RETR_EXTERNAL`
`static int` | `RETR_FLOODFILL`
`static int` | `RETR_LIST`
`static int` | `RETR_TREE`
`static int` | `THRESH_BINARY`
`static int` | `THRESH_BINARY_INV`
`static int` | `THRESH_MASK`
`static int` | `THRESH_OTSU`
`static int` | `THRESH_TOZERO`
`static int` | `THRESH_TOZERO_INV`
`static int` | `THRESH_TRUNC`
`static int` | `TM_CCOEFF`
`static int` | `TM_CCOEFF_NORMED`
`static int` | `TM_CCORR`
`static int` | `TM_CCORR_NORMED`
`static int` | `TM_SQDIFF`
`static int` | `TM_SQDIFF_NORMED`
`static int` | `WARP_INVERSE_MAP`

Constructor Summary
`Imgproc()`

Method Summary
`static void` | `accumulate(Mat src, Mat dst)` Adds an image to the accumulator.
`static void` | `accumulate(Mat src, Mat dst, Mat mask)` Adds an image to the accumulator.
`static void` | `accumulateProduct(Mat src1, Mat src2, Mat dst)` Adds the per-element product of two input images to the accumulator.
`static void` | `accumulateProduct(Mat src1, Mat src2, Mat dst, Mat mask)` Adds the per-element product of two input images to the accumulator.
`static void` | `accumulateSquare(Mat src, Mat dst)` Adds the square of a source image to the accumulator.
`static void` | `accumulateSquare(Mat src, Mat dst, Mat mask)` Adds the square of a source image to the accumulator.
`static void` | `accumulateWeighted(Mat src, Mat dst, double alpha)` Updates a running average.
`static void` | `accumulateWeighted(Mat src, Mat dst, double alpha, Mat mask)` Updates a running average.
`static void` | `adaptiveThreshold(Mat src, Mat dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C)` Applies an adaptive threshold to an array.
`static void` | `approxPolyDP(MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, boolean closed)` Approximates a polygonal curve(s) with the specified precision.
`static double` | `arcLength(MatOfPoint2f curve, boolean closed)` Calculates a contour perimeter or a curve length.
`static void` | `bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace)` Applies the bilateral filter to an image.
`static void` | `bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType)` Applies the bilateral filter to an image.
`static void` | `blur(Mat src, Mat dst, Size ksize)` Blurs an image using the normalized box filter.
`static void` | `blur(Mat src, Mat dst, Size ksize, Point anchor)` Blurs an image using the normalized box filter.
`static void` | `blur(Mat src, Mat dst, Size ksize, Point anchor, int borderType)` Blurs an image using the normalized box filter.
`static int` | `borderInterpolate(int p, int len, int borderType)` Computes the source location of an extrapolated pixel.
`static Rect` | `boundingRect(MatOfPoint points)` Calculates the up-right bounding rectangle of a point set.
`static void` | `boxFilter(Mat src, Mat dst, int ddepth, Size ksize)` Blurs an image using the box filter.
`static void` | `boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize)` Blurs an image using the box filter.
`static void` | `boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize, int borderType)` Blurs an image using the box filter.
`static void` | `calcBackProject(java.util.List<Mat> images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale)` Calculates the back projection of a histogram.
`static void` | `calcHist(java.util.List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges)` Calculates a histogram of a set of arrays.
`static void` | `calcHist(java.util.List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, boolean accumulate)` Calculates a histogram of a set of arrays.
`static void` | `Canny(Mat image, Mat edges, double threshold1, double threshold2)` Finds edges in an image using the [Canny86] algorithm.
`static void` | `Canny(Mat image, Mat edges, double threshold1, double threshold2, int apertureSize, boolean L2gradient)` Finds edges in an image using the [Canny86] algorithm.
`static double` | `compareHist(Mat H1, Mat H2, int method)` Compares two histograms.
`static double` | `contourArea(Mat contour)` Calculates a contour area.
`static double` | `contourArea(Mat contour, boolean oriented)` Calculates a contour area.
`static void` | `convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type)` Converts image transformation maps from one representation to another.
`static void` | `convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, boolean nninterpolation)` Converts image transformation maps from one representation to another.
`static void` | `convexHull(MatOfPoint points, MatOfInt hull)` Finds the convex hull of a point set.
`static void` | `convexHull(MatOfPoint points, MatOfInt hull, boolean clockwise)` Finds the convex hull of a point set.
`static void` | `convexityDefects(MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects)` Finds the convexity defects of a contour.
`static void` | `copyMakeBorder(Mat src, Mat dst, int top, int bottom, int left, int right, int borderType)` Forms a border around an image.
`static void` | `copyMakeBorder(Mat src, Mat dst, int top, int bottom, int left, int right, int borderType, Scalar value)` Forms a border around an image.
`static void` | `cornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize)` Calculates eigenvalues and eigenvectors of image blocks for corner detection.
`static void` | `cornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize, int borderType)` Calculates eigenvalues and eigenvectors of image blocks for corner detection.
`static void` | `cornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k)` Harris edge detector.
`static void` | `cornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k, int borderType)` Harris edge detector.
`static void` | `cornerMinEigenVal(Mat src, Mat dst, int blockSize)` Calculates the minimal eigenvalue of gradient matrices for corner detection.
`static void` | `cornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize)` Calculates the minimal eigenvalue of gradient matrices for corner detection.
`static void` | `cornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize, int borderType)` Calculates the minimal eigenvalue of gradient matrices for corner detection.
`static void` | `cornerSubPix(Mat image, MatOfPoint2f corners, Size winSize, Size zeroZone, TermCriteria criteria)` Refines the corner locations.
`static void` | `createHanningWindow(Mat dst, Size winSize, int type)` This function computes a Hanning window coefficients in two dimensions.
`static void` | `cvtColor(Mat src, Mat dst, int code)` Converts an image from one color space to another.
`static void` | `cvtColor(Mat src, Mat dst, int code, int dstCn)` Converts an image from one color space to another.
`static void` | `dilate(Mat src, Mat dst, Mat kernel)` Dilates an image by using a specific structuring element.
`static void` | `dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)` Dilates an image by using a specific structuring element.
`static void` | `dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)` Dilates an image by using a specific structuring element.
`static void` | `distanceTransform(Mat src, Mat dst, int distanceType, int maskSize)` Calculates the distance to the closest zero pixel for each pixel of the source image.
`static void` | `distanceTransformWithLabels(Mat src, Mat dst, Mat labels, int distanceType, int maskSize)` Calculates the distance to the closest zero pixel for each pixel of the source image.
`static void` | `distanceTransformWithLabels(Mat src, Mat dst, Mat labels, int distanceType, int maskSize, int labelType)` Calculates the distance to the closest zero pixel for each pixel of the source image.
`static void` | `drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color)` Draws contours outlines or filled contours.
`static void` | `drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness)` Draws contours outlines or filled contours.
`static void` | `drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel, Point offset)` Draws contours outlines or filled contours.
`static void` | `equalizeHist(Mat src, Mat dst)` Equalizes the histogram of a grayscale image.
`static void` | `erode(Mat src, Mat dst, Mat kernel)` Erodes an image by using a specific structuring element.
`static void` | `erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)` Erodes an image by using a specific structuring element.
`static void` | `erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)` Erodes an image by using a specific structuring element.
`static void` | `filter2D(Mat src, Mat dst, int ddepth, Mat kernel)` Convolves an image with the kernel.
`static void` | `filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta)` Convolves an image with the kernel.
`static void` | `filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta, int borderType)` Convolves an image with the kernel.
`static void` | `findContours(Mat image, java.util.List<MatOfPoint> contours, Mat hierarchy, int mode, int method)` Finds contours in a binary image.
`static void` | `findContours(Mat image, java.util.List<MatOfPoint> contours, Mat hierarchy, int mode, int method, Point offset)` Finds contours in a binary image.
`static RotatedRect` | `fitEllipse(MatOfPoint2f points)` Fits an ellipse around a set of 2D points.
`static void` | `fitLine(Mat points, Mat line, int distType, double param, double reps, double aeps)` Fits a line to a 2D or 3D point set.
`static int` | `floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal)` Fills a connected component with the given color.
`static int` | `floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff, int flags)` Fills a connected component with the given color.
`static void` | `GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX)` Blurs an image using a Gaussian filter.
`static void` | `GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY)` Blurs an image using a Gaussian filter.
`static void` | `GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY, int borderType)` Blurs an image using a Gaussian filter.
`static Mat` | `getAffineTransform(MatOfPoint2f src, MatOfPoint2f dst)` Calculates an affine transform from three pairs of the corresponding points.
`static Mat` | `getDefaultNewCameraMatrix(Mat cameraMatrix)` Returns the default new camera matrix.
`static Mat` | `getDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize, boolean centerPrincipalPoint)` Returns the default new camera matrix.
`static void` | `getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize)` Returns filter coefficients for computing spatial image derivatives.
`static void` | `getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, boolean normalize, int ktype)` Returns filter coefficients for computing spatial image derivatives.
`static Mat` | `getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma)`
`static Mat` | `getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype)`
`static Mat` | `getGaussianKernel(int ksize, double sigma)` Returns Gaussian filter coefficients.
`static Mat` | `getGaussianKernel(int ksize, double sigma, int ktype)` Returns Gaussian filter coefficients.
`static Mat` | `getPerspectiveTransform(Mat src, Mat dst)` Calculates a perspective transform from four pairs of the corresponding points.
`static void` | `getRectSubPix(Mat image, Size patchSize, Point center, Mat patch)` Retrieves a pixel rectangle from an image with sub-pixel accuracy.
`static void` | `getRectSubPix(Mat image, Size patchSize, Point center, Mat patch, int patchType)` Retrieves a pixel rectangle from an image with sub-pixel accuracy.
`static Mat` | `getRotationMatrix2D(Point center, double angle, double scale)` Calculates an affine matrix of 2D rotation.
`static Mat` | `getStructuringElement(int shape, Size ksize)` Returns a structuring element of the specified size and shape for morphological operations.
`static Mat` | `getStructuringElement(int shape, Size ksize, Point anchor)` Returns a structuring element of the specified size and shape for morphological operations.
`static void` | `goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance)` Determines strong corners on an image.
`static void` | `goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, boolean useHarrisDetector, double k)` Determines strong corners on an image.
`static void` | `grabCut(Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount)` Runs the GrabCut algorithm.
`static void` | `grabCut(Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode)` Runs the GrabCut algorithm.
`static void` | `HoughCircles(Mat image, Mat circles, int method, double dp, double minDist)` Finds circles in a grayscale image using the Hough transform.
`static void` | `HoughCircles(Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius)` Finds circles in a grayscale image using the Hough transform.
`static void` | `HoughLines(Mat image, Mat lines, double rho, double theta, int threshold)` Finds lines in a binary image using the standard Hough transform.
`static void` | `HoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn)` Finds lines in a binary image using the standard Hough transform.
`static void` | `HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold)` Finds line segments in a binary image using the probabilistic Hough transform.
`static void` | `HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength, double maxLineGap)` Finds line segments in a binary image using the probabilistic Hough transform.
`static void` | `HuMoments(Moments m, Mat hu)` Calculates seven Hu invariants.
`static void` | `initUndistortRectifyMap(Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2)` Computes the undistortion and rectification transformation map.
`static float` | `initWideAngleProjMap(Mat cameraMatrix, Mat distCoeffs, Size imageSize, int destImageWidth, int m1type, Mat map1, Mat map2)`
`static float` | `initWideAngleProjMap(Mat cameraMatrix, Mat distCoeffs, Size imageSize, int destImageWidth, int m1type, Mat map1, Mat map2, int projType, double alpha)`
`static void` | `integral(Mat src, Mat sum)` Calculates the integral of an image.
`static void` | `integral(Mat src, Mat sum, int sdepth)` Calculates the integral of an image.
`static void` | `integral2(Mat src, Mat sum, Mat sqsum)` Calculates the integral of an image.
`static void` | `integral2(Mat src, Mat sum, Mat sqsum, int sdepth)` Calculates the integral of an image.
`static void` | `integral3(Mat src, Mat sum, Mat sqsum, Mat tilted)` Calculates the integral of an image.
`static void` | `integral3(Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth)` Calculates the integral of an image.
`static float` | `intersectConvexConvex(Mat _p1, Mat _p2, Mat _p12)`
`static float` | `intersectConvexConvex(Mat _p1, Mat _p2, Mat _p12, boolean handleNested)`
`static void` | `invertAffineTransform(Mat M, Mat iM)` Inverts an affine transformation.
`static boolean` | `isContourConvex(MatOfPoint contour)` Tests a contour convexity.
`static void` | `Laplacian(Mat src, Mat dst, int ddepth)` Calculates the Laplacian of an image.
`static void` | `Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale, double delta)` Calculates the Laplacian of an image.
`static void` | `Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale, double delta, int borderType)` Calculates the Laplacian of an image.
`static double` | `matchShapes(Mat contour1, Mat contour2, int method, double parameter)` Compares two shapes.
`static void` | `matchTemplate(Mat image, Mat templ, Mat result, int method)` Compares a template against overlapped image regions.
`static void` | `medianBlur(Mat src, Mat dst, int ksize)` Blurs an image using the median filter.
`static RotatedRect` | `minAreaRect(MatOfPoint2f points)` Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
`static void` | `minEnclosingCircle(MatOfPoint2f points, Point center, float[] radius)` Finds a circle of the minimum area enclosing a 2D point set.
`static Moments` | `moments(Mat array)` Calculates all of the moments up to the third order of a polygon or rasterized shape.
`static Moments` | `moments(Mat array, boolean binaryImage)` Calculates all of the moments up to the third order of a polygon or rasterized shape.
`static void` | `morphologyEx(Mat src, Mat dst, int op, Mat kernel)` Performs advanced morphological transformations.
`static void` | `morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations)` Performs advanced morphological transformations.
`static void` | `morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)` Performs advanced morphological transformations.
`static Point` | `phaseCorrelate(Mat src1, Mat src2)` The function is used to detect translational shifts that occur between two images.
`static Point` | `phaseCorrelate(Mat src1, Mat src2, Mat window)` The function is used to detect translational shifts that occur between two images.
`static Point` | `phaseCorrelateRes(Mat src1, Mat src2, Mat window)`
`static Point` | `phaseCorrelateRes(Mat src1, Mat src2, Mat window, double[] response)`
`static double` | `pointPolygonTest(MatOfPoint2f contour, Point pt, boolean measureDist)` Performs a point-in-contour test.
`static void` | `preCornerDetect(Mat src, Mat dst, int ksize)` Calculates a feature map for corner detection.
`static void` | `preCornerDetect(Mat src, Mat dst, int ksize, int borderType)` Calculates a feature map for corner detection.
`static double` | `PSNR(Mat src1, Mat src2)`
`static void` | `pyrDown(Mat src, Mat dst)` Blurs an image and downsamples it.
`static void` | `pyrDown(Mat src, Mat dst, Size dstsize)` Blurs an image and downsamples it.
`static void` | `pyrDown(Mat src, Mat dst, Size dstsize, int borderType)` Blurs an image and downsamples it.
`static void` | `pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr)` Performs initial step of meanshift segmentation of an image.
`static void` | `pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria termcrit)` Performs initial step of meanshift segmentation of an image.
`static void` | `pyrUp(Mat src, Mat dst)` Upsamples an image and then blurs it.
`static void` | `pyrUp(Mat src, Mat dst, Size dstsize)` Upsamples an image and then blurs it.
`static void` | `pyrUp(Mat src, Mat dst, Size dstsize, int borderType)` Upsamples an image and then blurs it.
`static void` | `remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation)` Applies a generic geometrical transformation to an image.
`static void` | `remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, Scalar borderValue)` Applies a generic geometrical transformation to an image.
`static void` | `resize(Mat src, Mat dst, Size dsize)` Resizes an image.
`static void` | `resize(Mat src, Mat dst, Size dsize, double fx, double fy, int interpolation)` Resizes an image.
`static void` | `Scharr(Mat src, Mat dst, int ddepth, int dx, int dy)` Calculates the first x- or y- image derivative using Scharr operator.
`static void` | `Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta)` Calculates the first x- or y- image derivative using Scharr operator.
`static void` | `Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta, int borderType)` Calculates the first x- or y- image derivative using Scharr operator.
`static void` | `sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY)` Applies a separable linear filter to an image.
`static void` | `sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta)` Applies a separable linear filter to an image.
`static void` | `sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta, int borderType)` Applies a separable linear filter to an image.
`static void` | `Sobel(Mat src, Mat dst, int ddepth, int dx, int dy)` Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
`static void` | `Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta)` Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
`static void` | `Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType)` Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
`static double` | `threshold(Mat src, Mat dst, double thresh, double maxval, int type)` Applies a fixed-level threshold to each array element.
`static void` | `undistort(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)` Transforms an image to compensate for lens distortion.
`static void` | `undistort(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix)` Transforms an image to compensate for lens distortion.
`static void` | `undistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs)` Computes the ideal point coordinates from the observed point coordinates.
`static void` | `undistortPoints(MatOfPoint2f src, MatOfPoint2f dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P)` Computes the ideal point coordinates from the observed point coordinates.
`static void` | `warpAffine(Mat src, Mat dst, Mat M, Size dsize)` Applies an affine transformation to an image.
`static void` | `warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags)` Applies an affine transformation to an image.
`static void` | `warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)` Applies an affine transformation to an image.
`static void` | `warpPerspective(Mat src, Mat dst, Mat M, Size dsize)` Applies a perspective transformation to an image.
`static void` | `warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags)` Applies a perspective transformation to an image.
`static void` | `warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)` Applies a perspective transformation to an image.
`static void` | `watershed(Mat image, Mat markers)` Performs a marker-based image segmentation using the watershed algorithm.

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Field Detail

### ADAPTIVE_THRESH_GAUSSIAN_C

    public static final int ADAPTIVE_THRESH_GAUSSIAN_C

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### ADAPTIVE_THRESH_MEAN_C

    public static final int ADAPTIVE_THRESH_MEAN_C

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### BORDER_CONSTANT

    public static final int BORDER_CONSTANT

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### BORDER_DEFAULT

    public static final int BORDER_DEFAULT

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### BORDER_ISOLATED

    public static final int BORDER_ISOLATED

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### BORDER_REFLECT

    public static final int BORDER_REFLECT

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### BORDER_REFLECT_101

    public static final int BORDER_REFLECT_101

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### BORDER_REFLECT101

    public static final int BORDER_REFLECT101

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### BORDER_REPLICATE

    public static final int BORDER_REPLICATE

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### BORDER_TRANSPARENT

    public static final int BORDER_TRANSPARENT

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### BORDER_WRAP

    public static final int BORDER_WRAP

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### CHAIN_APPROX_NONE

    public static final int CHAIN_APPROX_NONE

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### CHAIN_APPROX_SIMPLE

    public static final int CHAIN_APPROX_SIMPLE

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### CHAIN_APPROX_TC89_KCOS

    public static final int CHAIN_APPROX_TC89_KCOS

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### CHAIN_APPROX_TC89_L1

    public static final int CHAIN_APPROX_TC89_L1

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### COLOR_BayerBG2BGR

    public static final int COLOR_BayerBG2BGR

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### COLOR_BayerBG2BGR_VNG

    public static final int COLOR_BayerBG2BGR_VNG

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### COLOR_BayerBG2GRAY

    public static final int COLOR_BayerBG2GRAY

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### COLOR_BayerBG2RGB

    public static final int COLOR_BayerBG2RGB

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### COLOR_BayerBG2RGB_VNG

    public static final int COLOR_BayerBG2RGB_VNG

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### COLOR_BayerGB2BGR

    public static final int COLOR_BayerGB2BGR

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### COLOR_BayerGB2BGR_VNG

    public static final int COLOR_BayerGB2BGR_VNG

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### COLOR_BayerGB2GRAY

    public static final int COLOR_BayerGB2GRAY

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### COLOR_BayerGB2RGB

    public static final int COLOR_BayerGB2RGB

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### COLOR_BayerGB2RGB_VNG

    public static final int COLOR_BayerGB2RGB_VNG

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### COLOR_BayerGR2BGR

    public static final int COLOR_BayerGR2BGR

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### COLOR_BayerGR2BGR_VNG

    public static final int COLOR_BayerGR2BGR_VNG

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### COLOR_BayerGR2GRAY

    public static final int COLOR_BayerGR2GRAY

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### COLOR_BayerGR2RGB

    public static final int COLOR_BayerGR2RGB

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### COLOR_BayerGR2RGB_VNG

    public static final int COLOR_BayerGR2RGB_VNG

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### COLOR_BayerRG2BGR

    public static final int COLOR_BayerRG2BGR

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### COLOR_BayerRG2BGR_VNG

    public static final int COLOR_BayerRG2BGR_VNG

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### COLOR_BayerRG2GRAY

    public static final int COLOR_BayerRG2GRAY

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### COLOR_BayerRG2RGB

    public static final int COLOR_BayerRG2RGB

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### COLOR_BayerRG2RGB_VNG

    public static final int COLOR_BayerRG2RGB_VNG

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### COLOR_BGR2BGR555

    public static final int COLOR_BGR2BGR555

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### COLOR_BGR2BGR565

    public static final int COLOR_BGR2BGR565

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### COLOR_BGR2BGRA

    public static final int COLOR_BGR2BGRA

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### COLOR_BGR2GRAY

    public static final int COLOR_BGR2GRAY

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### COLOR_BGR2HLS

    public static final int COLOR_BGR2HLS

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### COLOR_BGR2HLS_FULL

    public static final int COLOR_BGR2HLS_FULL

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### COLOR_BGR2HSV

    public static final int COLOR_BGR2HSV

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### COLOR_BGR2HSV_FULL

    public static final int COLOR_BGR2HSV_FULL

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### COLOR_BGR2Lab

    public static final int COLOR_BGR2Lab

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### COLOR_BGR2Luv

    public static final int COLOR_BGR2Luv

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### COLOR_BGR2RGB

    public static final int COLOR_BGR2RGB

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### COLOR_BGR2RGBA

    public static final int COLOR_BGR2RGBA

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### COLOR_BGR2XYZ

    public static final int COLOR_BGR2XYZ

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### COLOR_BGR2YCrCb

    public static final int COLOR_BGR2YCrCb

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### COLOR_BGR2YUV

    public static final int COLOR_BGR2YUV

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### COLOR_BGR5552BGR

    public static final int COLOR_BGR5552BGR

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### COLOR_BGR5552BGRA

    public static final int COLOR_BGR5552BGRA

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### COLOR_BGR5552GRAY

    public static final int COLOR_BGR5552GRAY

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### COLOR_BGR5552RGB

    public static final int COLOR_BGR5552RGB

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### COLOR_BGR5552RGBA

    public static final int COLOR_BGR5552RGBA

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### COLOR_BGR5652BGR

    public static final int COLOR_BGR5652BGR

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### COLOR_BGR5652BGRA

    public static final int COLOR_BGR5652BGRA

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### COLOR_BGR5652GRAY

    public static final int COLOR_BGR5652GRAY

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### COLOR_BGR5652RGB

    public static final int COLOR_BGR5652RGB

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### COLOR_BGR5652RGBA

    public static final int COLOR_BGR5652RGBA

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### COLOR_BGRA2BGR

    public static final int COLOR_BGRA2BGR

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### COLOR_BGRA2BGR555

    public static final int COLOR_BGRA2BGR555

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### COLOR_BGRA2BGR565

    public static final int COLOR_BGRA2BGR565

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### COLOR_BGRA2GRAY

    public static final int COLOR_BGRA2GRAY

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### COLOR_BGRA2RGB

    public static final int COLOR_BGRA2RGB

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### COLOR_BGRA2RGBA

    public static final int COLOR_BGRA2RGBA

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### COLOR_COLORCVT_MAX

    public static final int COLOR_COLORCVT_MAX

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### COLOR_GRAY2BGR

    public static final int COLOR_GRAY2BGR

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### COLOR_GRAY2BGR555

    public static final int COLOR_GRAY2BGR555

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### COLOR_GRAY2BGR565

    public static final int COLOR_GRAY2BGR565

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### COLOR_GRAY2BGRA

    public static final int COLOR_GRAY2BGRA

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### COLOR_GRAY2RGB

    public static final int COLOR_GRAY2RGB

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### COLOR_GRAY2RGBA

    public static final int COLOR_GRAY2RGBA

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### COLOR_HLS2BGR

    public static final int COLOR_HLS2BGR

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### COLOR_HLS2BGR_FULL

    public static final int COLOR_HLS2BGR_FULL

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### COLOR_HLS2RGB

    public static final int COLOR_HLS2RGB

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### COLOR_HLS2RGB_FULL

    public static final int COLOR_HLS2RGB_FULL

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### COLOR_HSV2BGR

    public static final int COLOR_HSV2BGR

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### COLOR_HSV2BGR_FULL

    public static final int COLOR_HSV2BGR_FULL

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### COLOR_HSV2RGB

    public static final int COLOR_HSV2RGB

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### COLOR_HSV2RGB_FULL

    public static final int COLOR_HSV2RGB_FULL

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### COLOR_Lab2BGR

    public static final int COLOR_Lab2BGR

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### COLOR_Lab2LBGR

    public static final int COLOR_Lab2LBGR

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### COLOR_Lab2LRGB

    public static final int COLOR_Lab2LRGB

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### COLOR_Lab2RGB

    public static final int COLOR_Lab2RGB

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### COLOR_LBGR2Lab

    public static final int COLOR_LBGR2Lab

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### COLOR_LBGR2Luv

    public static final int COLOR_LBGR2Luv

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### COLOR_LRGB2Lab

    public static final int COLOR_LRGB2Lab

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### COLOR_LRGB2Luv

    public static final int COLOR_LRGB2Luv

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### COLOR_Luv2BGR

    public static final int COLOR_Luv2BGR

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### COLOR_Luv2LBGR

    public static final int COLOR_Luv2LBGR

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### COLOR_Luv2LRGB

    public static final int COLOR_Luv2LRGB

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### COLOR_Luv2RGB

    public static final int COLOR_Luv2RGB

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### COLOR_mRGBA2RGBA

    public static final int COLOR_mRGBA2RGBA

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### COLOR_RGB2BGR

    public static final int COLOR_RGB2BGR

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### COLOR_RGB2BGR555

    public static final int COLOR_RGB2BGR555

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### COLOR_RGB2BGR565

    public static final int COLOR_RGB2BGR565

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### COLOR_RGB2BGRA

    public static final int COLOR_RGB2BGRA

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### COLOR_RGB2GRAY

    public static final int COLOR_RGB2GRAY

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### COLOR_RGB2HLS

    public static final int COLOR_RGB2HLS

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### COLOR_RGB2HLS_FULL

    public static final int COLOR_RGB2HLS_FULL

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### COLOR_RGB2HSV

    public static final int COLOR_RGB2HSV

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### COLOR_RGB2HSV_FULL

    public static final int COLOR_RGB2HSV_FULL

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### COLOR_RGB2Lab

    public static final int COLOR_RGB2Lab

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### COLOR_RGB2Luv

    public static final int COLOR_RGB2Luv

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### COLOR_RGB2RGBA

    public static final int COLOR_RGB2RGBA

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### COLOR_RGB2XYZ

    public static final int COLOR_RGB2XYZ

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### COLOR_RGB2YCrCb

    public static final int COLOR_RGB2YCrCb

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### COLOR_RGB2YUV

    public static final int COLOR_RGB2YUV

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### COLOR_RGBA2BGR

    public static final int COLOR_RGBA2BGR

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### COLOR_RGBA2BGR555

    public static final int COLOR_RGBA2BGR555

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### COLOR_RGBA2BGR565

    public static final int COLOR_RGBA2BGR565

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### COLOR_RGBA2BGRA

    public static final int COLOR_RGBA2BGRA

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### COLOR_RGBA2GRAY

    public static final int COLOR_RGBA2GRAY

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  Constant Field Values

### COLOR_RGBA2mRGBA

    public static final int COLOR_RGBA2mRGBA

See Also:
  Constant Field Values

### COLOR_RGBA2RGB

    public static final int COLOR_RGBA2RGB

See Also:
  Constant Field Values

### COLOR_XYZ2BGR

    public static final int COLOR_XYZ2BGR

See Also:
  Constant Field Values

### COLOR_XYZ2RGB

    public static final int COLOR_XYZ2RGB

See Also:
  Constant Field Values

### COLOR_YCrCb2BGR

    public static final int COLOR_YCrCb2BGR

See Also:
  Constant Field Values

### COLOR_YCrCb2RGB

    public static final int COLOR_YCrCb2RGB

See Also:
  Constant Field Values

### COLOR_YUV2BGR

    public static final int COLOR_YUV2BGR

See Also:
  Constant Field Values

### COLOR_YUV2BGR_I420

    public static final int COLOR_YUV2BGR_I420

See Also:
  Constant Field Values

### COLOR_YUV2BGR_IYUV

    public static final int COLOR_YUV2BGR_IYUV

See Also:
  Constant Field Values

### COLOR_YUV2BGR_NV12

    public static final int COLOR_YUV2BGR_NV12

See Also:
  Constant Field Values

### COLOR_YUV2BGR_NV21

    public static final int COLOR_YUV2BGR_NV21

See Also:
  Constant Field Values

### COLOR_YUV2BGR_UYNV

    public static final int COLOR_YUV2BGR_UYNV

See Also:
  Constant Field Values

### COLOR_YUV2BGR_UYVY

    public static final int COLOR_YUV2BGR_UYVY

See Also:
  Constant Field Values

### COLOR_YUV2BGR_Y422

    public static final int COLOR_YUV2BGR_Y422

See Also:
  Constant Field Values

### COLOR_YUV2BGR_YUNV

    public static final int COLOR_YUV2BGR_YUNV

See Also:
  Constant Field Values

### COLOR_YUV2BGR_YUY2

    public static final int COLOR_YUV2BGR_YUY2

See Also:
  Constant Field Values

### COLOR_YUV2BGR_YUYV

    public static final int COLOR_YUV2BGR_YUYV

See Also:
  Constant Field Values

### COLOR_YUV2BGR_YV12

    public static final int COLOR_YUV2BGR_YV12

See Also:
  Constant Field Values

### COLOR_YUV2BGR_YVYU

    public static final int COLOR_YUV2BGR_YVYU

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_I420

    public static final int COLOR_YUV2BGRA_I420

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_IYUV

    public static final int COLOR_YUV2BGRA_IYUV

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_NV12

    public static final int COLOR_YUV2BGRA_NV12

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_NV21

    public static final int COLOR_YUV2BGRA_NV21

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_UYNV

    public static final int COLOR_YUV2BGRA_UYNV

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_UYVY

    public static final int COLOR_YUV2BGRA_UYVY

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_Y422

    public static final int COLOR_YUV2BGRA_Y422

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_YUNV

    public static final int COLOR_YUV2BGRA_YUNV

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_YUY2

    public static final int COLOR_YUV2BGRA_YUY2

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_YUYV

    public static final int COLOR_YUV2BGRA_YUYV

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_YV12

    public static final int COLOR_YUV2BGRA_YV12

See Also:
  Constant Field Values

### COLOR_YUV2BGRA_YVYU

    public static final int COLOR_YUV2BGRA_YVYU

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_420

    public static final int COLOR_YUV2GRAY_420

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_I420

    public static final int COLOR_YUV2GRAY_I420

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_IYUV

    public static final int COLOR_YUV2GRAY_IYUV

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_NV12

    public static final int COLOR_YUV2GRAY_NV12

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_NV21

    public static final int COLOR_YUV2GRAY_NV21

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_UYNV

    public static final int COLOR_YUV2GRAY_UYNV

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_UYVY

    public static final int COLOR_YUV2GRAY_UYVY

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_Y422

    public static final int COLOR_YUV2GRAY_Y422

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_YUNV

    public static final int COLOR_YUV2GRAY_YUNV

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_YUY2

    public static final int COLOR_YUV2GRAY_YUY2

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_YUYV

    public static final int COLOR_YUV2GRAY_YUYV

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_YV12

    public static final int COLOR_YUV2GRAY_YV12

See Also:
  Constant Field Values

### COLOR_YUV2GRAY_YVYU

    public static final int COLOR_YUV2GRAY_YVYU

See Also:
  Constant Field Values

### COLOR_YUV2RGB

    public static final int COLOR_YUV2RGB

See Also:
  Constant Field Values

### COLOR_YUV2RGB_I420

    public static final int COLOR_YUV2RGB_I420

See Also:
  Constant Field Values

### COLOR_YUV2RGB_IYUV

    public static final int COLOR_YUV2RGB_IYUV

See Also:
  Constant Field Values

### COLOR_YUV2RGB_NV12

    public static final int COLOR_YUV2RGB_NV12

See Also:
  Constant Field Values

### COLOR_YUV2RGB_NV21

    public static final int COLOR_YUV2RGB_NV21

See Also:
  Constant Field Values

### COLOR_YUV2RGB_UYNV

    public static final int COLOR_YUV2RGB_UYNV

See Also:
  Constant Field Values

### COLOR_YUV2RGB_UYVY

    public static final int COLOR_YUV2RGB_UYVY

See Also:
  Constant Field Values

### COLOR_YUV2RGB_Y422

    public static final int COLOR_YUV2RGB_Y422

See Also:
  Constant Field Values

### COLOR_YUV2RGB_YUNV

    public static final int COLOR_YUV2RGB_YUNV

See Also:
  Constant Field Values

### COLOR_YUV2RGB_YUY2

    public static final int COLOR_YUV2RGB_YUY2

See Also:
  Constant Field Values

### COLOR_YUV2RGB_YUYV

    public static final int COLOR_YUV2RGB_YUYV

See Also:
  Constant Field Values

### COLOR_YUV2RGB_YV12

    public static final int COLOR_YUV2RGB_YV12

See Also:
  Constant Field Values

### COLOR_YUV2RGB_YVYU

    public static final int COLOR_YUV2RGB_YVYU

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_I420

    public static final int COLOR_YUV2RGBA_I420

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_IYUV

    public static final int COLOR_YUV2RGBA_IYUV

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_NV12

    public static final int COLOR_YUV2RGBA_NV12

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_NV21

    public static final int COLOR_YUV2RGBA_NV21

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_UYNV

    public static final int COLOR_YUV2RGBA_UYNV

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_UYVY

    public static final int COLOR_YUV2RGBA_UYVY

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_Y422

    public static final int COLOR_YUV2RGBA_Y422

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_YUNV

    public static final int COLOR_YUV2RGBA_YUNV

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_YUY2

    public static final int COLOR_YUV2RGBA_YUY2

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_YUYV

    public static final int COLOR_YUV2RGBA_YUYV

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_YV12

    public static final int COLOR_YUV2RGBA_YV12

See Also:
  Constant Field Values

### COLOR_YUV2RGBA_YVYU

    public static final int COLOR_YUV2RGBA_YVYU

See Also:
  Constant Field Values

### COLOR_YUV420p2BGR

    public static final int COLOR_YUV420p2BGR

See Also:
  Constant Field Values

### COLOR_YUV420p2BGRA

    public static final int COLOR_YUV420p2BGRA

See Also:
  Constant Field Values

### COLOR_YUV420p2GRAY

    public static final int COLOR_YUV420p2GRAY

See Also:
  Constant Field Values

### COLOR_YUV420p2RGB

    public static final int COLOR_YUV420p2RGB

See Also:
  Constant Field Values

### COLOR_YUV420p2RGBA

    public static final int COLOR_YUV420p2RGBA

See Also:
  Constant Field Values

### COLOR_YUV420sp2BGR

    public static final int COLOR_YUV420sp2BGR

See Also:
  Constant Field Values

### COLOR_YUV420sp2BGRA

    public static final int COLOR_YUV420sp2BGRA

See Also:
  Constant Field Values

### COLOR_YUV420sp2GRAY

    public static final int COLOR_YUV420sp2GRAY

See Also:
  Constant Field Values

### COLOR_YUV420sp2RGB

    public static final int COLOR_YUV420sp2RGB

See Also:
  Constant Field Values

### COLOR_YUV420sp2RGBA

    public static final int COLOR_YUV420sp2RGBA

See Also:
  Constant Field Values

### CV_BILATERAL

    public static final int CV_BILATERAL

See Also:
  Constant Field Values

### CV_BLUR

    public static final int CV_BLUR

See Also:
  Constant Field Values

### CV_BLUR_NO_SCALE

    public static final int CV_BLUR_NO_SCALE

See Also:
  Constant Field Values

### CV_CANNY_L2_GRADIENT

    public static final int CV_CANNY_L2_GRADIENT

See Also:
  Constant Field Values

### CV_CHAIN_CODE

    public static final int CV_CHAIN_CODE

See Also:
  Constant Field Values

### CV_CLOCKWISE

    public static final int CV_CLOCKWISE

See Also:
  Constant Field Values

### CV_COMP_BHATTACHARYYA

    public static final int CV_COMP_BHATTACHARYYA

See Also:
  Constant Field Values

### CV_COMP_CHISQR

    public static final int CV_COMP_CHISQR

See Also:
  Constant Field Values

### CV_COMP_CORREL

    public static final int CV_COMP_CORREL

See Also:
  Constant Field Values

### CV_COMP_HELLINGER

    public static final int CV_COMP_HELLINGER

See Also:
  Constant Field Values

### CV_COMP_INTERSECT

    public static final int CV_COMP_INTERSECT

See Also:
  Constant Field Values

### CV_CONTOURS_MATCH_I1

    public static final int CV_CONTOURS_MATCH_I1

See Also:
  Constant Field Values

### CV_CONTOURS_MATCH_I2

    public static final int CV_CONTOURS_MATCH_I2

See Also:
  Constant Field Values

### CV_CONTOURS_MATCH_I3

    public static final int CV_CONTOURS_MATCH_I3

See Also:
  Constant Field Values

### CV_COUNTER_CLOCKWISE

    public static final int CV_COUNTER_CLOCKWISE

See Also:
  Constant Field Values

### CV_DIST_C

    public static final int CV_DIST_C

See Also:
  Constant Field Values

### CV_DIST_FAIR

    public static final int CV_DIST_FAIR

See Also:
  Constant Field Values

### CV_DIST_HUBER

    public static final int CV_DIST_HUBER

See Also:
  Constant Field Values

### CV_DIST_L1

    public static final int CV_DIST_L1

See Also:
  Constant Field Values

### CV_DIST_L12

    public static final int CV_DIST_L12

See Also:
  Constant Field Values

### CV_DIST_L2

    public static final int CV_DIST_L2

See Also:
  Constant Field Values

### CV_DIST_LABEL_CCOMP

    public static final int CV_DIST_LABEL_CCOMP

See Also:
  Constant Field Values

### CV_DIST_LABEL_PIXEL

    public static final int CV_DIST_LABEL_PIXEL

See Also:
  Constant Field Values

### CV_DIST_MASK_3

    public static final int CV_DIST_MASK_3

See Also:
  Constant Field Values

### CV_DIST_MASK_5

    public static final int CV_DIST_MASK_5

See Also:
  Constant Field Values

### CV_DIST_MASK_PRECISE

    public static final int CV_DIST_MASK_PRECISE

See Also:
  Constant Field Values

### CV_DIST_USER

    public static final int CV_DIST_USER

See Also:
  Constant Field Values

### CV_DIST_WELSCH

    public static final int CV_DIST_WELSCH

See Also:
  Constant Field Values

### CV_GAUSSIAN

    public static final int CV_GAUSSIAN

See Also:
  Constant Field Values

### CV_GAUSSIAN_5x5

    public static final int CV_GAUSSIAN_5x5

See Also:
  Constant Field Values

### CV_HOUGH_GRADIENT

    public static final int CV_HOUGH_GRADIENT

See Also:
  Constant Field Values

### CV_HOUGH_MULTI_SCALE

    public static final int CV_HOUGH_MULTI_SCALE

See Also:
  Constant Field Values

### CV_HOUGH_PROBABILISTIC

    public static final int CV_HOUGH_PROBABILISTIC

See Also:
  Constant Field Values

### CV_HOUGH_STANDARD

    public static final int CV_HOUGH_STANDARD

See Also:
  Constant Field Values

### CV_LINK_RUNS

    public static final int CV_LINK_RUNS

See Also:
  Constant Field Values

### CV_MAX_SOBEL_KSIZE

    public static final int CV_MAX_SOBEL_KSIZE

See Also:
  Constant Field Values

### CV_MEDIAN

    public static final int CV_MEDIAN

See Also:
  Constant Field Values

### CV_mRGBA2RGBA

    public static final int CV_mRGBA2RGBA

See Also:
  Constant Field Values

### CV_POLY_APPROX_DP

    public static final int CV_POLY_APPROX_DP

See Also:
  Constant Field Values

### CV_RGBA2mRGBA

    public static final int CV_RGBA2mRGBA

See Also:
  Constant Field Values

### CV_SCHARR

    public static final int CV_SCHARR

See Also:
  Constant Field Values

### CV_SHAPE_CROSS

    public static final int CV_SHAPE_CROSS

See Also:
  Constant Field Values

### CV_SHAPE_CUSTOM

    public static final int CV_SHAPE_CUSTOM

See Also:
  Constant Field Values

### CV_SHAPE_ELLIPSE

    public static final int CV_SHAPE_ELLIPSE

See Also:
  Constant Field Values

### CV_SHAPE_RECT

    public static final int CV_SHAPE_RECT

See Also:
  Constant Field Values

### CV_WARP_FILL_OUTLIERS

    public static final int CV_WARP_FILL_OUTLIERS

See Also:
  Constant Field Values

### CV_WARP_INVERSE_MAP

    public static final int CV_WARP_INVERSE_MAP

See Also:
  Constant Field Values

### DIST_LABEL_CCOMP

    public static final int DIST_LABEL_CCOMP

See Also:
  Constant Field Values

### DIST_LABEL_PIXEL

    public static final int DIST_LABEL_PIXEL

See Also:
  Constant Field Values

### FLOODFILL_FIXED_RANGE

    public static final int FLOODFILL_FIXED_RANGE

See Also:
  Constant Field Values

### FLOODFILL_MASK_ONLY

    public static final int FLOODFILL_MASK_ONLY

See Also:
  Constant Field Values

### GC_BGD

    public static final int GC_BGD

See Also:
  Constant Field Values

### GC_EVAL

    public static final int GC_EVAL

See Also:
  Constant Field Values

### GC_FGD

    public static final int GC_FGD

See Also:
  Constant Field Values

### GC_INIT_WITH_MASK

    public static final int GC_INIT_WITH_MASK

See Also:
  Constant Field Values

### GC_INIT_WITH_RECT

    public static final int GC_INIT_WITH_RECT

See Also:
  Constant Field Values

### GC_PR_BGD

    public static final int GC_PR_BGD

See Also:
  Constant Field Values

### GC_PR_FGD

    public static final int GC_PR_FGD

See Also:
  Constant Field Values

### GHT_POSITION

    public static final int GHT_POSITION

See Also:
  Constant Field Values

### GHT_ROTATION

    public static final int GHT_ROTATION

See Also:
  Constant Field Values

### GHT_SCALE

    public static final int GHT_SCALE

See Also:
  Constant Field Values

### INTER_AREA

    public static final int INTER_AREA

See Also:
  Constant Field Values

### INTER_BITS

    public static final int INTER_BITS

See Also:
  Constant Field Values

### INTER_BITS2

    public static final int INTER_BITS2

See Also:
  Constant Field Values

### INTER_CUBIC

    public static final int INTER_CUBIC

See Also:
  Constant Field Values

### INTER_LANCZOS4

    public static final int INTER_LANCZOS4

See Also:
  Constant Field Values

### INTER_LINEAR

    public static final int INTER_LINEAR

See Also:
  Constant Field Values

### INTER_MAX

    public static final int INTER_MAX

See Also:
  Constant Field Values

### INTER_NEAREST

    public static final int INTER_NEAREST

See Also:
  Constant Field Values

### INTER_TAB_SIZE

    public static final int INTER_TAB_SIZE

See Also:
  Constant Field Values

### INTER_TAB_SIZE2

    public static final int INTER_TAB_SIZE2

See Also:
  Constant Field Values

### KERNEL_ASYMMETRICAL

    public static final int KERNEL_ASYMMETRICAL

See Also:
  Constant Field Values

### KERNEL_GENERAL

    public static final int KERNEL_GENERAL

See Also:
  Constant Field Values

### KERNEL_INTEGER

    public static final int KERNEL_INTEGER

See Also:
  Constant Field Values

### KERNEL_SMOOTH

    public static final int KERNEL_SMOOTH

See Also:
  Constant Field Values

### KERNEL_SYMMETRICAL

    public static final int KERNEL_SYMMETRICAL

See Also:
  Constant Field Values

### MORPH_BLACKHAT

    public static final int MORPH_BLACKHAT

See Also:
  Constant Field Values

### MORPH_CLOSE

    public static final int MORPH_CLOSE

See Also:
  Constant Field Values

### MORPH_CROSS

    public static final int MORPH_CROSS

See Also:
  Constant Field Values

### MORPH_DILATE

    public static final int MORPH_DILATE

See Also:
  Constant Field Values

### MORPH_ELLIPSE

    public static final int MORPH_ELLIPSE

See Also:
  Constant Field Values

### MORPH_ERODE

    public static final int MORPH_ERODE

See Also:
  Constant Field Values

### MORPH_GRADIENT

    public static final int MORPH_GRADIENT

See Also:
  Constant Field Values

### MORPH_OPEN

    public static final int MORPH_OPEN

See Also:
  Constant Field Values

### MORPH_RECT

    public static final int MORPH_RECT

See Also:
  Constant Field Values

### MORPH_TOPHAT

    public static final int MORPH_TOPHAT

See Also:
  Constant Field Values

### PROJ_SPHERICAL_EQRECT

    public static final int PROJ_SPHERICAL_EQRECT

See Also:
  Constant Field Values

### PROJ_SPHERICAL_ORTHO

    public static final int PROJ_SPHERICAL_ORTHO

See Also:
  Constant Field Values

### RETR_CCOMP

    public static final int RETR_CCOMP

See Also:
  Constant Field Values

### RETR_EXTERNAL

    public static final int RETR_EXTERNAL

See Also:
  Constant Field Values

### RETR_FLOODFILL

    public static final int RETR_FLOODFILL

See Also:
  Constant Field Values

### RETR_LIST

    public static final int RETR_LIST

See Also:
  Constant Field Values

### RETR_TREE

    public static final int RETR_TREE

See Also:
  Constant Field Values

### THRESH_BINARY

    public static final int THRESH_BINARY

See Also:
  Constant Field Values

### THRESH_BINARY_INV

    public static final int THRESH_BINARY_INV

See Also:
  Constant Field Values

### THRESH_MASK

    public static final int THRESH_MASK

See Also:
  Constant Field Values

### THRESH_OTSU

    public static final int THRESH_OTSU

See Also:
  Constant Field Values

### THRESH_TOZERO

    public static final int THRESH_TOZERO

See Also:
  Constant Field Values

### THRESH_TOZERO_INV

    public static final int THRESH_TOZERO_INV

See Also:
  Constant Field Values

### THRESH_TRUNC

    public static final int THRESH_TRUNC

See Also:
  Constant Field Values

### TM_CCOEFF

    public static final int TM_CCOEFF

See Also:
  Constant Field Values

### TM_CCOEFF_NORMED

    public static final int TM_CCOEFF_NORMED

See Also:
  Constant Field Values

### TM_CCORR

    public static final int TM_CCORR

See Also:
  Constant Field Values

### TM_CCORR_NORMED

    public static final int TM_CCORR_NORMED

See Also:
  Constant Field Values

### TM_SQDIFF

    public static final int TM_SQDIFF

See Also:
  Constant Field Values

### TM_SQDIFF_NORMED

    public static final int TM_SQDIFF_NORMED

See Also:
  Constant Field Values

### WARP_INVERSE_MAP

    public static final int WARP_INVERSE_MAP

See Also:
  Constant Field Values

Constructor Detail

### Imgproc

    public Imgproc()

Method Detail

### accumulate

    public static void accumulate(Mat src,
                                  Mat dst)

  Adds an image to the accumulator.

  The function adds `src` or some of its elements to `dst` :

  dst(x,y) <- dst(x,y) + src(x,y) if mask(x,y) != 0

  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.

  Parameters:
    `src` - Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
    `dst` - Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
  See Also:
    org.opencv.imgproc.Imgproc.accumulate, `accumulateWeighted(org.opencv.core.Mat, org.opencv.core.Mat, double, org.opencv.core.Mat)`, `accumulateProduct(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateSquare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### accumulate

    public static void accumulate(Mat src,
                                  Mat dst,
                                  Mat mask)

  Adds an image to the accumulator.

  The function adds `src` or some of its elements to `dst` :

  dst(x,y) <- dst(x,y) + src(x,y) if mask(x,y) != 0

  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.

  Parameters:
    `src` - Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
    `dst` - Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
    `mask` - Optional operation mask.
  See Also:
    org.opencv.imgproc.Imgproc.accumulate, `accumulateWeighted(org.opencv.core.Mat, org.opencv.core.Mat, double, org.opencv.core.Mat)`, `accumulateProduct(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateSquare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### accumulateProduct

    public static void accumulateProduct(Mat src1,
                                         Mat src2,
                                         Mat dst)

  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` :

  dst(x,y) <- dst(x,y) + src1(x,y) * src2(x,y) if mask(x,y) != 0

  The function supports multi-channel images. Each channel is processed independently.

  Parameters:
    `src1` - First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
    `src2` - Second input image of the same type and the same size as `src1`.
    `dst` - Accumulator with the same number of channels as input images, 32-bit or 64-bit floating-point.
  See Also:
    org.opencv.imgproc.Imgproc.accumulateProduct, `accumulate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateWeighted(org.opencv.core.Mat, org.opencv.core.Mat, double, org.opencv.core.Mat)`, `accumulateSquare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### accumulateProduct

    public static void accumulateProduct(Mat src1,
                                         Mat src2,
                                         Mat dst,
                                         Mat mask)

  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` :

  dst(x,y) <- dst(x,y) + src1(x,y) * src2(x,y) if mask(x,y) != 0

  The function supports multi-channel images. Each channel is processed independently.

  Parameters:
    `src1` - First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
    `src2` - Second input image of the same type and the same size as `src1`.
    `dst` - Accumulator with the same number of channels as input images, 32-bit or 64-bit floating-point.
    `mask` - Optional operation mask.
  See Also:
    org.opencv.imgproc.Imgproc.accumulateProduct, `accumulate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateWeighted(org.opencv.core.Mat, org.opencv.core.Mat, double, org.opencv.core.Mat)`, `accumulateSquare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### accumulateSquare

    public static void accumulateSquare(Mat src,
                                        Mat dst)

  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` :

  dst(x,y) <- dst(x,y) + src(x,y)^2 if mask(x,y) != 0

  The function supports multi-channel images. Each channel is processed independently.

  Parameters:
    `src` - Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
    `dst` - Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
  See Also:
    org.opencv.imgproc.Imgproc.accumulateSquare, `accumulateWeighted(org.opencv.core.Mat, org.opencv.core.Mat, double, org.opencv.core.Mat)`, `accumulateProduct(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateSquare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### accumulateSquare

    public static void accumulateSquare(Mat src,
                                        Mat dst,
                                        Mat mask)

  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` :

  dst(x,y) <- dst(x,y) + src(x,y)^2 if mask(x,y) != 0

  The function supports multi-channel images. Each channel is processed independently.

  Parameters:
    `src` - Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
    `dst` - Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
    `mask` - Optional operation mask.
  See Also:
    org.opencv.imgproc.Imgproc.accumulateSquare, `accumulateWeighted(org.opencv.core.Mat, org.opencv.core.Mat, double, org.opencv.core.Mat)`, `accumulateProduct(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateSquare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### accumulateWeighted

    public static void accumulateWeighted(Mat src,
                                          Mat dst,
                                          double alpha)

  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:

  dst(x,y) <- (1- alpha) * dst(x,y) + alpha * src(x,y) if mask(x,y) != 0

  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.

  Parameters:
    `src` - Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
    `dst` - Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
    `alpha` - Weight of the input image.
  See Also:
    org.opencv.imgproc.Imgproc.accumulateWeighted, `accumulate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateProduct(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateSquare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### accumulateWeighted

    public static void accumulateWeighted(Mat src,
                                          Mat dst,
                                          double alpha,
                                          Mat mask)

  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:

  dst(x,y) <- (1- alpha) * dst(x,y) + alpha * src(x,y) if mask(x,y) != 0

  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.

  Parameters:
    `src` - Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
    `dst` - Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
    `alpha` - Weight of the input image.
    `mask` - Optional operation mask.
  See Also:
    org.opencv.imgproc.Imgproc.accumulateWeighted, `accumulate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateProduct(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `accumulateSquare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### adaptiveThreshold

    public static void adaptiveThreshold(Mat src,
                                         Mat dst,
                                         double maxValue,
                                         int adaptiveMethod,
                                         int thresholdType,
                                         int blockSize,
                                         double C)

  Applies an adaptive threshold to an array.

  The function transforms a grayscale image to a binary image according to the formulae:

  - THRESH_BINARY

  dst(x,y) = maxValue if src(x,y) > T(x,y); 0 otherwise

  - THRESH_BINARY_INV

  dst(x,y) = 0 if src(x,y) > T(x,y); maxValue otherwise

  where T(x,y) is a threshold calculated individually for each pixel.

  - For the method `ADAPTIVE_THRESH_MEAN_C`, the threshold value T(x,y) is a mean of the blockSize x blockSize neighborhood of (x, y) minus `C`.
  - For the method `ADAPTIVE_THRESH_GAUSSIAN_C`, the threshold value T(x, y) is a weighted sum (cross-correlation with a Gaussian window) of the blockSize x blockSize neighborhood of (x, y) minus `C`. The default sigma (standard deviation) is used for the specified `blockSize`. See "getGaussianKernel".

  The function can process the image in-place.

  Parameters:
    `src` - Source 8-bit single-channel image.
    `dst` - Destination image of the same size and the same type as `src`.
    `maxValue` - Non-zero value assigned to the pixels for which the condition is satisfied. See the details below.
    `adaptiveMethod` - Adaptive thresholding algorithm to use, `ADAPTIVE_THRESH_MEAN_C` or `ADAPTIVE_THRESH_GAUSSIAN_C`. See the details below.
    `thresholdType` - Thresholding type that must be either `THRESH_BINARY` or `THRESH_BINARY_INV`.
    `blockSize` - Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.adaptiveThreshold, `threshold(org.opencv.core.Mat, org.opencv.core.Mat, double, double, int)`, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`

### approxPolyDP

    public static void approxPolyDP(MatOfPoint2f curve,
                                    MatOfPoint2f approxCurve,
                                    double epsilon,
                                    boolean closed)

  Approximates a polygonal curve(s) with the specified precision.

  The functions `approxPolyDP` approximate 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

  See http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/contours.cpp for the function usage model.

  Parameters:
    `curve` - Input vector of a 2D point stored in:
    - `std.vector` or `Mat` (C++ interface)
    - `Nx2` numpy array (Python interface)
    - `CvSeq` or `` CvMat" (C interface)
    `approxCurve` - Result of the approximation. The type should match the type of the input curve. In case of C interface the approximated curve is stored in the memory storage and pointer to it is returned.
    `epsilon` - Parameter specifying the approximation accuracy. This is the maximum distance between the original curve and its approximation.
    `closed` - If true, the approximated curve is closed (its first and last vertices are connected). Otherwise, it is not closed.
  See Also:
    org.opencv.imgproc.Imgproc.approxPolyDP

### arcLength

    public static double arcLength(MatOfPoint2f curve,
                                   boolean closed)

  Calculates a contour perimeter or a curve length.

  The function computes a curve length or a closed contour perimeter.

  Parameters:
    `curve` - Input vector of 2D points, stored in `std.vector` or `Mat`.
    `closed` - Flag indicating whether the curve is closed or not.
  See Also:
    org.opencv.imgproc.Imgproc.arcLength

### bilateralFilter

    public static void bilateralFilter(Mat src,
                                       Mat dst,
                                       int d,
                                       double sigmaColor,
                                       double sigmaSpace)

  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.

  Parameters:
    `src` - Source 8-bit or floating-point, 1-channel or 3-channel image.
    `dst` - Destination image of the same size and type as `src`.
    `d` - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from `sigmaSpace`.
    `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.
    `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`.
  See Also:
    org.opencv.imgproc.Imgproc.bilateralFilter

### bilateralFilter

    public static void bilateralFilter(Mat src,
                                       Mat dst,
                                       int d,
                                       double sigmaColor,
                                       double sigmaSpace,
                                       int borderType)

  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.

  Parameters:
    `src` - Source 8-bit or floating-point, 1-channel or 3-channel image.
    `dst` - Destination image of the same size and type as `src`.
    `d` - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from `sigmaSpace`.
    `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.
    `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`.
    `borderType` - a borderType
  See Also:
    org.opencv.imgproc.Imgproc.bilateralFilter

### blur

    public static void blur(Mat src,
                            Mat dst,
                            Size ksize)

  Blurs an image using the normalized box filter.

  The function smoothes an image using the kernel:

  K = 1/(ksize.width*ksize.height) 1 1 1 *s 1 1 1 1 1 *s 1 1.................. 1 1 1 *s 1 1

  The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), anchor, true, borderType)`.

  Parameters:
    `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`.
    `dst` - output image of the same size and type as `src`.
    `ksize` - blurring kernel size.
  See Also:
    org.opencv.imgproc.Imgproc.blur, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`

### blur

    public static void blur(Mat src,
                            Mat dst,
                            Size ksize,
                            Point anchor)

  Blurs an image using the normalized box filter.

  The function smoothes an image using the kernel:

  K = 1/(ksize.width*ksize.height) 1 1 1 *s 1 1 1 1 1 *s 1 1.................. 1 1 1 *s 1 1

  The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), anchor, true, borderType)`.

  Parameters:
    `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`.
    `dst` - output image of the same size and type as `src`.
    `ksize` - blurring kernel size.
    `anchor` - anchor point; default value `Point(-1,-1)` means that the anchor is at the kernel center.
  See Also:
    org.opencv.imgproc.Imgproc.blur, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`

### blur

    public static void blur(Mat src,
                            Mat dst,
                            Size ksize,
                            Point anchor,
                            int borderType)

  Blurs an image using the normalized box filter.

  The function smoothes an image using the kernel:

  K = 1/(ksize.width*ksize.height) 1 1 1 *s 1 1 1 1 1 *s 1 1.................. 1 1 1 *s 1 1

  The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), anchor, true, borderType)`.

  Parameters:
    `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`.
    `dst` - output image of the same size and type as `src`.
    `ksize` - blurring kernel size.
    `anchor` - anchor point; default value `Point(-1,-1)` means that the anchor is at the kernel center.
    `borderType` - border mode used to extrapolate pixels outside of the image.
  See Also:
    org.opencv.imgproc.Imgproc.blur, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`

### borderInterpolate

    public static int borderInterpolate(int p,
                                        int len,
                                        int borderType)

  Computes the source location of an extrapolated pixel.

  The function computes and returns the coordinate of a donor pixel corresponding to the specified extrapolated pixel when using the specified extrapolation border mode. For example, if you use `BORDER_WRAP` mode in the horizontal direction, `BORDER_REFLECT_101` in the vertical direction and want to compute value of the "virtual" pixel `Point(-5, 100)` in a floating-point image `img`, it looks like: ``

  // C++ code:

  float val = img.at(borderInterpolate(100, img.rows, BORDER_REFLECT_101),

  borderInterpolate(-5, img.cols, BORDER_WRAP));

  Normally, the function is not called directly. It is used inside

  "FilterEngine" and "copyMakeBorder" to compute tables for quick extrapolation.

  Parameters:
    `p` - 0-based coordinate of the extrapolated pixel along one of the axes, likely <0 or >= `len`.
    `len` - Length of the array along the corresponding axis.
    `borderType` - Border type, one of the `BORDER_*`, except for `BORDER_TRANSPARENT` and `BORDER_ISOLATED`. When `borderType==BORDER_CONSTANT`, the function always returns -1, regardless of `p` and `len`.
  See Also:
    org.opencv.imgproc.Imgproc.borderInterpolate, `org.opencv.imgproc.FilterEngine`, `copyMakeBorder(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, int, int, org.opencv.core.Scalar)`

### boundingRect

    public static Rect boundingRect(MatOfPoint points)

  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.

  Parameters:
    `points` - Input 2D point set, stored in `std.vector` or `Mat`.
  See Also:
    org.opencv.imgproc.Imgproc.boundingRect

### boxFilter

    public static void boxFilter(Mat src,
                                 Mat dst,
                                 int ddepth,
                                 Size ksize)

  Blurs an image using the box filter.

  The function smoothes an image using the kernel:

  K = alpha 1 1 1 *s 1 1 1 1 1 *s 1 1.................. 1 1 1 *s 1 1

  where

  alpha = 1/(ksize.width*ksize.height) when normalize=true; 1 otherwise

  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 "integral".

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and type as `src`.
    `ddepth` - a ddepth
    `ksize` - blurring kernel size.
  See Also:
    org.opencv.imgproc.Imgproc.boxFilter, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `integral(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`

### boxFilter

    public static void boxFilter(Mat src,
                                 Mat dst,
                                 int ddepth,
                                 Size ksize,
                                 Point anchor,
                                 boolean normalize)

  Blurs an image using the box filter.

  The function smoothes an image using the kernel:

  K = alpha 1 1 1 *s 1 1 1 1 1 *s 1 1.................. 1 1 1 *s 1 1

  where

  alpha = 1/(ksize.width*ksize.height) when normalize=true; 1 otherwise

  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 "integral".

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and type as `src`.
    `ddepth` - a ddepth
    `ksize` - blurring kernel size.
    `anchor` - anchor point; default value `Point(-1,-1)` means that the anchor is at the kernel center.
    `normalize` - flag, specifying whether the kernel is normalized by its area or not.
  See Also:
    org.opencv.imgproc.Imgproc.boxFilter, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `integral(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`

### boxFilter

    public static void boxFilter(Mat src,
                                 Mat dst,
                                 int ddepth,
                                 Size ksize,
                                 Point anchor,
                                 boolean normalize,
                                 int borderType)

  Blurs an image using the box filter.

  The function smoothes an image using the kernel:

  K = alpha 1 1 1 *s 1 1 1 1 1 *s 1 1.................. 1 1 1 *s 1 1

  where

  alpha = 1/(ksize.width*ksize.height) when normalize=true; 1 otherwise

  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 "integral".

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and type as `src`.
    `ddepth` - a ddepth
    `ksize` - blurring kernel size.
    `anchor` - anchor point; default value `Point(-1,-1)` means that the anchor is at the kernel center.
    `normalize` - flag, specifying whether the kernel is normalized by its area or not.
    `borderType` - border mode used to extrapolate pixels outside of the image.
  See Also:
    org.opencv.imgproc.Imgproc.boxFilter, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `integral(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`

### calcBackProject

    public static void calcBackProject(java.util.List<Mat> images,
                                       MatOfInt channels,
                                       Mat hist,
                                       Mat dst,
                                       MatOfFloat ranges,
                                       double scale)

  Calculates the back projection of a histogram.

  The functions `calcBackProject` calculate the back project of the histogram. That is, similarly to `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.

  Parameters:
    `images` - Source arrays. They all should have the same depth, `CV_8U` or `CV_32F`, and the same size. Each of them can have an arbitrary number of channels.
    `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.
    `hist` - Input histogram that can be dense or sparse.
    `dst` - a dst
    `ranges` - Array of arrays of the histogram bin boundaries in each dimension. See "calcHist".
    `scale` - Optional scale factor for the output back projection.
  See Also:
    org.opencv.imgproc.Imgproc.calcBackProject, `calcHist(java.util.List, org.opencv.core.MatOfInt, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.MatOfInt, org.opencv.core.MatOfFloat, boolean)`

### calcHist

    public static void calcHist(java.util.List<Mat> images,
                                MatOfInt channels,
                                Mat mask,
                                Mat hist,
                                MatOfInt histSize,
                                MatOfFloat ranges)

  Calculates a histogram of a set of arrays.

  The functions `calcHist` calculate the histogram of one or more arrays. The elements of a tuple used to increment a histogram bin are taken from the correspondinginput arrays at the same location. The sample below shows how to compute a 2D Hue-Saturation histogram for a color image. ``

  // C++ code:

  #include

  using namespace cv;

  int main(int argc, char argv)

  Mat src, hsv;

  if(argc != 2 || !(src=imread(argv[1], 1)).data)

  return -1;

  cvtColor(src, hsv, CV_BGR2HSV);

  // Quantize the hue to 30 levels

  // and the saturation to 32 levels

  int hbins = 30, sbins = 32;

  int histSize[] = {hbins, sbins};

  // hue varies from 0 to 179, see cvtColor

  float hranges[] = { 0, 180 };

  // saturation varies from 0 (black-gray-white) to

  // 255 (pure spectrum color)

  float sranges[] = { 0, 256 };

  const float* ranges[] = { hranges, sranges };

  MatND hist;

  // we compute the histogram from the 0-th and 1-st channels

  int channels[] = {0, 1};

  calcHist(&hsv, 1, channels, Mat(), // do not use mask

  hist, 2, histSize, ranges,

  true, // the histogram is uniform

  false);

  double maxVal=0;

  minMaxLoc(hist, 0, &maxVal, 0, 0);

  int scale = 10;

  Mat histImg = Mat.zeros(sbins*scale, hbins*10, CV_8UC3);

  for(int h = 0; h < hbins; h++)

  for(int s = 0; s < sbins; s++)

  float binVal = hist.at(h, s);

  int intensity = cvRound(binVal*255/maxVal);

  rectangle(histImg, Point(h*scale, s*scale),

  Point((h+1)*scale - 1, (s+1)*scale - 1),

  Scalar.all(intensity),

  CV_FILLED);

  namedWindow("Source", 1);

  imshow("Source", src);

  namedWindow("H-S Histogram", 1);

  imshow("H-S Histogram", histImg);

  waitKey();

  Parameters:
    `images` - Source arrays. They all should have the same depth, `CV_8U` or `CV_32F`, and the same size. Each of them can have an arbitrary number of channels.
    `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.
    `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.
    `hist` - Output histogram, which is a dense or sparse `dims` -dimensional array.
    `histSize` - Array of histogram sizes in each dimension.
    `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 L_0 of the 0-th histogram bin and the upper (exclusive) boundary U_(histSize[i]-1) 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: L_0, U_0=L_1, U_1=L_2,..., U_(histSize[i]-2)=L_(histSize[i]-1), U_(histSize[i]-1). The array elements, that are not between L_0 and U_(histSize[i]-1), are not counted in the histogram.
  See Also:
    org.opencv.imgproc.Imgproc.calcHist

### calcHist

    public static void calcHist(java.util.List<Mat> images,
                                MatOfInt channels,
                                Mat mask,
                                Mat hist,
                                MatOfInt histSize,
                                MatOfFloat ranges,
                                boolean accumulate)

  Calculates a histogram of a set of arrays.

  The functions `calcHist` calculate the histogram of one or more arrays. The elements of a tuple used to increment a histogram bin are taken from the correspondinginput arrays at the same location. The sample below shows how to compute a 2D Hue-Saturation histogram for a color image. ``

  // C++ code:

  #include

  using namespace cv;

  int main(int argc, char argv)

  Mat src, hsv;

  if(argc != 2 || !(src=imread(argv[1], 1)).data)

  return -1;

  cvtColor(src, hsv, CV_BGR2HSV);

  // Quantize the hue to 30 levels

  // and the saturation to 32 levels

  int hbins = 30, sbins = 32;

  int histSize[] = {hbins, sbins};

  // hue varies from 0 to 179, see cvtColor

  float hranges[] = { 0, 180 };

  // saturation varies from 0 (black-gray-white) to

  // 255 (pure spectrum color)

  float sranges[] = { 0, 256 };

  const float* ranges[] = { hranges, sranges };

  MatND hist;

  // we compute the histogram from the 0-th and 1-st channels

  int channels[] = {0, 1};

  calcHist(&hsv, 1, channels, Mat(), // do not use mask

  hist, 2, histSize, ranges,

  true, // the histogram is uniform

  false);

  double maxVal=0;

  minMaxLoc(hist, 0, &maxVal, 0, 0);

  int scale = 10;

  Mat histImg = Mat.zeros(sbins*scale, hbins*10, CV_8UC3);

  for(int h = 0; h < hbins; h++)

  for(int s = 0; s < sbins; s++)

  float binVal = hist.at(h, s);

  int intensity = cvRound(binVal*255/maxVal);

  rectangle(histImg, Point(h*scale, s*scale),

  Point((h+1)*scale - 1, (s+1)*scale - 1),

  Scalar.all(intensity),

  CV_FILLED);

  namedWindow("Source", 1);

  imshow("Source", src);

  namedWindow("H-S Histogram", 1);

  imshow("H-S Histogram", histImg);

  waitKey();

  Parameters:
    `images` - Source arrays. They all should have the same depth, `CV_8U` or `CV_32F`, and the same size. Each of them can have an arbitrary number of channels.
    `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.
    `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.
    `hist` - Output histogram, which is a dense or sparse `dims` -dimensional array.
    `histSize` - Array of histogram sizes in each dimension.
    `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 L_0 of the 0-th histogram bin and the upper (exclusive) boundary U_(histSize[i]-1) 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: L_0, U_0=L_1, U_1=L_2,..., U_(histSize[i]-2)=L_(histSize[i]-1), U_(histSize[i]-1). The array elements, that are not between L_0 and U_(histSize[i]-1), are not counted in the histogram.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.calcHist

### Canny

    public static void Canny(Mat image,
                             Mat edges,
                             double threshold1,
                             double threshold2)

  Finds edges in an image using the [Canny86] algorithm.

  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

  Parameters:
    `image` - single-channel 8-bit input image.
    `edges` - output edge map; it has the same size and type as `image`.
    `threshold1` - first threshold for the hysteresis procedure.
    `threshold2` - second threshold for the hysteresis procedure.
  See Also:
    org.opencv.imgproc.Imgproc.Canny

### Canny

    public static void Canny(Mat image,
                             Mat edges,
                             double threshold1,
                             double threshold2,
                             int apertureSize,
                             boolean L2gradient)

  Finds edges in an image using the [Canny86] algorithm.

  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

  Parameters:
    `image` - single-channel 8-bit input image.
    `edges` - output edge map; it has the same size and type as `image`.
    `threshold1` - first threshold for the hysteresis procedure.
    `threshold2` - second threshold for the hysteresis procedure.
    `apertureSize` - aperture size for the "Sobel" operator.
    `L2gradient` - a flag, indicating whether a more accurate L_2 norm =sqrt((dI/dx)^2 + (dI/dy)^2) should be used to calculate the image gradient magnitude (`L2gradient=true`), or whether the default L_1 norm =|dI/dx|+|dI/dy| is enough (`L2gradient=false`).
  See Also:
    org.opencv.imgproc.Imgproc.Canny

### compareHist

    public static double compareHist(Mat H1,
                                     Mat H2,
                                     int method)

  Compares two histograms.

  The functions `compareHist` compare two dense or two sparse histograms using the specified method:

  - Correlation (`method=CV_COMP_CORREL`)

  d(H_1,H_2) = (sum_I(H_1(I) - H_1")(H_2(I) - H_2"))/(sqrt(sum_I(H_1(I) - H_1")^2 sum_I(H_2(I) - H_2")^2))

  where

  H_k" = 1/(N) sum _J H_k(J)

  and N is a total number of histogram bins.

  - Chi-Square (`method=CV_COMP_CHISQR`)

  d(H_1,H_2) = sum _I((H_1(I)-H_2(I))^2)/(H_1(I))

  - Intersection (`method=CV_COMP_INTERSECT`)

  d(H_1,H_2) = sum _I min(H_1(I), H_2(I))

  - Bhattacharyya distance (`method=CV_COMP_BHATTACHARYYA` or `method=CV_COMP_HELLINGER`). In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.

  d(H_1,H_2) = sqrt(1 - frac(1)(sqrt(H_1" H_2" N^2)) sum_I sqrt(H_1(I) * H_2(I)))

  The function returns d(H_1, H_2).

  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 "EMD" function.

  Parameters:
    `H1` - First compared histogram.
    `H2` - Second compared histogram of the same size as `H1`.
    `method` - Comparison method that could be one of the following:
    - CV_COMP_CORREL Correlation
    - CV_COMP_CHISQR Chi-Square
    - CV_COMP_INTERSECT Intersection
    - CV_COMP_BHATTACHARYYA Bhattacharyya distance
    - CV_COMP_HELLINGER Synonym for `CV_COMP_BHATTACHARYYA`
  See Also:
    org.opencv.imgproc.Imgproc.compareHist

### contourArea

    public static double contourArea(Mat contour)

  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: ``

  // C++ code:

  vector contour;

  contour.push_back(Point2f(0, 0));

  contour.push_back(Point2f(10, 0));

  contour.push_back(Point2f(10, 10));

  contour.push_back(Point2f(5, 4));

  double area0 = contourArea(contour);

  vector approx;

  approxPolyDP(contour, approx, 5, true);

  double area1 = contourArea(approx);

  cout << "area0 =" << area0 << endl <<

  "area1 =" << area1 << endl <<

  "approx poly vertices" << approx.size() << endl;

  Parameters:
    `contour` - Input vector of 2D points (contour vertices), stored in `std.vector` or `Mat`.
  See Also:
    org.opencv.imgproc.Imgproc.contourArea

### contourArea

    public static double contourArea(Mat contour,
                                     boolean oriented)

  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: ``

  // C++ code:

  vector contour;

  contour.push_back(Point2f(0, 0));

  contour.push_back(Point2f(10, 0));

  contour.push_back(Point2f(10, 10));

  contour.push_back(Point2f(5, 4));

  double area0 = contourArea(contour);

  vector approx;

  approxPolyDP(contour, approx, 5, true);

  double area1 = contourArea(approx);

  cout << "area0 =" << area0 << endl <<

  "area1 =" << area1 << endl <<

  "approx poly vertices" << approx.size() << endl;

  Parameters:
    `contour` - Input vector of 2D points (contour vertices), stored in `std.vector` or `Mat`.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.contourArea

### convertMaps

    public static void convertMaps(Mat map1,
                                   Mat map2,
                                   Mat dstmap1,
                                   Mat dstmap2,
                                   int dstmap1type)

  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())` -> `(dstmap1.type(), dstmap2.type())`) are supported:

  - (CV_32FC1, CV_32FC1) -> (CV_16SC2, CV_16UC1). 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.
  - (CV_32FC2) -> (CV_16SC2, CV_16UC1). 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.

  Parameters:
    `map1` - The first input map of type `CV_16SC2`, `CV_32FC1`, or `CV_32FC2`.
    `map2` - The second input map of type `CV_16UC1`, `CV_32FC1`, or none (empty matrix), respectively.
    `dstmap1` - The first output map that has the type `dstmap1type` and the same size as `src`.
    `dstmap2` - The second output map.
    `dstmap1type` - Type of the first output map that should be `CV_16SC2`, `CV_32FC1`, or `CV_32FC2`.
  See Also:
    org.opencv.imgproc.Imgproc.convertMaps, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `initUndistortRectifyMap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, org.opencv.core.Mat, org.opencv.core.Mat)`, `undistort(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### convertMaps

    public static void convertMaps(Mat map1,
                                   Mat map2,
                                   Mat dstmap1,
                                   Mat dstmap2,
                                   int dstmap1type,
                                   boolean nninterpolation)

  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())` -> `(dstmap1.type(), dstmap2.type())`) are supported:

  - (CV_32FC1, CV_32FC1) -> (CV_16SC2, CV_16UC1). 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.
  - (CV_32FC2) -> (CV_16SC2, CV_16UC1). 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.

  Parameters:
    `map1` - The first input map of type `CV_16SC2`, `CV_32FC1`, or `CV_32FC2`.
    `map2` - The second input map of type `CV_16UC1`, `CV_32FC1`, or none (empty matrix), respectively.
    `dstmap1` - The first output map that has the type `dstmap1type` and the same size as `src`.
    `dstmap2` - The second output map.
    `dstmap1type` - Type of the first output map that should be `CV_16SC2`, `CV_32FC1`, or `CV_32FC2`.
    `nninterpolation` - Flag indicating whether the fixed-point maps are used for the nearest-neighbor or for a more complex interpolation.
  See Also:
    org.opencv.imgproc.Imgproc.convertMaps, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `initUndistortRectifyMap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, org.opencv.core.Mat, org.opencv.core.Mat)`, `undistort(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### convexHull

    public static void convexHull(MatOfPoint points,
                                  MatOfInt hull)

  Finds the convex hull of a point set.

  The functions find the convex hull of a 2D point set using the Sklansky's algorithm [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.

  Parameters:
    `points` - Input 2D point set, stored in `std.vector` or `Mat`.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.convexHull

### convexHull

    public static void convexHull(MatOfPoint points,
                                  MatOfInt hull,
                                  boolean clockwise)

  Finds the convex hull of a point set.

  The functions find the convex hull of a 2D point set using the Sklansky's algorithm [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.

  Parameters:
    `points` - Input 2D point set, stored in `std.vector` or `Mat`.
    `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.
    `clockwise` - Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The usual screen coordinate system is assumed so that the origin is at the top-left corner, x axis is oriented to the right, and y axis is oriented downwards.
  See Also:
    org.opencv.imgproc.Imgproc.convexHull

### convexityDefects

    public static void convexityDefects(MatOfPoint contour,
                                        MatOfInt convexhull,
                                        MatOfInt4 convexityDefects)

  Finds the convexity defects of a contour.

  The function finds all convexity defects of the input contour and returns a sequence of the `CvConvexityDefect` structures, where `CvConvexityDetect` is defined as: ``

  // C++ code:

  struct CvConvexityDefect

  CvPoint* start; // point of the contour where the defect begins

  CvPoint* end; // point of the contour where the defect ends

  CvPoint* depth_point; // the farthest from the convex hull point within the defect

  float depth; // distance between the farthest point and the convex hull

  };

  The figure below displays convexity defects of a hand contour:

  Parameters:
    `contour` - Input contour.
    `convexhull` - Convex hull obtained using "convexHull" that should contain indices of the contour points that make the hull.
    `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`. In C interface convexity defect is represented by `CvConvexityDefect` structure - see below.
  See Also:
    org.opencv.imgproc.Imgproc.convexityDefects

### copyMakeBorder

    public static void copyMakeBorder(Mat src,
                                      Mat dst,
                                      int top,
                                      int bottom,
                                      int left,
                                      int right,
                                      int borderType)

  Forms a border around an image.

  The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what "FilterEngine" or filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling. The function supports the mode when `src` is already in the middle of `dst`. In this case, the function does not copy `src` itself but simply constructs the border, for example: ``

  // C++ code:

  // let border be the same in all directions

  int border=2;

  // constructs a larger image to fit both the image and the border

  Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());

  // select the middle part of it w/o copying data

  Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));

  // convert image from RGB to grayscale

  cvtColor(rgb, gray, CV_RGB2GRAY);

  // form a border in-place

  copyMakeBorder(gray, gray_buf, border, border,

  border, border, BORDER_REPLICATE);

  // now do some custom filtering......

  Note:

  When the source image is a part (ROI) of a bigger image, the function will try to use the pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as if `src` was not a ROI, use `borderType | BORDER_ISOLATED`.

  Parameters:
    `src` - Source image.
    `dst` - Destination image of the same type as `src` and the size `Size(src.cols+left+right, src.rows+top+bottom)`.
    `top` - a top
    `bottom` - a bottom
    `left` - a left
    `right` - Parameter specifying how many pixels in each direction from the source image rectangle to extrapolate. For example, `top=1, bottom=1, left=1, right=1` mean that 1 pixel-wide border needs to be built.
    `borderType` - Border type. See "borderInterpolate" for details.
  See Also:
    org.opencv.imgproc.Imgproc.copyMakeBorder, `borderInterpolate(int, int, int)`

### copyMakeBorder

    public static void copyMakeBorder(Mat src,
                                      Mat dst,
                                      int top,
                                      int bottom,
                                      int left,
                                      int right,
                                      int borderType,
                                      Scalar value)

  Forms a border around an image.

  The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what "FilterEngine" or filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling. The function supports the mode when `src` is already in the middle of `dst`. In this case, the function does not copy `src` itself but simply constructs the border, for example: ``

  // C++ code:

  // let border be the same in all directions

  int border=2;

  // constructs a larger image to fit both the image and the border

  Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());

  // select the middle part of it w/o copying data

  Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));

  // convert image from RGB to grayscale

  cvtColor(rgb, gray, CV_RGB2GRAY);

  // form a border in-place

  copyMakeBorder(gray, gray_buf, border, border,

  border, border, BORDER_REPLICATE);

  // now do some custom filtering......

  Note:

  When the source image is a part (ROI) of a bigger image, the function will try to use the pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as if `src` was not a ROI, use `borderType | BORDER_ISOLATED`.

  Parameters:
    `src` - Source image.
    `dst` - Destination image of the same type as `src` and the size `Size(src.cols+left+right, src.rows+top+bottom)`.
    `top` - a top
    `bottom` - a bottom
    `left` - a left
    `right` - Parameter specifying how many pixels in each direction from the source image rectangle to extrapolate. For example, `top=1, bottom=1, left=1, right=1` mean that 1 pixel-wide border needs to be built.
    `borderType` - Border type. See "borderInterpolate" for details.
    `value` - Border value if `borderType==BORDER_CONSTANT`.
  See Also:
    org.opencv.imgproc.Imgproc.copyMakeBorder, `borderInterpolate(int, int, int)`

### cornerEigenValsAndVecs

    public static void cornerEigenValsAndVecs(Mat src,
                                              Mat dst,
                                              int blockSize,
                                              int ksize)

  Calculates eigenvalues and eigenvectors of image blocks for corner detection.

  For every pixel p, the function `cornerEigenValsAndVecs` considers a `blockSize` x `blockSize` neighborhood S(p). It calculates the covariation matrix of derivatives over the neighborhood as:

  M = sum(by: S(p))(dI/dx)^2 sum(by: S(p))(dI/dx dI/dy)^2 sum(by: S(p))(dI/dx dI/dy)^2 sum(by: S(p))(dI/dy)^2

  where the derivatives are computed using the "Sobel" operator.

  After that, it finds eigenvectors and eigenvalues of M and stores them in the destination image as (lambda_1, lambda_2, x_1, y_1, x_2, y_2) where

  - lambda_1, lambda_2 are the non-sorted eigenvalues of M
  - x_1, y_1 are the eigenvectors corresponding to lambda_1
  - x_2, y_2 are the eigenvectors corresponding to lambda_2

  The output of the function can be used for robust edge or corner detection.

  Parameters:
    `src` - Input single-channel 8-bit or floating-point image.
    `dst` - Image to store the results. It has the same size as `src` and the type `CV_32FC(6)`.
    `blockSize` - Neighborhood size (see details below).
    `ksize` - Aperture parameter for the "Sobel" operator.
  See Also:
    org.opencv.imgproc.Imgproc.cornerEigenValsAndVecs, `cornerHarris(org.opencv.core.Mat, org.opencv.core.Mat, int, int, double, int)`, `cornerMinEigenVal(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int)`, `preCornerDetect(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`

### cornerEigenValsAndVecs

    public static void cornerEigenValsAndVecs(Mat src,
                                              Mat dst,
                                              int blockSize,
                                              int ksize,
                                              int borderType)

  Calculates eigenvalues and eigenvectors of image blocks for corner detection.

  For every pixel p, the function `cornerEigenValsAndVecs` considers a `blockSize` x `blockSize` neighborhood S(p). It calculates the covariation matrix of derivatives over the neighborhood as:

  M = sum(by: S(p))(dI/dx)^2 sum(by: S(p))(dI/dx dI/dy)^2 sum(by: S(p))(dI/dx dI/dy)^2 sum(by: S(p))(dI/dy)^2

  where the derivatives are computed using the "Sobel" operator.

  After that, it finds eigenvectors and eigenvalues of M and stores them in the destination image as (lambda_1, lambda_2, x_1, y_1, x_2, y_2) where

  - lambda_1, lambda_2 are the non-sorted eigenvalues of M
  - x_1, y_1 are the eigenvectors corresponding to lambda_1
  - x_2, y_2 are the eigenvectors corresponding to lambda_2

  The output of the function can be used for robust edge or corner detection.

  Parameters:
    `src` - Input single-channel 8-bit or floating-point image.
    `dst` - Image to store the results. It has the same size as `src` and the type `CV_32FC(6)`.
    `blockSize` - Neighborhood size (see details below).
    `ksize` - Aperture parameter for the "Sobel" operator.
    `borderType` - Pixel extrapolation method. See "borderInterpolate".
  See Also:
    org.opencv.imgproc.Imgproc.cornerEigenValsAndVecs, `cornerHarris(org.opencv.core.Mat, org.opencv.core.Mat, int, int, double, int)`, `cornerMinEigenVal(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int)`, `preCornerDetect(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`

### cornerHarris

    public static void cornerHarris(Mat src,
                                    Mat dst,
                                    int blockSize,
                                    int ksize,
                                    double k)

  Harris edge detector.

  The function runs the Harris edge detector on the image. Similarly to "cornerMinEigenVal" and "cornerEigenValsAndVecs", for each pixel (x, y) it calculates a 2x2 gradient covariance matrix M^((x,y)) over a blockSize x blockSize neighborhood. Then, it computes the following characteristic:

  dst(x,y) = det M^((x,y)) - k * (tr M^((x,y)))^2

  Corners in the image can be found as the local maxima of this response map.

  Parameters:
    `src` - Input single-channel 8-bit or floating-point image.
    `dst` - Image to store the Harris detector responses. It has the type `CV_32FC1` and the same size as `src`.
    `blockSize` - Neighborhood size (see the details on "cornerEigenValsAndVecs").
    `ksize` - Aperture parameter for the "Sobel" operator.
    `k` - Harris detector free parameter. See the formula below.
  See Also:
    org.opencv.imgproc.Imgproc.cornerHarris

### cornerHarris

    public static void cornerHarris(Mat src,
                                    Mat dst,
                                    int blockSize,
                                    int ksize,
                                    double k,
                                    int borderType)

  Harris edge detector.

  The function runs the Harris edge detector on the image. Similarly to "cornerMinEigenVal" and "cornerEigenValsAndVecs", for each pixel (x, y) it calculates a 2x2 gradient covariance matrix M^((x,y)) over a blockSize x blockSize neighborhood. Then, it computes the following characteristic:

  dst(x,y) = det M^((x,y)) - k * (tr M^((x,y)))^2

  Corners in the image can be found as the local maxima of this response map.

  Parameters:
    `src` - Input single-channel 8-bit or floating-point image.
    `dst` - Image to store the Harris detector responses. It has the type `CV_32FC1` and the same size as `src`.
    `blockSize` - Neighborhood size (see the details on "cornerEigenValsAndVecs").
    `ksize` - Aperture parameter for the "Sobel" operator.
    `k` - Harris detector free parameter. See the formula below.
    `borderType` - Pixel extrapolation method. See "borderInterpolate".
  See Also:
    org.opencv.imgproc.Imgproc.cornerHarris

### cornerMinEigenVal

    public static void cornerMinEigenVal(Mat src,
                                         Mat dst,
                                         int blockSize)

  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, min(lambda_1, lambda_2) in terms of the formulae in the "cornerEigenValsAndVecs" description.

  Parameters:
    `src` - Input single-channel 8-bit or floating-point image.
    `dst` - Image to store the minimal eigenvalues. It has the type `CV_32FC1` and the same size as `src`.
    `blockSize` - Neighborhood size (see the details on "cornerEigenValsAndVecs").
  See Also:
    org.opencv.imgproc.Imgproc.cornerMinEigenVal

### cornerMinEigenVal

    public static void cornerMinEigenVal(Mat src,
                                         Mat dst,
                                         int blockSize,
                                         int ksize)

  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, min(lambda_1, lambda_2) in terms of the formulae in the "cornerEigenValsAndVecs" description.

  Parameters:
    `src` - Input single-channel 8-bit or floating-point image.
    `dst` - Image to store the minimal eigenvalues. It has the type `CV_32FC1` and the same size as `src`.
    `blockSize` - Neighborhood size (see the details on "cornerEigenValsAndVecs").
    `ksize` - Aperture parameter for the "Sobel" operator.
  See Also:
    org.opencv.imgproc.Imgproc.cornerMinEigenVal

### cornerMinEigenVal

    public static void cornerMinEigenVal(Mat src,
                                         Mat dst,
                                         int blockSize,
                                         int ksize,
                                         int borderType)

  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, min(lambda_1, lambda_2) in terms of the formulae in the "cornerEigenValsAndVecs" description.

  Parameters:
    `src` - Input single-channel 8-bit or floating-point image.
    `dst` - Image to store the minimal eigenvalues. It has the type `CV_32FC1` and the same size as `src`.
    `blockSize` - Neighborhood size (see the details on "cornerEigenValsAndVecs").
    `ksize` - Aperture parameter for the "Sobel" operator.
    `borderType` - Pixel extrapolation method. See "borderInterpolate".
  See Also:
    org.opencv.imgproc.Imgproc.cornerMinEigenVal

### cornerSubPix

    public static void cornerSubPix(Mat image,
                                    MatOfPoint2f corners,
                                    Size winSize,
                                    Size zeroZone,
                                    TermCriteria criteria)

  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 q to a point p located within a neighborhood of q is orthogonal to the image gradient at p subject to image and measurement noise. Consider the expression:

  epsilon _i = (DI_(p_i))^T * (q - p_i)

  where (DI_(p_i)) is an image gradient at one of the points p_i in a neighborhood of q. The value of q is to be found so that epsilon_i is minimized. A system of equations may be set up with epsilon_i set to zero:

  sum _i(DI_(p_i) * (DI_(p_i))^T) - sum _i(DI_(p_i) * (DI_(p_i))^T * p_i)

  where the gradients are summed within a neighborhood ("search window") of q. Calling the first gradient term G and the second gradient term b gives:

  q = G^(-1) * b

  The algorithm sets the center of the neighborhood window at this new center q and then iterates until the center stays within a set threshold.

  Parameters:
    `image` - Input image.
    `corners` - Initial coordinates of the input corners and refined coordinates provided for output.
    `winSize` - Half of the side length of the search window. For example, if `winSize=Size(5,5)`, then a 5*2+1 x 5*2+1 = 11 x 11 search window is used.
    `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.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.cornerSubPix

### createHanningWindow

    public static void createHanningWindow(Mat dst,
                                           Size winSize,
                                           int type)

  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: ``

  // C++ code:

  // create hanning window of size 100x100 and type CV_32F

  Mat hann;

  createHanningWindow(hann, Size(100, 100), CV_32F);

  Parameters:
    `dst` - Destination array to place Hann coefficients in
    `winSize` - The window size specifications
    `type` - Created array type
  See Also:
    org.opencv.imgproc.Imgproc.createHanningWindow, `phaseCorrelate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### cvtColor

    public static void cvtColor(Mat src,
                                Mat dst,
                                int code)

  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-> 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: ``

  // C++ code:

  img *= 1./255;

  cvtColor(img, img, CV_BGR2Luv);

  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.

  The function can do the following transformations:

  - Transformations within RGB space like adding/removing the alpha channel, reversing the channel order, conversion to/from 16-bit RGB color (R5:G6:B5 or R5:G5:B5), as well as conversion to/from grayscale using:

  RGB[A] to Gray: Y <- 0.299 * R + 0.587 * G + 0.114 * B

  and

  Gray to RGB[A]: R <- Y, G <- Y, B <- Y, A <- 0

  The conversion from a RGB image to gray is done with:

  ``

  // C++ code:

  cvtColor(src, bwsrc, CV_RGB2GRAY);

  More advanced channel reordering can also be done with "mixChannels".

  - RGB <-> CIE XYZ.Rec 709 with D65 white point (`CV_BGR2XYZ, CV_RGB2XYZ, CV_XYZ2BGR, CV_XYZ2RGB`):

  X Z ltBR gt <- 0.412453 0.357580 0.180423 0.212671 0.715160 0.072169 0.019334 0.119193 0.950227 ltBR gt * R B ltBR gt

  R B ltBR gt <- 3.240479 -1.53715 -0.498535 -0.969256 1.875991 0.041556 0.055648 -0.204043 1.057311 ltBR gt * X Z ltBR gt

  X, Y and Z cover the whole value range (in case of floating-point images, Z may exceed 1).

  - RGB <-> YCrCb JPEG (or YCC) (`CV_BGR2YCrCb, CV_RGB2YCrCb, CV_YCrCb2BGR, CV_YCrCb2RGB`)

  Y <- 0.299 * R + 0.587 * G + 0.114 * B

  Cr <- (R-Y) * 0.713 + delta

  Cb <- (B-Y) * 0.564 + delta

  R <- Y + 1.403 * (Cr - delta)

  G <- Y - 0.344 * (Cr - delta) - 0.714 * (Cb - delta)

  B <- Y + 1.773 * (Cb - delta)

  where

  delta = <= ft (128 for 8-bit images 32768 for 16-bit images 0.5 for floating-point images right.

  Y, Cr, and Cb cover the whole value range.

  - RGB <-> HSV (`CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB`) In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and scaled to fit the 0 to 1 range.

  V <- max(R,G,B)

  S <- (V-min(R,G,B))/(V) if V != 0; 0 otherwise

  H <- (60(G - B))/((V-min(R,G,B))) if V=R; (120+60(B - R))/((V-min(R,G,B))) if V=G; (240+60(R - G))/((V-min(R,G,B))) if V=B

  If H<0 then H <- H+360. On output 0 <= V <= 1, 0 <= S <= 1, 0 <= H <= 360.

  The values are then converted to the destination data type:

  - 8-bit images

  V <- 255 V, S <- 255 S, H <- H/2(to fit to 0 to 255)

  - 16-bit images (currently not supported)

  V <- 65535 V, S <- 65535 S, H <- H

  - 32-bit images H, S, and V are left as is
  - RGB <-> HLS (`CV_BGR2HLS, CV_RGB2HLS, CV_HLS2BGR, CV_HLS2RGB`).

  In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and scaled to fit the 0 to 1 range.

  V_(max) <- (max)(R,G,B)

  V_(min) <- (min)(R,G,B)

  L <- (V_(max) + V_(min))/2

  S <- fork ((V_(max) - V_(min))/(V_(max) + V_(min)))(if L < 0.5)<BR>((V_(max) - V_(min))/(2 - (V_(max) + V_(min))))(if L >= 0.5)

  H <- forkthree ((60(G - B))/(S))(if V_(max)=R)<BR>((120+60(B - R))/(S))(if V_(max)=G)<BR>((240+60(R - G))/(S))(if V_(max)=B)

  If H<0 then H <- H+360. On output 0 <= L <= 1, 0 <= S <= 1, 0 <= H <= 360.

  The values are then converted to the destination data type:

  - 8-bit images

  V <- 255 * V, S <- 255 * S, H <- H/2(to fit to 0 to 255)

  - 16-bit images (currently not supported)

  V <- 65535 * V, S <- 65535 * S, H <- H

  - 32-bit images H, S, V are left as is
  - RGB <-> CIE L*a*b* (`CV_BGR2Lab, CV_RGB2Lab, CV_Lab2BGR, CV_Lab2RGB`).

  In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and scaled to fit the 0 to 1 range.

  [X Y Z] <- |0.412453 0.357580 0.180423| |0.212671 0.715160 0.072169| |0.019334 0.119193 0.950227|

  - [R G B]

  X <- X/X_n, where X_n = 0.950456

  Z <- Z/Z_n, where Z_n = 1.088754

  L <- 116*Y^(1/3)-16 for Y>0.008856; 903.3*Y for Y <= 0.008856

  a <- 500(f(X)-f(Y)) + delta

  b <- 200(f(Y)-f(Z)) + delta

  where

  f(t)= t^(1/3) for t>0.008856; 7.787 t+16/116 for t <= 0.008856

  and

  delta = 128 for 8-bit images; 0 for floating-point images

  This outputs 0 <= L <= 100, -127 <= a <= 127, -127 <= b <= 127. The values are then converted to the destination data type:

  - 8-bit images

  L <- L*255/100, a <- a + 128, b <- b + 128

  - 16-bit images (currently not supported)
  - 32-bit images L, a, and b are left as is
  - RGB <-> CIE L*u*v* (`CV_BGR2Luv, CV_RGB2Luv, CV_Luv2BGR, CV_Luv2RGB`).

  In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and scaled to fit 0 to 1 range.

  [X Y Z] <- |0.412453 0.357580 0.180423| |0.212671 0.715160 0.072169| |0.019334 0.119193 0.950227|

  - [R G B]

  L <- 116 Y^(1/3) for Y>0.008856; 903.3 Y for Y <= 0.008856

  u' <- 4*X/(X + 15*Y + 3 Z)

  v' <- 9*Y/(X + 15*Y + 3 Z)

  u <- 13*L*(u' - u_n) where u_n=0.19793943

  v <- 13*L*(v' - v_n) where v_n=0.46831096

  This outputs 0 <= L <= 100, -134 <= u <= 220, -140 <= v <= 122.

  The values are then converted to the destination data type:

  - 8-bit images

  L <- 255/100 L, u <- 255/354(u + 134), v <- 255/256(v + 140)

  - 16-bit images (currently not supported)
  - 32-bit images L, u, and v are left as is

  The above formulae for converting RGB to/from various color spaces have been taken from multiple sources on the web, primarily from the Charles Poynton site http://www.poynton.com/ColorFAQ.html

  - Bayer -> RGB (`CV_BayerBG2BGR, CV_BayerGB2BGR, CV_BayerRG2BGR, CV_BayerGR2BGR, CV_BayerBG2RGB, CV_BayerGB2RGB, CV_BayerRG2RGB, CV_BayerGR2RGB`). The Bayer pattern is widely used in CCD and CMOS cameras. It enables you to get color pictures from a single plane where R,G, and B pixels (sensors of a particular component) are interleaved as follows: The output RGB components of a pixel are interpolated from 1, 2, or ``

  // C++ code:

  4 neighbors of the pixel having the same color. There are several

  modifications of the above pattern that can be achieved by shifting

  the pattern one pixel left and/or one pixel up. The two letters

  C_1 and

  C_2 in the conversion constants `CV_Bayer` C_1 C_2 `2BGR` and `CV_Bayer` C_1 C_2 `2RGB` indicate the particular pattern

  type. These are components from the second row, second and third

  columns, respectively. For example, the above pattern has a very

  popular "BG" type.

  Parameters:
    `src` - input image: 8-bit unsigned, 16-bit unsigned (`CV_16UC...`), or single-precision floating-point.
    `dst` - output image of the same size and depth as `src`.
    `code` - color space conversion code (see the description below).
  See Also:
    org.opencv.imgproc.Imgproc.cvtColor

### cvtColor

    public static void cvtColor(Mat src,
                                Mat dst,
                                int code,
                                int dstCn)

  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-> 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: ``

  // C++ code:

  img *= 1./255;

  cvtColor(img, img, CV_BGR2Luv);

  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.

  The function can do the following transformations:

  - Transformations within RGB space like adding/removing the alpha channel, reversing the channel order, conversion to/from 16-bit RGB color (R5:G6:B5 or R5:G5:B5), as well as conversion to/from grayscale using:

  RGB[A] to Gray: Y <- 0.299 * R + 0.587 * G + 0.114 * B

  and

  Gray to RGB[A]: R <- Y, G <- Y, B <- Y, A <- 0

  The conversion from a RGB image to gray is done with:

  ``

  // C++ code:

  cvtColor(src, bwsrc, CV_RGB2GRAY);

  More advanced channel reordering can also be done with "mixChannels".

  - RGB <-> CIE XYZ.Rec 709 with D65 white point (`CV_BGR2XYZ, CV_RGB2XYZ, CV_XYZ2BGR, CV_XYZ2RGB`):

  X Z ltBR gt <- 0.412453 0.357580 0.180423 0.212671 0.715160 0.072169 0.019334 0.119193 0.950227 ltBR gt * R B ltBR gt

  R B ltBR gt <- 3.240479 -1.53715 -0.498535 -0.969256 1.875991 0.041556 0.055648 -0.204043 1.057311 ltBR gt * X Z ltBR gt

  X, Y and Z cover the whole value range (in case of floating-point images, Z may exceed 1).

  - RGB <-> YCrCb JPEG (or YCC) (`CV_BGR2YCrCb, CV_RGB2YCrCb, CV_YCrCb2BGR, CV_YCrCb2RGB`)

  Y <- 0.299 * R + 0.587 * G + 0.114 * B

  Cr <- (R-Y) * 0.713 + delta

  Cb <- (B-Y) * 0.564 + delta

  R <- Y + 1.403 * (Cr - delta)

  G <- Y - 0.344 * (Cr - delta) - 0.714 * (Cb - delta)

  B <- Y + 1.773 * (Cb - delta)

  where

  delta = <= ft (128 for 8-bit images 32768 for 16-bit images 0.5 for floating-point images right.

  Y, Cr, and Cb cover the whole value range.

  - RGB <-> HSV (`CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB`) In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and scaled to fit the 0 to 1 range.

  V <- max(R,G,B)

  S <- (V-min(R,G,B))/(V) if V != 0; 0 otherwise

  H <- (60(G - B))/((V-min(R,G,B))) if V=R; (120+60(B - R))/((V-min(R,G,B))) if V=G; (240+60(R - G))/((V-min(R,G,B))) if V=B

  If H<0 then H <- H+360. On output 0 <= V <= 1, 0 <= S <= 1, 0 <= H <= 360.

  The values are then converted to the destination data type:

  - 8-bit images

  V <- 255 V, S <- 255 S, H <- H/2(to fit to 0 to 255)

  - 16-bit images (currently not supported)

  V <- 65535 V, S <- 65535 S, H <- H

  - 32-bit images H, S, and V are left as is
  - RGB <-> HLS (`CV_BGR2HLS, CV_RGB2HLS, CV_HLS2BGR, CV_HLS2RGB`).

  In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and scaled to fit the 0 to 1 range.

  V_(max) <- (max)(R,G,B)

  V_(min) <- (min)(R,G,B)

  L <- (V_(max) + V_(min))/2

  S <- fork ((V_(max) - V_(min))/(V_(max) + V_(min)))(if L < 0.5)<BR>((V_(max) - V_(min))/(2 - (V_(max) + V_(min))))(if L >= 0.5)

  H <- forkthree ((60(G - B))/(S))(if V_(max)=R)<BR>((120+60(B - R))/(S))(if V_(max)=G)<BR>((240+60(R - G))/(S))(if V_(max)=B)

  If H<0 then H <- H+360. On output 0 <= L <= 1, 0 <= S <= 1, 0 <= H <= 360.

  The values are then converted to the destination data type:

  - 8-bit images

  V <- 255 * V, S <- 255 * S, H <- H/2(to fit to 0 to 255)

  - 16-bit images (currently not supported)

  V <- 65535 * V, S <- 65535 * S, H <- H

  - 32-bit images H, S, V are left as is
  - RGB <-> CIE L*a*b* (`CV_BGR2Lab, CV_RGB2Lab, CV_Lab2BGR, CV_Lab2RGB`).

  In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and scaled to fit the 0 to 1 range.

  [X Y Z] <- |0.412453 0.357580 0.180423| |0.212671 0.715160 0.072169| |0.019334 0.119193 0.950227|

  - [R G B]

  X <- X/X_n, where X_n = 0.950456

  Z <- Z/Z_n, where Z_n = 1.088754

  L <- 116*Y^(1/3)-16 for Y>0.008856; 903.3*Y for Y <= 0.008856

  a <- 500(f(X)-f(Y)) + delta

  b <- 200(f(Y)-f(Z)) + delta

  where

  f(t)= t^(1/3) for t>0.008856; 7.787 t+16/116 for t <= 0.008856

  and

  delta = 128 for 8-bit images; 0 for floating-point images

  This outputs 0 <= L <= 100, -127 <= a <= 127, -127 <= b <= 127. The values are then converted to the destination data type:

  - 8-bit images

  L <- L*255/100, a <- a + 128, b <- b + 128

  - 16-bit images (currently not supported)
  - 32-bit images L, a, and b are left as is
  - RGB <-> CIE L*u*v* (`CV_BGR2Luv, CV_RGB2Luv, CV_Luv2BGR, CV_Luv2RGB`).

  In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and scaled to fit 0 to 1 range.

  [X Y Z] <- |0.412453 0.357580 0.180423| |0.212671 0.715160 0.072169| |0.019334 0.119193 0.950227|

  - [R G B]

  L <- 116 Y^(1/3) for Y>0.008856; 903.3 Y for Y <= 0.008856

  u' <- 4*X/(X + 15*Y + 3 Z)

  v' <- 9*Y/(X + 15*Y + 3 Z)

  u <- 13*L*(u' - u_n) where u_n=0.19793943

  v <- 13*L*(v' - v_n) where v_n=0.46831096

  This outputs 0 <= L <= 100, -134 <= u <= 220, -140 <= v <= 122.

  The values are then converted to the destination data type:

  - 8-bit images

  L <- 255/100 L, u <- 255/354(u + 134), v <- 255/256(v + 140)

  - 16-bit images (currently not supported)
  - 32-bit images L, u, and v are left as is

  The above formulae for converting RGB to/from various color spaces have been taken from multiple sources on the web, primarily from the Charles Poynton site http://www.poynton.com/ColorFAQ.html

  - Bayer -> RGB (`CV_BayerBG2BGR, CV_BayerGB2BGR, CV_BayerRG2BGR, CV_BayerGR2BGR, CV_BayerBG2RGB, CV_BayerGB2RGB, CV_BayerRG2RGB, CV_BayerGR2RGB`). The Bayer pattern is widely used in CCD and CMOS cameras. It enables you to get color pictures from a single plane where R,G, and B pixels (sensors of a particular component) are interleaved as follows: The output RGB components of a pixel are interpolated from 1, 2, or ``

  // C++ code:

  4 neighbors of the pixel having the same color. There are several

  modifications of the above pattern that can be achieved by shifting

  the pattern one pixel left and/or one pixel up. The two letters

  C_1 and

  C_2 in the conversion constants `CV_Bayer` C_1 C_2 `2BGR` and `CV_Bayer` C_1 C_2 `2RGB` indicate the particular pattern

  type. These are components from the second row, second and third

  columns, respectively. For example, the above pattern has a very

  popular "BG" type.

  Parameters:
    `src` - input image: 8-bit unsigned, 16-bit unsigned (`CV_16UC...`), or single-precision floating-point.
    `dst` - output image of the same size and depth as `src`.
    `code` - color space conversion code (see the description below).
    `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 Also:
    org.opencv.imgproc.Imgproc.cvtColor

### dilate

    public static void dilate(Mat src,
                              Mat dst,
                              Mat kernel)

  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:

  dst(x,y) = max _((x',y'): element(x',y') != 0) src(x+x',y+y')

  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.

  Parameters:
    `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".
    `dst` - output image of the same size and type as `src`.
    `kernel` - a kernel
  See Also:
    org.opencv.imgproc.Imgproc.dilate, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `erode(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `morphologyEx(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### dilate

    public static void dilate(Mat src,
                              Mat dst,
                              Mat kernel,
                              Point anchor,
                              int iterations)

  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:

  dst(x,y) = max _((x',y'): element(x',y') != 0) src(x+x',y+y')

  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.

  Parameters:
    `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".
    `dst` - output image of the same size and type as `src`.
    `kernel` - a kernel
    `anchor` - position of the anchor within the element; default value `(-1, -1)` means that the anchor is at the element center.
    `iterations` - number of times dilation is applied.
  See Also:
    org.opencv.imgproc.Imgproc.dilate, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `erode(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `morphologyEx(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### dilate

    public static void dilate(Mat src,
                              Mat dst,
                              Mat kernel,
                              Point anchor,
                              int iterations,
                              int borderType,
                              Scalar borderValue)

  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:

  dst(x,y) = max _((x',y'): element(x',y') != 0) src(x+x',y+y')

  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.

  Parameters:
    `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".
    `dst` - output image of the same size and type as `src`.
    `kernel` - a kernel
    `anchor` - position of the anchor within the element; default value `(-1, -1)` means that the anchor is at the element center.
    `iterations` - number of times dilation is applied.
    `borderType` - pixel extrapolation method (see "borderInterpolate" for details).
    `borderValue` - border value in case of a constant border (see "createMorphologyFilter" for details).
  See Also:
    org.opencv.imgproc.Imgproc.dilate, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `erode(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `morphologyEx(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### distanceTransform

    public static void distanceTransform(Mat src,
                                         Mat dst,
                                         int distanceType,
                                         int maskSize)

  Calculates the distance to the closest zero pixel for each pixel of the source image.

  The functions `distanceTransform` calculate 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 == CV_DIST_MASK_PRECISE` and `distanceType == CV_DIST_L2`, the function runs the algorithm described in [Felzenszwalb04]. This algorithm is parallelized with the TBB library.

  In other cases, the algorithm [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 5x 5 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 5x 5 mask gives more accurate results). For `a`,`b`, and `c`, OpenCV uses the values suggested in the original paper:

  ============== =================== ====================== `CV_DIST_C` (3x 3) a = 1, b = 1 \ ============== =================== ====================== `CV_DIST_L1` (3x 3) a = 1, b = 2 \ `CV_DIST_L2` (3x 3) a=0.955, b=1.3693 \ `CV_DIST_L2` (5x 5) a=1, b=1.4, c=2.1969 \ ============== =================== ======================

  Typically, for a fast, coarse distance estimation `CV_DIST_L2`, a 3x 3 mask is used. For a more accurate distance estimation `CV_DIST_L2`, a 5x 5 mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.

  The second variant of the function does not only compute the minimum distance for each pixel (x, y) 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=CV_DIST_MASK_PRECISE` is not supported yet.

  Parameters:
    `src` - 8-bit, single-channel (binary) source image.
    `dst` - Output image with calculated distances. It is a 32-bit floating-point, single-channel image of the same size as `src`.
    `distanceType` - Type of distance. It can be `CV_DIST_L1, CV_DIST_L2`, or `CV_DIST_C`.
    `maskSize` - Size of the distance transform mask. It can be 3, 5, or `CV_DIST_MASK_PRECISE` (the latter option is only supported by the first function). In case of the `CV_DIST_L1` or `CV_DIST_C` distance type, the parameter is forced to 3 because a 3x 3 mask gives the same result as 5x 5 or any larger aperture.
  See Also:
    org.opencv.imgproc.Imgproc.distanceTransform

### distanceTransformWithLabels

    public static void distanceTransformWithLabels(Mat src,
                                                   Mat dst,
                                                   Mat labels,
                                                   int distanceType,
                                                   int maskSize)

  Calculates the distance to the closest zero pixel for each pixel of the source image.

  The functions `distanceTransform` calculate 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 == CV_DIST_MASK_PRECISE` and `distanceType == CV_DIST_L2`, the function runs the algorithm described in [Felzenszwalb04]. This algorithm is parallelized with the TBB library.

  In other cases, the algorithm [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 5x 5 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 5x 5 mask gives more accurate results). For `a`,`b`, and `c`, OpenCV uses the values suggested in the original paper:

  ============== =================== ====================== `CV_DIST_C` (3x 3) a = 1, b = 1 \ ============== =================== ====================== `CV_DIST_L1` (3x 3) a = 1, b = 2 \ `CV_DIST_L2` (3x 3) a=0.955, b=1.3693 \ `CV_DIST_L2` (5x 5) a=1, b=1.4, c=2.1969 \ ============== =================== ======================

  Typically, for a fast, coarse distance estimation `CV_DIST_L2`, a 3x 3 mask is used. For a more accurate distance estimation `CV_DIST_L2`, a 5x 5 mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.

  The second variant of the function does not only compute the minimum distance for each pixel (x, y) 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=CV_DIST_MASK_PRECISE` is not supported yet.

  Parameters:
    `src` - 8-bit, single-channel (binary) source image.
    `dst` - Output image with calculated distances. It is a 32-bit floating-point, single-channel image of the same size as `src`.
    `labels` - Optional output 2D array of labels (the discrete Voronoi diagram). It has the type `CV_32SC1` and the same size as `src`. See the details below.
    `distanceType` - Type of distance. It can be `CV_DIST_L1, CV_DIST_L2`, or `CV_DIST_C`.
    `maskSize` - Size of the distance transform mask. It can be 3, 5, or `CV_DIST_MASK_PRECISE` (the latter option is only supported by the first function). In case of the `CV_DIST_L1` or `CV_DIST_C` distance type, the parameter is forced to 3 because a 3x 3 mask gives the same result as 5x 5 or any larger aperture.
  See Also:
    org.opencv.imgproc.Imgproc.distanceTransform

### distanceTransformWithLabels

    public static void distanceTransformWithLabels(Mat src,
                                                   Mat dst,
                                                   Mat labels,
                                                   int distanceType,
                                                   int maskSize,
                                                   int labelType)

  Calculates the distance to the closest zero pixel for each pixel of the source image.

  The functions `distanceTransform` calculate 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 == CV_DIST_MASK_PRECISE` and `distanceType == CV_DIST_L2`, the function runs the algorithm described in [Felzenszwalb04]. This algorithm is parallelized with the TBB library.

  In other cases, the algorithm [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 5x 5 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 5x 5 mask gives more accurate results). For `a`,`b`, and `c`, OpenCV uses the values suggested in the original paper:

  ============== =================== ====================== `CV_DIST_C` (3x 3) a = 1, b = 1 \ ============== =================== ====================== `CV_DIST_L1` (3x 3) a = 1, b = 2 \ `CV_DIST_L2` (3x 3) a=0.955, b=1.3693 \ `CV_DIST_L2` (5x 5) a=1, b=1.4, c=2.1969 \ ============== =================== ======================

  Typically, for a fast, coarse distance estimation `CV_DIST_L2`, a 3x 3 mask is used. For a more accurate distance estimation `CV_DIST_L2`, a 5x 5 mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.

  The second variant of the function does not only compute the minimum distance for each pixel (x, y) 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=CV_DIST_MASK_PRECISE` is not supported yet.

  Parameters:
    `src` - 8-bit, single-channel (binary) source image.
    `dst` - Output image with calculated distances. It is a 32-bit floating-point, single-channel image of the same size as `src`.
    `labels` - Optional output 2D array of labels (the discrete Voronoi diagram). It has the type `CV_32SC1` and the same size as `src`. See the details below.
    `distanceType` - Type of distance. It can be `CV_DIST_L1, CV_DIST_L2`, or `CV_DIST_C`.
    `maskSize` - Size of the distance transform mask. It can be 3, 5, or `CV_DIST_MASK_PRECISE` (the latter option is only supported by the first function). In case of the `CV_DIST_L1` or `CV_DIST_C` distance type, the parameter is forced to 3 because a 3x 3 mask gives the same result as 5x 5 or any larger aperture.
    `labelType` - Type of the label array to build. If `labelType==DIST_LABEL_CCOMP` then 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. If `labelType==DIST_LABEL_PIXEL` then each zero pixel (and all the non-zero pixels closest to it) gets its own label.
  See Also:
    org.opencv.imgproc.Imgproc.distanceTransform

### drawContours

    public static void drawContours(Mat image,
                                    java.util.List<MatOfPoint> contours,
                                    int contourIdx,
                                    Scalar color)

  Draws contours outlines or filled contours.

  The function draws contour outlines in the image if thickness >= 0 or fills the area bounded by the contours ifthickness<0. The example below shows how to retrieve connected components from the binary image and label them: ``

  // C++ code:

  #include "cv.h"

  #include "highgui.h"

  using namespace cv;

  int main(int argc, char argv)

  Mat src;

  // the first command-line parameter must be a filename of the binary

  // (black-n-white) image

  if(argc != 2 || !(src=imread(argv[1], 0)).data)

  return -1;

  Mat dst = Mat.zeros(src.rows, src.cols, CV_8UC3);

  src = src > 1;

  namedWindow("Source", 1);

  imshow("Source", src);

  vector > contours;

  vector hierarchy;

  findContours(src, contours, hierarchy,

  CV_RETR_CCOMP, CV_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, CV_FILLED, 8, hierarchy);

  namedWindow("Components", 1);

  imshow("Components", dst);

  waitKey(0);

  Parameters:
    `image` - Destination image.
    `contours` - All the input contours. Each contour is stored as a point vector.
    `contourIdx` - Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
    `color` - Color of the contours.
  See Also:
    org.opencv.imgproc.Imgproc.drawContours

### drawContours

    public static void drawContours(Mat image,
                                    java.util.List<MatOfPoint> contours,
                                    int contourIdx,
                                    Scalar color,
                                    int thickness)

  Draws contours outlines or filled contours.

  The function draws contour outlines in the image if thickness >= 0 or fills the area bounded by the contours ifthickness<0. The example below shows how to retrieve connected components from the binary image and label them: ``

  // C++ code:

  #include "cv.h"

  #include "highgui.h"

  using namespace cv;

  int main(int argc, char argv)

  Mat src;

  // the first command-line parameter must be a filename of the binary

  // (black-n-white) image

  if(argc != 2 || !(src=imread(argv[1], 0)).data)

  return -1;

  Mat dst = Mat.zeros(src.rows, src.cols, CV_8UC3);

  src = src > 1;

  namedWindow("Source", 1);

  imshow("Source", src);

  vector > contours;

  vector hierarchy;

  findContours(src, contours, hierarchy,

  CV_RETR_CCOMP, CV_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, CV_FILLED, 8, hierarchy);

  namedWindow("Components", 1);

  imshow("Components", dst);

  waitKey(0);

  Parameters:
    `image` - Destination image.
    `contours` - All the input contours. Each contour is stored as a point vector.
    `contourIdx` - Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
    `color` - Color of the contours.
    `thickness` - Thickness of lines the contours are drawn with. If it is negative (for example, `thickness=CV_FILLED`), the contour interiors are drawn.
  See Also:
    org.opencv.imgproc.Imgproc.drawContours

### drawContours

    public static void drawContours(Mat image,
                                    java.util.List<MatOfPoint> contours,
                                    int contourIdx,
                                    Scalar color,
                                    int thickness,
                                    int lineType,
                                    Mat hierarchy,
                                    int maxLevel,
                                    Point offset)

  Draws contours outlines or filled contours.

  The function draws contour outlines in the image if thickness >= 0 or fills the area bounded by the contours ifthickness<0. The example below shows how to retrieve connected components from the binary image and label them: ``

  // C++ code:

  #include "cv.h"

  #include "highgui.h"

  using namespace cv;

  int main(int argc, char argv)

  Mat src;

  // the first command-line parameter must be a filename of the binary

  // (black-n-white) image

  if(argc != 2 || !(src=imread(argv[1], 0)).data)

  return -1;

  Mat dst = Mat.zeros(src.rows, src.cols, CV_8UC3);

  src = src > 1;

  namedWindow("Source", 1);

  imshow("Source", src);

  vector > contours;

  vector hierarchy;

  findContours(src, contours, hierarchy,

  CV_RETR_CCOMP, CV_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, CV_FILLED, 8, hierarchy);

  namedWindow("Components", 1);

  imshow("Components", dst);

  waitKey(0);

  Parameters:
    `image` - Destination image.
    `contours` - All the input contours. Each contour is stored as a point vector.
    `contourIdx` - Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
    `color` - Color of the contours.
    `thickness` - Thickness of lines the contours are drawn with. If it is negative (for example, `thickness=CV_FILLED`), the contour interiors are drawn.
    `lineType` - Line connectivity. See "line" for details.
    `hierarchy` - Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see `maxLevel`).
    `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.
    `offset` - Optional contour shift parameter. Shift all the drawn contours by the specified offset=(dx,dy).
  See Also:
    org.opencv.imgproc.Imgproc.drawContours

### equalizeHist

    public static void equalizeHist(Mat src,
                                    Mat dst)

  Equalizes the histogram of a grayscale image.

  The function equalizes the histogram of the input image using the following algorithm:

  - Calculate the histogram H for `src`.
  - Normalize the histogram so that the sum of histogram bins is 255.
  - Compute the integral of the histogram:

  H'_i = sum(by: 0 <= j < i) H(j)

  Transform the image using H' as a look-up table: dst(x,y) = H'(src(x,y))

  The algorithm normalizes the brightness and increases the contrast of the image.

  Parameters:
    `src` - Source 8-bit single channel image.
    `dst` - Destination image of the same size and type as `src`.
  See Also:
    org.opencv.imgproc.Imgproc.equalizeHist

### erode

    public static void erode(Mat src,
                             Mat dst,
                             Mat kernel)

  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:

  dst(x,y) = min _((x',y'): element(x',y') != 0) src(x+x',y+y')

  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.

  Parameters:
    `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".
    `dst` - output image of the same size and type as `src`.
    `kernel` - a kernel
  See Also:
    org.opencv.imgproc.Imgproc.erode, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `morphologyEx(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `dilate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### erode

    public static void erode(Mat src,
                             Mat dst,
                             Mat kernel,
                             Point anchor,
                             int iterations)

  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:

  dst(x,y) = min _((x',y'): element(x',y') != 0) src(x+x',y+y')

  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.

  Parameters:
    `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".
    `dst` - output image of the same size and type as `src`.
    `kernel` - a kernel
    `anchor` - position of the anchor within the element; default value `(-1, -1)` means that the anchor is at the element center.
    `iterations` - number of times erosion is applied.
  See Also:
    org.opencv.imgproc.Imgproc.erode, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `morphologyEx(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `dilate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### erode

    public static void erode(Mat src,
                             Mat dst,
                             Mat kernel,
                             Point anchor,
                             int iterations,
                             int borderType,
                             Scalar borderValue)

  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:

  dst(x,y) = min _((x',y'): element(x',y') != 0) src(x+x',y+y')

  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.

  Parameters:
    `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".
    `dst` - output image of the same size and type as `src`.
    `kernel` - a kernel
    `anchor` - position of the anchor within the element; default value `(-1, -1)` means that the anchor is at the element center.
    `iterations` - number of times erosion is applied.
    `borderType` - pixel extrapolation method (see "borderInterpolate" for details).
    `borderValue` - border value in case of a constant border (see "createMorphologyFilter" for details).
  See Also:
    org.opencv.imgproc.Imgproc.erode, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `morphologyEx(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `dilate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### filter2D

    public static void filter2D(Mat src,
                                Mat dst,
                                int ddepth,
                                Mat kernel)

  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:

  dst(x,y) = sum(by: 0 <= x' < kernel.cols, 0 <= y' < kernel.rows) kernel(x',y')* src(x+x'- anchor.x,y+y'- anchor.y)

  That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using "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 (that uses the engine retrieved by "createLinearFilter") for small kernels.

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - desired depth of the destination image; if it is negative, it will be the same as `src.depth()`; the following combinations of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the output image will have the same depth as the source.

    `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.
  See Also:
    org.opencv.imgproc.Imgproc.filter2D, `matchTemplate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int)`, `Core.dft(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `org.opencv.imgproc.Imgproc#createLinearFilter`

### filter2D

    public static void filter2D(Mat src,
                                Mat dst,
                                int ddepth,
                                Mat kernel,
                                Point anchor,
                                double delta)

  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:

  dst(x,y) = sum(by: 0 <= x' < kernel.cols, 0 <= y' < kernel.rows) kernel(x',y')* src(x+x'- anchor.x,y+y'- anchor.y)

  That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using "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 (that uses the engine retrieved by "createLinearFilter") for small kernels.

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - desired depth of the destination image; if it is negative, it will be the same as `src.depth()`; the following combinations of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the output image will have the same depth as the source.

    `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.
    `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.
    `delta` - optional value added to the filtered pixels before storing them in `dst`.
  See Also:
    org.opencv.imgproc.Imgproc.filter2D, `matchTemplate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int)`, `Core.dft(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `org.opencv.imgproc.Imgproc#createLinearFilter`

### filter2D

    public static void filter2D(Mat src,
                                Mat dst,
                                int ddepth,
                                Mat kernel,
                                Point anchor,
                                double delta,
                                int borderType)

  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:

  dst(x,y) = sum(by: 0 <= x' < kernel.cols, 0 <= y' < kernel.rows) kernel(x',y')* src(x+x'- anchor.x,y+y'- anchor.y)

  That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using "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 (that uses the engine retrieved by "createLinearFilter") for small kernels.

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - desired depth of the destination image; if it is negative, it will be the same as `src.depth()`; the following combinations of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the output image will have the same depth as the source.

    `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.
    `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.
    `delta` - optional value added to the filtered pixels before storing them in `dst`.
    `borderType` - pixel extrapolation method (see "borderInterpolate" for details).
  See Also:
    org.opencv.imgproc.Imgproc.filter2D, `matchTemplate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int)`, `Core.dft(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `org.opencv.imgproc.Imgproc#createLinearFilter`

### findContours

    public static void findContours(Mat image,
                                    java.util.List<MatOfPoint> contours,
                                    Mat hierarchy,
                                    int mode,
                                    int method)

  Finds contours in a binary image.

  The function retrieves contours from the binary image using the algorithm [Suzuki85]. The contours are a useful tool for shape analysis and object detection and recognition. See `squares.c` in the OpenCV sample directory.

  Note: Source `image` is modified by this function. Also, the function does not take into account 1-pixel border of the image (it's filled with 0's and used for neighbor analysis in the algorithm), therefore the contours touching the image border will be clipped.

  Note: If you use the new Python interface then the `CV_` prefix has to be omitted in contour retrieval mode and contour approximation method parameters (for example, use `cv2.RETR_LIST` and `cv2.CHAIN_APPROX_NONE` parameters). If you use the old Python interface then these parameters have the `CV_` prefix (for example, use `cv.CV_RETR_LIST` and `cv.CV_CHAIN_APPROX_NONE`).

  Parameters:
    `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 "compare", "inRange", "threshold", "adaptiveThreshold", "Canny", and others to create a binary image out of a grayscale or color one. The function modifies the `image` while extracting the contours.
    `contours` - Detected contours. Each contour is stored as a vector of points.
    `hierarchy` - Optional output vector containing information about the image topology. It has as many elements as the number of contours. For each 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 a contour `i` there are no next, previous, parent, or nested contours, the corresponding elements of `hierarchy[i]` will be negative.
    `mode` - Contour retrieval mode (if you use Python see also a note below).
    - CV_RETR_EXTERNAL retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for all the contours.
    - CV_RETR_LIST retrieves all of the contours without establishing any hierarchical relationships.
    - CV_RETR_CCOMP 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.
    - CV_RETR_TREE retrieves all of the contours and reconstructs a full hierarchy of nested contours. This full hierarchy is built and shown in the OpenCV `contours.c` demo.
    `method` - Contour approximation method (if you use Python see also a note below).
    - CV_CHAIN_APPROX_NONE 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`.
    - CV_CHAIN_APPROX_SIMPLE compresses horizontal, vertical, and diagonal segments and leaves only their end points. For example, an up-right rectangular contour is encoded with 4 points.
    - CV_CHAIN_APPROX_TC89_L1,CV_CHAIN_APPROX_TC89_KCOS applies one of the flavors of the Teh-Chin chain approximation algorithm. See [TehChin89] for details.
  See Also:
    org.opencv.imgproc.Imgproc.findContours

### findContours

    public static void findContours(Mat image,
                                    java.util.List<MatOfPoint> contours,
                                    Mat hierarchy,
                                    int mode,
                                    int method,
                                    Point offset)

  Finds contours in a binary image.

  The function retrieves contours from the binary image using the algorithm [Suzuki85]. The contours are a useful tool for shape analysis and object detection and recognition. See `squares.c` in the OpenCV sample directory.

  Note: Source `image` is modified by this function. Also, the function does not take into account 1-pixel border of the image (it's filled with 0's and used for neighbor analysis in the algorithm), therefore the contours touching the image border will be clipped.

  Note: If you use the new Python interface then the `CV_` prefix has to be omitted in contour retrieval mode and contour approximation method parameters (for example, use `cv2.RETR_LIST` and `cv2.CHAIN_APPROX_NONE` parameters). If you use the old Python interface then these parameters have the `CV_` prefix (for example, use `cv.CV_RETR_LIST` and `cv.CV_CHAIN_APPROX_NONE`).

  Parameters:
    `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 "compare", "inRange", "threshold", "adaptiveThreshold", "Canny", and others to create a binary image out of a grayscale or color one. The function modifies the `image` while extracting the contours.
    `contours` - Detected contours. Each contour is stored as a vector of points.
    `hierarchy` - Optional output vector containing information about the image topology. It has as many elements as the number of contours. For each 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 a contour `i` there are no next, previous, parent, or nested contours, the corresponding elements of `hierarchy[i]` will be negative.
    `mode` - Contour retrieval mode (if you use Python see also a note below).
    - CV_RETR_EXTERNAL retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for all the contours.
    - CV_RETR_LIST retrieves all of the contours without establishing any hierarchical relationships.
    - CV_RETR_CCOMP 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.
    - CV_RETR_TREE retrieves all of the contours and reconstructs a full hierarchy of nested contours. This full hierarchy is built and shown in the OpenCV `contours.c` demo.
    `method` - Contour approximation method (if you use Python see also a note below).
    - CV_CHAIN_APPROX_NONE 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`.
    - CV_CHAIN_APPROX_SIMPLE compresses horizontal, vertical, and diagonal segments and leaves only their end points. For example, an up-right rectangular contour is encoded with 4 points.
    - CV_CHAIN_APPROX_TC89_L1,CV_CHAIN_APPROX_TC89_KCOS applies one of the flavors of the Teh-Chin chain approximation algorithm. See [TehChin89] for details.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.findContours

### fitEllipse

    public static RotatedRect fitEllipse(MatOfPoint2f points)

  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 algorithm [Fitzgibbon95] is used.

  Parameters:
    `points` - Input 2D point set, stored in:
    - `std.vector<>` or `Mat` (C++ interface)
    - `CvSeq*` or `CvMat*` (C interface)
    - Nx2 numpy array (Python interface)
  See Also:
    org.opencv.imgproc.Imgproc.fitEllipse

### fitLine

    public static void fitLine(Mat points,
                               Mat line,
                               int distType,
                               double param,
                               double reps,
                               double aeps)

  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 sum_i rho(r_i) where r_i is a distance between the i^(th) point, the line and rho(r) is a distance function, one of the following:

  - distType=CV_DIST_L2

  rho(r) = r^2/2(the simplest and the fastest least-squares method)

  - distType=CV_DIST_L1

  rho(r) = r

  - distType=CV_DIST_L12

  rho(r) = 2 * (sqrt(1 + frac(r^2)2) - 1)

  - distType=CV_DIST_FAIR

  rho(r) = C^2 * ((r)/(C) - log((1 + (r)/(C)))) where C=1.3998

  - distType=CV_DIST_WELSCH

  rho(r) = (C^2)/2 * (1 - exp((-((r)/(C))^2))) where C=2.9846

  - distType=CV_DIST_HUBER

  rho(r) = r^2/2 if r < C; C * (r-C/2) otherwise where C=1.345

  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 w_i are adjusted to be inversely proportional to rho(r_i).

  Parameters:
    `points` - Input vector of 2D or 3D points, stored in `std.vector<>` or `Mat`.
    `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.
    `distType` - Distance used by the M-estimator (see the discussion below).
    `param` - Numerical parameter (`C`) for some types of distances. If it is 0, an optimal value is chosen.
    `reps` - Sufficient accuracy for the radius (distance between the coordinate origin and the line).
    `aeps` - Sufficient accuracy for the angle. 0.01 would be a good default value for `reps` and `aeps`.
  See Also:
    org.opencv.imgproc.Imgproc.fitLine

### floodFill

    public static int floodFill(Mat image,
                                Mat mask,
                                Point seedPoint,
                                Scalar newVal)

  Fills a connected component with the given color.

  The functions `floodFill` fill 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 (x,y) is considered to belong to the repainted domain if:

  - src(x',y')- loDiff <= src(x,y) <= src(x',y')+ upDiff

  in case of a grayscale image and floating range

  - src(seedPoint.x, seedPoint.y)- loDiff <= src(x,y) <= src(seedPoint.x, seedPoint.y)+ upDiff

  in case of a grayscale image and fixed range

  - src(x',y')_r- loDiff _r <= src(x,y)_r <= src(x',y')_r+ upDiff _r,

  src(x',y')_g- loDiff _g <= src(x,y)_g <= src(x',y')_g+ upDiff _g

  and

  src(x',y')_b- loDiff _b <= src(x,y)_b <= src(x',y')_b+ upDiff _b

  in case of a color image and floating range

  - src(seedPoint.x, seedPoint.y)_r- loDiff _r <= src(x,y)_r <= src(seedPoint.x, seedPoint.y)_r+ upDiff _r,

  src(seedPoint.x, seedPoint.y)_g- loDiff _g <= src(x,y)_g <= src(seedPoint.x, seedPoint.y)_g+ upDiff _g

  and

  src(seedPoint.x, seedPoint.y)_b- loDiff _b <= src(x,y)_b <= src(seedPoint.x, seedPoint.y)_b+ upDiff _b

  in case of a color image and fixed range

  where src(x',y') 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. Various modes of the function are demonstrated in the `floodfill.cpp` sample.

  Parameters:
    `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.
    `mask` - (For the second function only) Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller. The function uses and updates the mask, so you take responsibility of initializing the `mask` content. Flood-filling cannot go across non-zero pixels in the mask. For example, an edge detector output can be used as a mask to stop filling at edges. It is possible to use the same mask in multiple calls to the function to make sure the filled area does not overlap.

    Note: Since the mask is larger than the filled image, a pixel (x, y) in `image` corresponds to the pixel (x+1, y+1) in the `mask`.

    `seedPoint` - Starting point.
    `newVal` - New value of the repainted domain pixels.
  See Also:
    org.opencv.imgproc.Imgproc.floodFill, `findContours(org.opencv.core.Mat, java.util.List, org.opencv.core.Mat, int, int, org.opencv.core.Point)`

### floodFill

    public static int floodFill(Mat image,
                                Mat mask,
                                Point seedPoint,
                                Scalar newVal,
                                Rect rect,
                                Scalar loDiff,
                                Scalar upDiff,
                                int flags)

  Fills a connected component with the given color.

  The functions `floodFill` fill 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 (x,y) is considered to belong to the repainted domain if:

  - src(x',y')- loDiff <= src(x,y) <= src(x',y')+ upDiff

  in case of a grayscale image and floating range

  - src(seedPoint.x, seedPoint.y)- loDiff <= src(x,y) <= src(seedPoint.x, seedPoint.y)+ upDiff

  in case of a grayscale image and fixed range

  - src(x',y')_r- loDiff _r <= src(x,y)_r <= src(x',y')_r+ upDiff _r,

  src(x',y')_g- loDiff _g <= src(x,y)_g <= src(x',y')_g+ upDiff _g

  and

  src(x',y')_b- loDiff _b <= src(x,y)_b <= src(x',y')_b+ upDiff _b

  in case of a color image and floating range

  - src(seedPoint.x, seedPoint.y)_r- loDiff _r <= src(x,y)_r <= src(seedPoint.x, seedPoint.y)_r+ upDiff _r,

  src(seedPoint.x, seedPoint.y)_g- loDiff _g <= src(x,y)_g <= src(seedPoint.x, seedPoint.y)_g+ upDiff _g

  and

  src(seedPoint.x, seedPoint.y)_b- loDiff _b <= src(x,y)_b <= src(seedPoint.x, seedPoint.y)_b+ upDiff _b

  in case of a color image and fixed range

  where src(x',y') 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. Various modes of the function are demonstrated in the `floodfill.cpp` sample.

  Parameters:
    `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.
    `mask` - (For the second function only) Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller. The function uses and updates the mask, so you take responsibility of initializing the `mask` content. Flood-filling cannot go across non-zero pixels in the mask. For example, an edge detector output can be used as a mask to stop filling at edges. It is possible to use the same mask in multiple calls to the function to make sure the filled area does not overlap.

    Note: Since the mask is larger than the filled image, a pixel (x, y) in `image` corresponds to the pixel (x+1, y+1) in the `mask`.

    `seedPoint` - Starting point.
    `newVal` - New value of the repainted domain pixels.
    `rect` - Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
    `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.
    `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.
    `flags` - Operation flags. Lower bits contain a connectivity value, 4 (default) or 8, used within the function. Connectivity determines which neighbors of a pixel are considered. Upper bits can be 0 or a combination of the following flags:
    - FLOODFILL_FIXED_RANGE 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_MASK_ONLY If set, the function does not change the image (`newVal` is ignored), but fills the mask. The flag can be used for the second variant only.
  See Also:
    org.opencv.imgproc.Imgproc.floodFill, `findContours(org.opencv.core.Mat, java.util.List, org.opencv.core.Mat, int, int, org.opencv.core.Point)`

### GaussianBlur

    public static void GaussianBlur(Mat src,
                                    Mat dst,
                                    Size ksize,
                                    double sigmaX)

  Blurs an image using a Gaussian filter.

  The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.

  Parameters:
    `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`.
    `dst` - output image of the same size and type as `src`.
    `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*`.
    `sigmaX` - Gaussian kernel standard deviation in X direction.
  See Also:
    org.opencv.imgproc.Imgproc.GaussianBlur, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`

### GaussianBlur

    public static void GaussianBlur(Mat src,
                                    Mat dst,
                                    Size ksize,
                                    double sigmaX,
                                    double sigmaY)

  Blurs an image using a Gaussian filter.

  The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.

  Parameters:
    `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`.
    `dst` - output image of the same size and type as `src`.
    `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*`.
    `sigmaX` - Gaussian kernel standard deviation in X direction.
    `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 "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`.
  See Also:
    org.opencv.imgproc.Imgproc.GaussianBlur, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`

### GaussianBlur

    public static void GaussianBlur(Mat src,
                                    Mat dst,
                                    Size ksize,
                                    double sigmaX,
                                    double sigmaY,
                                    int borderType)

  Blurs an image using a Gaussian filter.

  The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.

  Parameters:
    `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`.
    `dst` - output image of the same size and type as `src`.
    `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*`.
    `sigmaX` - Gaussian kernel standard deviation in X direction.
    `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 "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`.
    `borderType` - pixel extrapolation method (see "borderInterpolate" for details).
  See Also:
    org.opencv.imgproc.Imgproc.GaussianBlur, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `medianBlur(org.opencv.core.Mat, org.opencv.core.Mat, int)`, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`

### getAffineTransform

    public static Mat getAffineTransform(MatOfPoint2f src,
                                         MatOfPoint2f dst)

  Calculates an affine transform from three pairs of the corresponding points.

  The function calculates the 2 x 3 matrix of an affine transform so that:

  x'_i y'_i = map_matrix * x_i y_i 1

  where

  dst(i)=(x'_i,y'_i),<BR>src(i)=(x_i, y_i),<BR>i=0,1,2

  Parameters:
    `src` - Coordinates of triangle vertices in the source image.
    `dst` - Coordinates of the corresponding triangle vertices in the destination image.
  See Also:
    org.opencv.imgproc.Imgproc.getAffineTransform, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `Core.transform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### getDefaultNewCameraMatrix

    public static Mat getDefaultNewCameraMatrix(Mat cameraMatrix)

  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_x 0(imgSize.width -1)*0.5 0 f_y(imgSize.height -1)*0.5 0 0 1,

  where f_x and f_y are (0,0) and (1,1) 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.

  Parameters:
    `cameraMatrix` - Input camera matrix.
  See Also:
    org.opencv.imgproc.Imgproc.getDefaultNewCameraMatrix

### getDefaultNewCameraMatrix

    public static Mat getDefaultNewCameraMatrix(Mat cameraMatrix,
                                                Size imgsize,
                                                boolean centerPrincipalPoint)

  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_x 0(imgSize.width -1)*0.5 0 f_y(imgSize.height -1)*0.5 0 0 1,

  where f_x and f_y are (0,0) and (1,1) 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.

  Parameters:
    `cameraMatrix` - Input camera matrix.
    `imgsize` - Camera view image size in pixels.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.getDefaultNewCameraMatrix

### getDerivKernels

    public static void getDerivKernels(Mat kx,
                                       Mat ky,
                                       int dx,
                                       int dy,
                                       int ksize)

  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 3 x 3 kernels are generated (see "Scharr"). Otherwise, Sobel kernels are generated (see "Sobel"). The filters are normally passed to "sepFilter2D" or to "createSeparableLinearFilter".

  Parameters:
    `kx` - Output matrix of row filter coefficients. It has the type `ktype`.
    `ky` - Output matrix of column filter coefficients. It has the type `ktype`.
    `dx` - Derivative order in respect of x.
    `dy` - Derivative order in respect of y.
    `ksize` - Aperture size. It can be `CV_SCHARR`, 1, 3, 5, or 7.
  See Also:
    org.opencv.imgproc.Imgproc.getDerivKernels

### getDerivKernels

    public static void getDerivKernels(Mat kx,
                                       Mat ky,
                                       int dx,
                                       int dy,
                                       int ksize,
                                       boolean normalize,
                                       int ktype)

  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 3 x 3 kernels are generated (see "Scharr"). Otherwise, Sobel kernels are generated (see "Sobel"). The filters are normally passed to "sepFilter2D" or to "createSeparableLinearFilter".

  Parameters:
    `kx` - Output matrix of row filter coefficients. It has the type `ktype`.
    `ky` - Output matrix of column filter coefficients. It has the type `ktype`.
    `dx` - Derivative order in respect of x.
    `dy` - Derivative order in respect of y.
    `ksize` - Aperture size. It can be `CV_SCHARR`, 1, 3, 5, or 7.
    `normalize` - Flag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator =2^(ksize*2-dx-dy-2). 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`.
    `ktype` - Type of filter coefficients. It can be `CV_32f` or `CV_64F`.
  See Also:
    org.opencv.imgproc.Imgproc.getDerivKernels

### getGaborKernel

    public static Mat getGaborKernel(Size ksize,
                                     double sigma,
                                     double theta,
                                     double lambd,
                                     double gamma)

### getGaborKernel

    public static Mat getGaborKernel(Size ksize,
                                     double sigma,
                                     double theta,
                                     double lambd,
                                     double gamma,
                                     double psi,
                                     int ktype)

### getGaussianKernel

    public static Mat getGaussianKernel(int ksize,
                                        double sigma)

  Returns Gaussian filter coefficients.

  The function computes and returns the ksize x 1 matrix of Gaussian filter coefficients:

  G_i= alpha *e^(-(i-(ksize -1)/2)^2/(2* sigma)^2),

  where i=0..ksize-1 and alpha is the scale factor chosen so that sum_i G_i=1.

  Two of such generated kernels can be passed to "sepFilter2D" or to "createSeparableLinearFilter". 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".

  Parameters:
    `ksize` - Aperture size. It should be odd (ksize mod 2 = 1) and positive.
    `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`.
  See Also:
    org.opencv.imgproc.Imgproc.getGaussianKernel, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `getStructuringElement(int, org.opencv.core.Size, org.opencv.core.Point)`, `getDerivKernels(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, boolean, int)`, `org.opencv.imgproc.Imgproc#createSeparableLinearFilter`

### getGaussianKernel

    public static Mat getGaussianKernel(int ksize,
                                        double sigma,
                                        int ktype)

  Returns Gaussian filter coefficients.

  The function computes and returns the ksize x 1 matrix of Gaussian filter coefficients:

  G_i= alpha *e^(-(i-(ksize -1)/2)^2/(2* sigma)^2),

  where i=0..ksize-1 and alpha is the scale factor chosen so that sum_i G_i=1.

  Two of such generated kernels can be passed to "sepFilter2D" or to "createSeparableLinearFilter". 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".

  Parameters:
    `ksize` - Aperture size. It should be odd (ksize mod 2 = 1) and positive.
    `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`.
    `ktype` - Type of filter coefficients. It can be `CV_32f` or `CV_64F`.
  See Also:
    org.opencv.imgproc.Imgproc.getGaussianKernel, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `getStructuringElement(int, org.opencv.core.Size, org.opencv.core.Point)`, `getDerivKernels(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, boolean, int)`, `org.opencv.imgproc.Imgproc#createSeparableLinearFilter`

### getPerspectiveTransform

    public static Mat getPerspectiveTransform(Mat src,
                                              Mat dst)

  Calculates a perspective transform from four pairs of the corresponding points.

  The function calculates the 3 x 3 matrix of a perspective transform so that:

  t_i x'_i t_i y'_i t_i = map_matrix * x_i y_i 1

  where

  dst(i)=(x'_i,y'_i),<BR>src(i)=(x_i, y_i),<BR>i=0,1,2,3

  Parameters:
    `src` - Coordinates of quadrangle vertices in the source image.
    `dst` - Coordinates of the corresponding quadrangle vertices in the destination image.
  See Also:
    org.opencv.imgproc.Imgproc.getPerspectiveTransform, `Calib3d.findHomography(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f, int, double, org.opencv.core.Mat)`, `Core.perspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`

### getRectSubPix

    public static void getRectSubPix(Mat image,
                                     Size patchSize,
                                     Point center,
                                     Mat patch)

  Retrieves a pixel rectangle from an image with sub-pixel accuracy.

  The function `getRectSubPix` extracts pixels from `src`

  dst(x, y) = src(x + center.x - (dst.cols -1)*0.5, y + center.y - (dst.rows -1)*0.5)

  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 "borderInterpolate") is used to extrapolate the pixel values outside of the image.

  Parameters:
    `image` - a image
    `patchSize` - Size of the extracted patch.
    `center` - Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
    `patch` - a patch
  See Also:
    org.opencv.imgproc.Imgproc.getRectSubPix, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`

### getRectSubPix

    public static void getRectSubPix(Mat image,
                                     Size patchSize,
                                     Point center,
                                     Mat patch,
                                     int patchType)

  Retrieves a pixel rectangle from an image with sub-pixel accuracy.

  The function `getRectSubPix` extracts pixels from `src`

  dst(x, y) = src(x + center.x - (dst.cols -1)*0.5, y + center.y - (dst.rows -1)*0.5)

  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 "borderInterpolate") is used to extrapolate the pixel values outside of the image.

  Parameters:
    `image` - a image
    `patchSize` - Size of the extracted patch.
    `center` - Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
    `patch` - a patch
    `patchType` - Depth of the extracted pixels. By default, they have the same depth as `src`.
  See Also:
    org.opencv.imgproc.Imgproc.getRectSubPix, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`

### getRotationMatrix2D

    public static Mat getRotationMatrix2D(Point center,
                                          double angle,
                                          double scale)

  Calculates an affine matrix of 2D rotation.

  The function calculates the following matrix:

  alpha beta(1- alpha) * center.x - beta * center.y - beta alpha beta * center.x + (1- alpha) * center.y

  where

  alpha = scale * cos angle, beta = scale * sin angle

  The transformation maps the rotation center to itself. If this is not the target, adjust the shift.

  Parameters:
    `center` - Center of the rotation in the source image.
    `angle` - Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).
    `scale` - Isotropic scale factor.
  See Also:
    org.opencv.imgproc.Imgproc.getRotationMatrix2D, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `getAffineTransform(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f)`, `Core.transform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### getStructuringElement

    public static Mat getStructuringElement(int shape,
                                            Size ksize)

  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 "createMorphologyFilter", "erode", "dilate" or "morphologyEx". But you can also construct an arbitrary binary mask yourself and use it as the structuring element.

  Note: When using OpenCV 1.x C API, the created structuring element `IplConvKernel* element` must be released in the end using `cvReleaseStructuringElement(&element)`.

  Parameters:
    `shape` - Element shape that could be one of the following:
    - MORPH_RECT - a rectangular structuring element:

    E_(ij)=1

    - MORPH_ELLIPSE - an elliptic structuring element, that is, a filled ellipse inscribed into the rectangle `Rect(0, 0, esize.width, 0.esize.height)`
    - MORPH_CROSS - a cross-shaped structuring element:

    E_(ij) = 1 if i=anchor.y or j=anchor.x; 0 otherwise

    - CV_SHAPE_CUSTOM - custom structuring element (OpenCV 1.x API)
    `ksize` - Size of the structuring element.
  See Also:
    org.opencv.imgproc.Imgproc.getStructuringElement

### getStructuringElement

    public static Mat getStructuringElement(int shape,
                                            Size ksize,
                                            Point anchor)

  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 "createMorphologyFilter", "erode", "dilate" or "morphologyEx". But you can also construct an arbitrary binary mask yourself and use it as the structuring element.

  Note: When using OpenCV 1.x C API, the created structuring element `IplConvKernel* element` must be released in the end using `cvReleaseStructuringElement(&element)`.

  Parameters:
    `shape` - Element shape that could be one of the following:
    - MORPH_RECT - a rectangular structuring element:

    E_(ij)=1

    - MORPH_ELLIPSE - an elliptic structuring element, that is, a filled ellipse inscribed into the rectangle `Rect(0, 0, esize.width, 0.esize.height)`
    - MORPH_CROSS - a cross-shaped structuring element:

    E_(ij) = 1 if i=anchor.y or j=anchor.x; 0 otherwise

    - CV_SHAPE_CUSTOM - custom structuring element (OpenCV 1.x API)
    `ksize` - Size of the structuring element.
    `anchor` - Anchor position within the element. The default value (-1, -1) 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.
  See Also:
    org.opencv.imgproc.Imgproc.getStructuringElement

### goodFeaturesToTrack

    public static void goodFeaturesToTrack(Mat image,
                                           MatOfPoint corners,
                                           int maxCorners,
                                           double qualityLevel,
                                           double minDistance)

  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 [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 qualityLevel * max_(x,y) qualityMeasureMap(x,y) 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`.

  Parameters:
    `image` - Input 8-bit or floating-point 32-bit, single-channel image.
    `corners` - Output vector of detected corners.
    `maxCorners` - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
    `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.
    `minDistance` - Minimum possible Euclidean distance between the returned corners.
  See Also:
    org.opencv.imgproc.Imgproc.goodFeaturesToTrack, `cornerHarris(org.opencv.core.Mat, org.opencv.core.Mat, int, int, double, int)`, `Video.estimateRigidTransform(org.opencv.core.Mat, org.opencv.core.Mat, boolean)`, `cornerMinEigenVal(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int)`, `Video.calcOpticalFlowPyrLK(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfByte, org.opencv.core.MatOfFloat, org.opencv.core.Size, int, org.opencv.core.TermCriteria, int, double)`

### goodFeaturesToTrack

    public static void goodFeaturesToTrack(Mat image,
                                           MatOfPoint corners,
                                           int maxCorners,
                                           double qualityLevel,
                                           double minDistance,
                                           Mat mask,
                                           int blockSize,
                                           boolean useHarrisDetector,
                                           double k)

  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 [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 qualityLevel * max_(x,y) qualityMeasureMap(x,y) 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`.

  Parameters:
    `image` - Input 8-bit or floating-point 32-bit, single-channel image.
    `corners` - Output vector of detected corners.
    `maxCorners` - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
    `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.
    `minDistance` - Minimum possible Euclidean distance between the returned corners.
    `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.
    `blockSize` - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See "cornerEigenValsAndVecs".
    `useHarrisDetector` - Parameter indicating whether to use a Harris detector (see "cornerHarris") or "cornerMinEigenVal".
    `k` - Free parameter of the Harris detector.
  See Also:
    org.opencv.imgproc.Imgproc.goodFeaturesToTrack, `cornerHarris(org.opencv.core.Mat, org.opencv.core.Mat, int, int, double, int)`, `Video.estimateRigidTransform(org.opencv.core.Mat, org.opencv.core.Mat, boolean)`, `cornerMinEigenVal(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int)`, `Video.calcOpticalFlowPyrLK(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfByte, org.opencv.core.MatOfFloat, org.opencv.core.Size, int, org.opencv.core.TermCriteria, int, double)`

### grabCut

    public static void grabCut(Mat img,
                               Mat mask,
                               Rect rect,
                               Mat bgdModel,
                               Mat fgdModel,
                               int iterCount)

  Runs the GrabCut algorithm.

  The function implements the GrabCut image segmentation algorithm (http://en.wikipedia.org/wiki/GrabCut). See the sample `grabcut.cpp` to learn how to use the function.

  Parameters:
    `img` - Input 8-bit 3-channel image.
    `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 following values:
    - GC_BGD defines an obvious background pixels.
    - GC_FGD defines an obvious foreground (object) pixel.
    - GC_PR_BGD defines a possible background pixel.
    - GC_PR_BGD defines a possible foreground pixel.
    `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`.
    `bgdModel` - Temporary array for the background model. Do not modify it while you are processing the same image.
    `fgdModel` - Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
    `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`.
  See Also:
    org.opencv.imgproc.Imgproc.grabCut

### grabCut

    public static void grabCut(Mat img,
                               Mat mask,
                               Rect rect,
                               Mat bgdModel,
                               Mat fgdModel,
                               int iterCount,
                               int mode)

  Runs the GrabCut algorithm.

  The function implements the GrabCut image segmentation algorithm (http://en.wikipedia.org/wiki/GrabCut). See the sample `grabcut.cpp` to learn how to use the function.

  Parameters:
    `img` - Input 8-bit 3-channel image.
    `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 following values:
    - GC_BGD defines an obvious background pixels.
    - GC_FGD defines an obvious foreground (object) pixel.
    - GC_PR_BGD defines a possible background pixel.
    - GC_PR_BGD defines a possible foreground pixel.
    `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`.
    `bgdModel` - Temporary array for the background model. Do not modify it while you are processing the same image.
    `fgdModel` - Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
    `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`.
    `mode` - Operation mode that could be one of the following:
    - GC_INIT_WITH_RECT The function initializes the state and the mask using the provided rectangle. After that it runs `iterCount` iterations of the algorithm.
    - GC_INIT_WITH_MASK 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_EVAL The value means that the algorithm should just resume.
  See Also:
    org.opencv.imgproc.Imgproc.grabCut

### HoughCircles

    public static void HoughCircles(Mat image,
                                    Mat circles,
                                    int method,
                                    double dp,
                                    double minDist)

  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: ``

  // C++ code:

  #include

  using namespace cv;

  int main(int argc, char argv)

  Mat img, gray;

  if(argc != 2 && !(img=imread(argv[1], 1)).data)

  return -1;

  cvtColor(img, gray, CV_BGR2GRAY);

  // smooth it, otherwise a lot of false circles may be detected

  GaussianBlur(gray, gray, Size(9, 9), 2, 2);

  vector circles;

  HoughCircles(gray, circles, CV_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);

  return 0;

  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.

  Parameters:
    `image` - 8-bit, single-channel, grayscale input image.
    `circles` - Output vector of found circles. Each vector is encoded as a 3-element floating-point vector (x, y, radius).
    `method` - Detection method to use. Currently, the only implemented method is `CV_HOUGH_GRADIENT`, which is basically *21HT*, described in [Yuen90].
    `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.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.HoughCircles, `minEnclosingCircle(org.opencv.core.MatOfPoint2f, org.opencv.core.Point, float[])`, `fitEllipse(org.opencv.core.MatOfPoint2f)`

### HoughCircles

    public static void HoughCircles(Mat image,
                                    Mat circles,
                                    int method,
                                    double dp,
                                    double minDist,
                                    double param1,
                                    double param2,
                                    int minRadius,
                                    int maxRadius)

  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: ``

  // C++ code:

  #include

  using namespace cv;

  int main(int argc, char argv)

  Mat img, gray;

  if(argc != 2 && !(img=imread(argv[1], 1)).data)

  return -1;

  cvtColor(img, gray, CV_BGR2GRAY);

  // smooth it, otherwise a lot of false circles may be detected

  GaussianBlur(gray, gray, Size(9, 9), 2, 2);

  vector circles;

  HoughCircles(gray, circles, CV_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);

  return 0;

  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.

  Parameters:
    `image` - 8-bit, single-channel, grayscale input image.
    `circles` - Output vector of found circles. Each vector is encoded as a 3-element floating-point vector (x, y, radius).
    `method` - Detection method to use. Currently, the only implemented method is `CV_HOUGH_GRADIENT`, which is basically *21HT*, described in [Yuen90].
    `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.
    `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.
    `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).
    `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.
    `minRadius` - Minimum circle radius.
    `maxRadius` - Maximum circle radius.
  See Also:
    org.opencv.imgproc.Imgproc.HoughCircles, `minEnclosingCircle(org.opencv.core.MatOfPoint2f, org.opencv.core.Point, float[])`, `fitEllipse(org.opencv.core.MatOfPoint2f)`

### HoughLines

    public static void HoughLines(Mat image,
                                  Mat lines,
                                  double rho,
                                  double theta,
                                  int threshold)

  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. See also the example in "HoughLinesP" description.

  Parameters:
    `image` - 8-bit, single-channel binary source image. The image may be modified by the function.
    `lines` - Output vector of lines. Each line is represented by a two-element vector (rho, theta). rho is the distance from the coordinate origin (0,0) (top-left corner of the image). theta is the line rotation angle in radians (0 ~ vertical line, pi/2 ~ horizontal line).
    `rho` - Distance resolution of the accumulator in pixels.
    `theta` - Angle resolution of the accumulator in radians.
    `threshold` - Accumulator threshold parameter. Only those lines are returned that get enough votes (>threshold).
  See Also:
    org.opencv.imgproc.Imgproc.HoughLines

### HoughLines

    public static void HoughLines(Mat image,
                                  Mat lines,
                                  double rho,
                                  double theta,
                                  int threshold,
                                  double srn,
                                  double stn)

  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. See also the example in "HoughLinesP" description.

  Parameters:
    `image` - 8-bit, single-channel binary source image. The image may be modified by the function.
    `lines` - Output vector of lines. Each line is represented by a two-element vector (rho, theta). rho is the distance from the coordinate origin (0,0) (top-left corner of the image). theta is the line rotation angle in radians (0 ~ vertical line, pi/2 ~ horizontal line).
    `rho` - Distance resolution of the accumulator in pixels.
    `theta` - Angle resolution of the accumulator in radians.
    `threshold` - Accumulator threshold parameter. Only those lines are returned that get enough votes (>threshold).
    `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.
    `stn` - For the multi-scale Hough transform, it is a divisor for the distance resolution `theta`.
  See Also:
    org.opencv.imgproc.Imgproc.HoughLines

### HoughLinesP

    public static void HoughLinesP(Mat image,
                                   Mat lines,
                                   double rho,
                                   double theta,
                                   int threshold)

  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[Matas00]. See the line detection example below: ``

  // C++ code:

  / * This is a standalone program. Pass an image name as the first parameter

  of the program. Switch between standard and probabilistic Hough transform

  by changing "#if 1" to "#if 0" and back * /

  #include

  using namespace cv;

  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, CV_GRAY2BGR);

  #if 0

  vector lines;

  HoughLines(dst, lines, 1, CV_PI/180, 100);

  for(size_t i = 0; i < lines.size(); i++)

  float rho = lines[i][0];

  float theta = lines[i][1];

  double a = cos(theta), b = sin(theta);

  double x0 = a*rho, y0 = b*rho;

  Point pt1(cvRound(x0 + 1000*(-b)),

  cvRound(y0 + 1000*(a)));

  Point pt2(cvRound(x0 - 1000*(-b)),

  cvRound(y0 - 1000*(a)));

  line(color_dst, pt1, pt2, Scalar(0,0,255), 3, 8);

  #else

  vector lines;

  HoughLinesP(dst, lines, 1, CV_PI/180, 80, 30, 10);

  for(size_t i = 0; i < lines.size(); i++)

  line(color_dst, Point(lines[i][0], lines[i][1]),

  Point(lines[i][2], lines[i][3]), Scalar(0,0,255), 3, 8);

  #endif

  namedWindow("Source", 1);

  imshow("Source", src);

  namedWindow("Detected Lines", 1);

  imshow("Detected Lines", color_dst);

  waitKey(0);

  return 0;

  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:

  Parameters:
    `image` - 8-bit, single-channel binary source image. The image may be modified by the function.
    `lines` - Output vector of lines. Each line is represented by a 4-element vector (x_1, y_1, x_2, y_2), where (x_1,y_1) and (x_2, y_2) are the ending points of each detected line segment.
    `rho` - Distance resolution of the accumulator in pixels.
    `theta` - Angle resolution of the accumulator in radians.
    `threshold` - Accumulator threshold parameter. Only those lines are returned that get enough votes (>threshold).
  See Also:
    org.opencv.imgproc.Imgproc.HoughLinesP

### HoughLinesP

    public static void HoughLinesP(Mat image,
                                   Mat lines,
                                   double rho,
                                   double theta,
                                   int threshold,
                                   double minLineLength,
                                   double maxLineGap)

  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[Matas00]. See the line detection example below: ``

  // C++ code:

  / * This is a standalone program. Pass an image name as the first parameter

  of the program. Switch between standard and probabilistic Hough transform

  by changing "#if 1" to "#if 0" and back * /

  #include

  using namespace cv;

  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, CV_GRAY2BGR);

  #if 0

  vector lines;

  HoughLines(dst, lines, 1, CV_PI/180, 100);

  for(size_t i = 0; i < lines.size(); i++)

  float rho = lines[i][0];

  float theta = lines[i][1];

  double a = cos(theta), b = sin(theta);

  double x0 = a*rho, y0 = b*rho;

  Point pt1(cvRound(x0 + 1000*(-b)),

  cvRound(y0 + 1000*(a)));

  Point pt2(cvRound(x0 - 1000*(-b)),

  cvRound(y0 - 1000*(a)));

  line(color_dst, pt1, pt2, Scalar(0,0,255), 3, 8);

  #else

  vector lines;

  HoughLinesP(dst, lines, 1, CV_PI/180, 80, 30, 10);

  for(size_t i = 0; i < lines.size(); i++)

  line(color_dst, Point(lines[i][0], lines[i][1]),

  Point(lines[i][2], lines[i][3]), Scalar(0,0,255), 3, 8);

  #endif

  namedWindow("Source", 1);

  imshow("Source", src);

  namedWindow("Detected Lines", 1);

  imshow("Detected Lines", color_dst);

  waitKey(0);

  return 0;

  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:

  Parameters:
    `image` - 8-bit, single-channel binary source image. The image may be modified by the function.
    `lines` - Output vector of lines. Each line is represented by a 4-element vector (x_1, y_1, x_2, y_2), where (x_1,y_1) and (x_2, y_2) are the ending points of each detected line segment.
    `rho` - Distance resolution of the accumulator in pixels.
    `theta` - Angle resolution of the accumulator in radians.
    `threshold` - Accumulator threshold parameter. Only those lines are returned that get enough votes (>threshold).
    `minLineLength` - Minimum line length. Line segments shorter than that are rejected.
    `maxLineGap` - Maximum allowed gap between points on the same line to link them.
  See Also:
    org.opencv.imgproc.Imgproc.HoughLinesP

### HuMoments

    public static void HuMoments(Moments m,
                                 Mat hu)

  Calculates seven Hu invariants.

  The function calculates seven Hu invariants (introduced in [Hu62]; see also http://en.wikipedia.org/wiki/Image_moment) defined as:

  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]

  where eta_(ji) stands for Moments.nu_(ji).

  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.

  Parameters:
    `m` - a m
    `hu` - Output Hu invariants.
  See Also:
    org.opencv.imgproc.Imgproc.HuMoments, `matchShapes(org.opencv.core.Mat, org.opencv.core.Mat, int, double)`

### initUndistortRectifyMap

    public static void initUndistortRectifyMap(Mat cameraMatrix,
                                               Mat distCoeffs,
                                               Mat R,
                                               Mat newCameraMatrix,
                                               Size size,
                                               int m1type,
                                               Mat map1,
                                               Mat map2)

  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 "getOptimalNewCameraMatrix" for a better control over scaling. In case of a stereo camera, `newCameraMatrix` is normally set to `P1` or `P2` computed by "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 (u, v) 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:

  x <- (u - (c')_x)/(f')_x y <- (v - (c')_y)/(f')_y ([X Y W]) ^T <- R^(-1)*[x y 1]^T x' <- X/W y' <- Y/W x" <- x' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + 2p_1 x' y' + p_2(r^2 + 2 x'^2) y" <- y' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + p_1(r^2 + 2 y'^2) + 2 p_2 x' y' map_x(u,v) <- x" f_x + c_x map_y(u,v) <- y" f_y + c_y

  where (k_1, k_2, p_1, p_2[, k_3]) 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 "stereoCalibrate". But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using "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

  R = cameraMatrix ^(-1) * H * cameraMatrix

  where `cameraMatrix` can be chosen arbitrarily.

  Parameters:
    `cameraMatrix` - Input camera matrix A=

    |f_x 0 c_x| |0 f_y c_y| |0 0 1|

    .
    `distCoeffs` - Input vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
    `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.
    `newCameraMatrix` - New camera matrix A'=

    |f_x' 0 c_x'| |0 f_y' c_y'| |0 0 1|

    .
    `size` - Undistorted image size.
    `m1type` - Type of the first output map that can be `CV_32FC1` or `CV_16SC2`. See "convertMaps" for details.
    `map1` - The first output map.
    `map2` - The second output map.
  See Also:
    org.opencv.imgproc.Imgproc.initUndistortRectifyMap

### initWideAngleProjMap

    public static float initWideAngleProjMap(Mat cameraMatrix,
                                             Mat distCoeffs,
                                             Size imageSize,
                                             int destImageWidth,
                                             int m1type,
                                             Mat map1,
                                             Mat map2)

### initWideAngleProjMap

    public static float initWideAngleProjMap(Mat cameraMatrix,
                                             Mat distCoeffs,
                                             Size imageSize,
                                             int destImageWidth,
                                             int m1type,
                                             Mat map1,
                                             Mat map2,
                                             int projType,
                                             double alpha)

### integral

    public static void integral(Mat src,
                                Mat sum)

  Calculates the integral of an image.

  The functions calculate one or more integral images for the source image as follows:

  sum(X,Y) = sum(by: x<X,y<Y) image(x,y)

  sqsum(X,Y) = sum(by: x<X,y<Y) image(x,y)^2

  tilted(X,Y) = sum(by: y<Y,abs(x-X+1) <= Y-y-1) image(x,y)

  Using these integral images, you can calculate sa um, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:

  sum(by: x_1 <= x < x_2, y_1 <= y < y_2) image(x,y) = sum(x_2,y_2)- sum(x_1,y_2)- sum(x_2,y_1)+ sum(x_1,y_1)

  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`.

  Parameters:
    `src` - a src
    `sum` - integral image as (W+1)x(H+1), 32-bit integer or floating-point (32f or 64f).
  See Also:
    org.opencv.imgproc.Imgproc.integral

### integral

    public static void integral(Mat src,
                                Mat sum,
                                int sdepth)

  Calculates the integral of an image.

  The functions calculate one or more integral images for the source image as follows:

  sum(X,Y) = sum(by: x<X,y<Y) image(x,y)

  sqsum(X,Y) = sum(by: x<X,y<Y) image(x,y)^2

  tilted(X,Y) = sum(by: y<Y,abs(x-X+1) <= Y-y-1) image(x,y)

  Using these integral images, you can calculate sa um, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:

  sum(by: x_1 <= x < x_2, y_1 <= y < y_2) image(x,y) = sum(x_2,y_2)- sum(x_1,y_2)- sum(x_2,y_1)+ sum(x_1,y_1)

  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`.

  Parameters:
    `src` - a src
    `sum` - integral image as (W+1)x(H+1), 32-bit integer or floating-point (32f or 64f).
    `sdepth` - desired depth of the integral and the tilted integral images, `CV_32S`, `CV_32F`, or `CV_64F`.
  See Also:
    org.opencv.imgproc.Imgproc.integral

### integral2

    public static void integral2(Mat src,
                                 Mat sum,
                                 Mat sqsum)

  Calculates the integral of an image.

  The functions calculate one or more integral images for the source image as follows:

  sum(X,Y) = sum(by: x<X,y<Y) image(x,y)

  sqsum(X,Y) = sum(by: x<X,y<Y) image(x,y)^2

  tilted(X,Y) = sum(by: y<Y,abs(x-X+1) <= Y-y-1) image(x,y)

  Using these integral images, you can calculate sa um, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:

  sum(by: x_1 <= x < x_2, y_1 <= y < y_2) image(x,y) = sum(x_2,y_2)- sum(x_1,y_2)- sum(x_2,y_1)+ sum(x_1,y_1)

  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`.

  Parameters:
    `src` - a src
    `sum` - integral image as (W+1)x(H+1), 32-bit integer or floating-point (32f or 64f).
    `sqsum` - integral image for squared pixel values; it is (W+1)x(H+1), double-precision floating-point (64f) array.
  See Also:
    org.opencv.imgproc.Imgproc.integral

### integral2

    public static void integral2(Mat src,
                                 Mat sum,
                                 Mat sqsum,
                                 int sdepth)

  Calculates the integral of an image.

  The functions calculate one or more integral images for the source image as follows:

  sum(X,Y) = sum(by: x<X,y<Y) image(x,y)

  sqsum(X,Y) = sum(by: x<X,y<Y) image(x,y)^2

  tilted(X,Y) = sum(by: y<Y,abs(x-X+1) <= Y-y-1) image(x,y)

  Using these integral images, you can calculate sa um, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:

  sum(by: x_1 <= x < x_2, y_1 <= y < y_2) image(x,y) = sum(x_2,y_2)- sum(x_1,y_2)- sum(x_2,y_1)+ sum(x_1,y_1)

  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`.

  Parameters:
    `src` - a src
    `sum` - integral image as (W+1)x(H+1), 32-bit integer or floating-point (32f or 64f).
    `sqsum` - integral image for squared pixel values; it is (W+1)x(H+1), double-precision floating-point (64f) array.
    `sdepth` - desired depth of the integral and the tilted integral images, `CV_32S`, `CV_32F`, or `CV_64F`.
  See Also:
    org.opencv.imgproc.Imgproc.integral

### integral3

    public static void integral3(Mat src,
                                 Mat sum,
                                 Mat sqsum,
                                 Mat tilted)

  Calculates the integral of an image.

  The functions calculate one or more integral images for the source image as follows:

  sum(X,Y) = sum(by: x<X,y<Y) image(x,y)

  sqsum(X,Y) = sum(by: x<X,y<Y) image(x,y)^2

  tilted(X,Y) = sum(by: y<Y,abs(x-X+1) <= Y-y-1) image(x,y)

  Using these integral images, you can calculate sa um, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:

  sum(by: x_1 <= x < x_2, y_1 <= y < y_2) image(x,y) = sum(x_2,y_2)- sum(x_1,y_2)- sum(x_2,y_1)+ sum(x_1,y_1)

  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`.

  Parameters:
    `src` - a src
    `sum` - integral image as (W+1)x(H+1), 32-bit integer or floating-point (32f or 64f).
    `sqsum` - integral image for squared pixel values; it is (W+1)x(H+1), double-precision floating-point (64f) array.
    `tilted` - integral for the image rotated by 45 degrees; it is (W+1)x(H+1) array with the same data type as `sum`.
  See Also:
    org.opencv.imgproc.Imgproc.integral

### integral3

    public static void integral3(Mat src,
                                 Mat sum,
                                 Mat sqsum,
                                 Mat tilted,
                                 int sdepth)

  Calculates the integral of an image.

  The functions calculate one or more integral images for the source image as follows:

  sum(X,Y) = sum(by: x<X,y<Y) image(x,y)

  sqsum(X,Y) = sum(by: x<X,y<Y) image(x,y)^2

  tilted(X,Y) = sum(by: y<Y,abs(x-X+1) <= Y-y-1) image(x,y)

  Using these integral images, you can calculate sa um, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:

  sum(by: x_1 <= x < x_2, y_1 <= y < y_2) image(x,y) = sum(x_2,y_2)- sum(x_1,y_2)- sum(x_2,y_1)+ sum(x_1,y_1)

  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`.

  Parameters:
    `src` - a src
    `sum` - integral image as (W+1)x(H+1), 32-bit integer or floating-point (32f or 64f).
    `sqsum` - integral image for squared pixel values; it is (W+1)x(H+1), double-precision floating-point (64f) array.
    `tilted` - integral for the image rotated by 45 degrees; it is (W+1)x(H+1) array with the same data type as `sum`.
    `sdepth` - desired depth of the integral and the tilted integral images, `CV_32S`, `CV_32F`, or `CV_64F`.
  See Also:
    org.opencv.imgproc.Imgproc.integral

### intersectConvexConvex

    public static float intersectConvexConvex(Mat _p1,
                                              Mat _p2,
                                              Mat _p12)

### intersectConvexConvex

    public static float intersectConvexConvex(Mat _p1,
                                              Mat _p2,
                                              Mat _p12,
                                              boolean handleNested)

### invertAffineTransform

    public static void invertAffineTransform(Mat M,
                                             Mat iM)

  Inverts an affine transformation.

  The function computes an inverse affine transformation represented by 2 x 3 matrix `M` :

  a_11 a_12 b_1 a_21 a_22 b_2

  The result is also a 2 x 3 matrix of the same type as `M`.

  Parameters:
    `M` - Original affine transformation.
    `iM` - Output reverse affine transformation.
  See Also:
    org.opencv.imgproc.Imgproc.invertAffineTransform

### isContourConvex

    public static boolean isContourConvex(MatOfPoint contour)

  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.

  Parameters:
    `contour` - Input vector of 2D points, stored in:
    - `std.vector<>` or `Mat` (C++ interface)
    - `CvSeq*` or `CvMat*` (C interface)
    - Nx2 numpy array (Python interface)
  See Also:
    org.opencv.imgproc.Imgproc.isContourConvex

### Laplacian

    public static void Laplacian(Mat src,
                                 Mat dst,
                                 int ddepth)

  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:

  dst = Delta src = (d^2 src)/(dx^2) + (d^2 src)/(dy^2)

  This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image with the following 3 x 3 aperture:

  vecthreethree 0101(-4)1010

  Parameters:
    `src` - Source image.
    `dst` - Destination image of the same size and the same number of channels as `src`.
    `ddepth` - Desired depth of the destination image.
  See Also:
    org.opencv.imgproc.Imgproc.Laplacian, `Scharr(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, double, double, int)`, `Sobel(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, int, double, double, int)`

### Laplacian

    public static void Laplacian(Mat src,
                                 Mat dst,
                                 int ddepth,
                                 int ksize,
                                 double scale,
                                 double delta)

  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:

  dst = Delta src = (d^2 src)/(dx^2) + (d^2 src)/(dy^2)

  This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image with the following 3 x 3 aperture:

  vecthreethree 0101(-4)1010

  Parameters:
    `src` - Source image.
    `dst` - Destination image of the same size and the same number of channels as `src`.
    `ddepth` - Desired depth of the destination image.
    `ksize` - Aperture size used to compute the second-derivative filters. See "getDerivKernels" for details. The size must be positive and odd.
    `scale` - Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See "getDerivKernels" for details.
    `delta` - Optional delta value that is added to the results prior to storing them in `dst`.
  See Also:
    org.opencv.imgproc.Imgproc.Laplacian, `Scharr(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, double, double, int)`, `Sobel(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, int, double, double, int)`

### Laplacian

    public static void Laplacian(Mat src,
                                 Mat dst,
                                 int ddepth,
                                 int ksize,
                                 double scale,
                                 double delta,
                                 int borderType)

  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:

  dst = Delta src = (d^2 src)/(dx^2) + (d^2 src)/(dy^2)

  This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image with the following 3 x 3 aperture:

  vecthreethree 0101(-4)1010

  Parameters:
    `src` - Source image.
    `dst` - Destination image of the same size and the same number of channels as `src`.
    `ddepth` - Desired depth of the destination image.
    `ksize` - Aperture size used to compute the second-derivative filters. See "getDerivKernels" for details. The size must be positive and odd.
    `scale` - Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See "getDerivKernels" for details.
    `delta` - Optional delta value that is added to the results prior to storing them in `dst`.
    `borderType` - Pixel extrapolation method. See "borderInterpolate" for details.
  See Also:
    org.opencv.imgproc.Imgproc.Laplacian, `Scharr(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, double, double, int)`, `Sobel(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, int, double, double, int)`

### matchShapes

    public static double matchShapes(Mat contour1,
                                     Mat contour2,
                                     int method,
                                     double parameter)

  Compares two shapes.

  The function compares two shapes. All three implemented methods use the Hu invariants (see "HuMoments") as follows (A denotes `object1`,B denotes `object2`):

  - method=CV_CONTOURS_MATCH_I1

  I_1(A,B) = sum(by: i=1...7) <= ft|1/(m^A_i) - 1/(m^B_i) right|

  - method=CV_CONTOURS_MATCH_I2

  I_2(A,B) = sum(by: i=1...7) <= ft|m^A_i - m^B_i right|

  - method=CV_CONTOURS_MATCH_I3

  I_3(A,B) = max _(i=1...7)(<= ft| m^A_i - m^B_i right|)/(<= ft| m^A_i right|)

  where

  m^A_i = sign(h^A_i) * log(h^A_i) m^B_i = sign(h^B_i) * log(h^B_i)

  and h^A_i, h^B_i are the Hu moments of A and B, respectively.

  Parameters:
    `contour1` - a contour1
    `contour2` - a contour2
    `method` - Comparison method: `CV_CONTOURS_MATCH_I1`, `CV_CONTOURS_MATCH_I2` \

    or `CV_CONTOURS_MATCH_I3` (see the details below).

    `parameter` - Method-specific parameter (not supported now).
  See Also:
    org.opencv.imgproc.Imgproc.matchShapes

### matchTemplate

    public static void matchTemplate(Mat image,
                                     Mat templ,
                                     Mat result,
                                     int method)

  Compares a template against overlapped image regions.

  The function slides through `image`, compares the overlapped patches of size w x h against `templ` using the specified method and stores the comparison results in `result`. Here are the formulae for the available comparison methods (I denotes `image`, T `template`, R `result`). The summation is done over template and/or the image patch: x' = 0...w-1, y' = 0...h-1

  - method=CV_TM_SQDIFF

  R(x,y)= sum(by: x',y')(T(x',y')-I(x+x',y+y'))^2

  - method=CV_TM_SQDIFF_NORMED

  R(x,y)= (sum_(x',y')(T(x',y')-I(x+x',y+y'))^2)/(sqrt(sum_(x',y')T(x',y')^2 * sum_(x',y') I(x+x',y+y')^2))

  - method=CV_TM_CCORR

  R(x,y)= sum(by: x',y')(T(x',y') * I(x+x',y+y'))

  - method=CV_TM_CCORR_NORMED

  R(x,y)= (sum_(x',y')(T(x',y') * I(x+x',y+y')))/(sqrt(sum_(x',y')T(x',y')^2 * sum_(x',y') I(x+x',y+y')^2))

  - method=CV_TM_CCOEFF

  R(x,y)= sum(by: x',y')(T'(x',y') * I'(x+x',y+y'))

  where

  T'(x',y')=T(x',y') - 1/(w * h) * sum(by: x'',y'') T(x'',y'') I'(x+x',y+y')=I(x+x',y+y') - 1/(w * h) * sum(by: x'',y'') I(x+x'',y+y'')

  - method=CV_TM_CCOEFF_NORMED

  R(x,y)= (sum_(x',y')(T'(x',y') * I'(x+x',y+y')))/(sqrt(sum_(x',y')T'(x',y')^2 * sum_(x',y') I'(x+x',y+y')^2))

  After the function finishes the comparison, the best matches can be found as global minimums (when `CV_TM_SQDIFF` was used) or maximums (when `CV_TM_CCORR` or `CV_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.

  Parameters:
    `image` - Image where the search is running. It must be 8-bit or 32-bit floating-point.
    `templ` - Searched template. It must be not greater than the source image and have the same data type.
    `result` - Map of comparison results. It must be single-channel 32-bit floating-point. If `image` is W x H and `templ` is w x h, then `result` is (W-w+1) x(H-h+1).
    `method` - Parameter specifying the comparison method (see below).
  See Also:
    org.opencv.imgproc.Imgproc.matchTemplate

### medianBlur

    public static void medianBlur(Mat src,
                                  Mat dst,
                                  int ksize)

  Blurs an image using the median filter.

  The function smoothes an image using the median filter with the ksize x ksize aperture. Each channel of a multi-channel image is processed independently. In-place operation is supported.

  Parameters:
    `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`.
    `dst` - destination array of the same size and type as `src`.
    `ksize` - aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7...
  See Also:
    org.opencv.imgproc.Imgproc.medianBlur, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `bilateralFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, double, double, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`

### minAreaRect

    public static RotatedRect minAreaRect(MatOfPoint2f points)

  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`.

  Parameters:
    `points` - Input vector of 2D points, stored in:
    - `std.vector<>` or `Mat` (C++ interface)
    - `CvSeq*` or `CvMat*` (C interface)
    - Nx2 numpy array (Python interface)
  See Also:
    org.opencv.imgproc.Imgproc.minAreaRect

### minEnclosingCircle

    public static void minEnclosingCircle(MatOfPoint2f points,
                                          Point center,
                                          float[] radius)

  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`.

  Parameters:
    `points` - Input vector of 2D points, stored in:
    - `std.vector<>` or `Mat` (C++ interface)
    - `CvSeq*` or `CvMat*` (C interface)
    - Nx2 numpy array (Python interface)
    `center` - Output center of the circle.
    `radius` - Output radius of the circle.
  See Also:
    org.opencv.imgproc.Imgproc.minEnclosingCircle

### moments

    public static Moments moments(Mat array)

  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 `Moments` defined as: ``

  // C++ code:

  class Moments

  public:

  Moments();

  Moments(double m00, double m10, double m01, double m20, double m11,

  double m02, double m30, double m21, double m12, double m03);

  Moments(const CvMoments& moments);

  operator CvMoments() const;

  // spatial moments

  double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;

  // central moments

  double mu20, mu11, mu02, mu30, mu21, mu12, mu03;

  // central normalized moments

  double nu20, nu11, nu02, nu30, nu21, nu12, nu03;

  In case of a raster image, the spatial moments Moments.m_(ji) are computed as:

  m _(ji)= sum(by: x,y)(array(x,y) * x^j * y^i)

  The central moments Moments.mu_(ji) are computed as:

  mu _(ji)= sum(by: x,y)(array(x,y) * (x - x")^j * (y - y")^i)

  where (x", y") is the mass center:

  x" = (m_10)/(m_(00)), y" = (m_01)/(m_(00))

  The normalized central moments Moments.nu_(ij) are computed as:

  nu _(ji)= (mu_(ji))/(m_(00)^((i+j)/2+1)).

  Note:

  mu_00=m_00, nu_00=1 nu_10=mu_10=mu_01=mu_10=0, hence the values are not stored.

  The moments of a contour are defined in the same way but computed using the Green's formula (see http://en.wikipedia.org/wiki/Green_theorem). So, due to a limited raster resolution, the moments computed for a contour are slightly different from the moments computed for the same rasterized contour.

  Note:

  Since the contour moments are computed using Green formula, you may get seemingly odd results for contours with self-intersections, e.g. a zero area (`m00`) for butterfly-shaped contours.

  Parameters:
    `array` - Raster image (single-channel, 8-bit or floating-point 2D array) or an array (1 x N or N x 1) of 2D points (`Point` or `Point2f`).
  See Also:
    org.opencv.imgproc.Imgproc.moments, `contourArea(org.opencv.core.Mat, boolean)`, `arcLength(org.opencv.core.MatOfPoint2f, boolean)`

### moments

    public static Moments moments(Mat array,
                                  boolean binaryImage)

  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 `Moments` defined as: ``

  // C++ code:

  class Moments

  public:

  Moments();

  Moments(double m00, double m10, double m01, double m20, double m11,

  double m02, double m30, double m21, double m12, double m03);

  Moments(const CvMoments& moments);

  operator CvMoments() const;

  // spatial moments

  double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;

  // central moments

  double mu20, mu11, mu02, mu30, mu21, mu12, mu03;

  // central normalized moments

  double nu20, nu11, nu02, nu30, nu21, nu12, nu03;

  In case of a raster image, the spatial moments Moments.m_(ji) are computed as:

  m _(ji)= sum(by: x,y)(array(x,y) * x^j * y^i)

  The central moments Moments.mu_(ji) are computed as:

  mu _(ji)= sum(by: x,y)(array(x,y) * (x - x")^j * (y - y")^i)

  where (x", y") is the mass center:

  x" = (m_10)/(m_(00)), y" = (m_01)/(m_(00))

  The normalized central moments Moments.nu_(ij) are computed as:

  nu _(ji)= (mu_(ji))/(m_(00)^((i+j)/2+1)).

  Note:

  mu_00=m_00, nu_00=1 nu_10=mu_10=mu_01=mu_10=0, hence the values are not stored.

  The moments of a contour are defined in the same way but computed using the Green's formula (see http://en.wikipedia.org/wiki/Green_theorem). So, due to a limited raster resolution, the moments computed for a contour are slightly different from the moments computed for the same rasterized contour.

  Note:

  Since the contour moments are computed using Green formula, you may get seemingly odd results for contours with self-intersections, e.g. a zero area (`m00`) for butterfly-shaped contours.

  Parameters:
    `array` - Raster image (single-channel, 8-bit or floating-point 2D array) or an array (1 x N or N x 1) of 2D points (`Point` or `Point2f`).
    `binaryImage` - If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.
  See Also:
    org.opencv.imgproc.Imgproc.moments, `contourArea(org.opencv.core.Mat, boolean)`, `arcLength(org.opencv.core.MatOfPoint2f, boolean)`

### morphologyEx

    public static void morphologyEx(Mat src,
                                    Mat dst,
                                    int op,
                                    Mat kernel)

  Performs advanced morphological transformations.

  The function can perform advanced morphological transformations using an erosion and dilation as basic operations.

  Opening operation:

  dst = open(src, element)= dilate(erode(src, element))

  Closing operation:

  dst = close(src, element)= erode(dilate(src, element))

  Morphological gradient:

  dst = morph_grad(src, element)= dilate(src, element)- erode(src, element)

  "Top hat":

  dst = tophat(src, element)= src - open(src, element)

  "Black hat":

  dst = blackhat(src, element)= close(src, element)- src

  Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.

  Parameters:
    `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".
    `dst` - Destination image of the same size and type as `src`.
    `op` - Type of a morphological operation that can be one of the following:
    - MORPH_OPEN - an opening operation
    - MORPH_CLOSE - a closing operation
    - MORPH_GRADIENT - a morphological gradient
    - MORPH_TOPHAT - "top hat"
    - MORPH_BLACKHAT - "black hat"
    `kernel` - a kernel
  See Also:
    org.opencv.imgproc.Imgproc.morphologyEx, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `erode(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `dilate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### morphologyEx

    public static void morphologyEx(Mat src,
                                    Mat dst,
                                    int op,
                                    Mat kernel,
                                    Point anchor,
                                    int iterations)

  Performs advanced morphological transformations.

  The function can perform advanced morphological transformations using an erosion and dilation as basic operations.

  Opening operation:

  dst = open(src, element)= dilate(erode(src, element))

  Closing operation:

  dst = close(src, element)= erode(dilate(src, element))

  Morphological gradient:

  dst = morph_grad(src, element)= dilate(src, element)- erode(src, element)

  "Top hat":

  dst = tophat(src, element)= src - open(src, element)

  "Black hat":

  dst = blackhat(src, element)= close(src, element)- src

  Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.

  Parameters:
    `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".
    `dst` - Destination image of the same size and type as `src`.
    `op` - Type of a morphological operation that can be one of the following:
    - MORPH_OPEN - an opening operation
    - MORPH_CLOSE - a closing operation
    - MORPH_GRADIENT - a morphological gradient
    - MORPH_TOPHAT - "top hat"
    - MORPH_BLACKHAT - "black hat"
    `kernel` - a kernel
    `anchor` - a anchor
    `iterations` - Number of times erosion and dilation are applied.
  See Also:
    org.opencv.imgproc.Imgproc.morphologyEx, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `erode(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `dilate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### morphologyEx

    public static void morphologyEx(Mat src,
                                    Mat dst,
                                    int op,
                                    Mat kernel,
                                    Point anchor,
                                    int iterations,
                                    int borderType,
                                    Scalar borderValue)

  Performs advanced morphological transformations.

  The function can perform advanced morphological transformations using an erosion and dilation as basic operations.

  Opening operation:

  dst = open(src, element)= dilate(erode(src, element))

  Closing operation:

  dst = close(src, element)= erode(dilate(src, element))

  Morphological gradient:

  dst = morph_grad(src, element)= dilate(src, element)- erode(src, element)

  "Top hat":

  dst = tophat(src, element)= src - open(src, element)

  "Black hat":

  dst = blackhat(src, element)= close(src, element)- src

  Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.

  Parameters:
    `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".
    `dst` - Destination image of the same size and type as `src`.
    `op` - Type of a morphological operation that can be one of the following:
    - MORPH_OPEN - an opening operation
    - MORPH_CLOSE - a closing operation
    - MORPH_GRADIENT - a morphological gradient
    - MORPH_TOPHAT - "top hat"
    - MORPH_BLACKHAT - "black hat"
    `kernel` - a kernel
    `anchor` - a anchor
    `iterations` - Number of times erosion and dilation are applied.
    `borderType` - Pixel extrapolation method. See "borderInterpolate" for details.
    `borderValue` - Border value in case of a constant border. The default value has a special meaning. See "createMorphologyFilter" for details.
  See Also:
    org.opencv.imgproc.Imgproc.morphologyEx, `org.opencv.imgproc.Imgproc#createMorphologyFilter`, `erode(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`, `dilate(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, int, int, org.opencv.core.Scalar)`

### phaseCorrelate

    public static Point phaseCorrelate(Mat src1,
                                       Mat src2)

  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".

  Return value: detected phase shift (sub-pixel) between the two arrays.

  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:

  mathbf(G)_a = mathcal(F)(src_1), mathbf(G)_b = mathcal(F)(src_2)

  where mathcal(F) is the forward DFT.

  - It then computes the cross-power spectrum of each frequency domain array:

  R = (mathbf(G)_a mathbf(G)_b^*)/(|mathbf(G)_a mathbf(G)_b^*|)

  - Next the cross-correlation is converted back into the time domain via the inverse DFT:

  r = mathcal(F)^(-1)(R)

  - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  (Delta x, Delta y) = weightedCentroid (arg max_((x, y))(r))

  - 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.

  Parameters:
    `src1` - Source floating point array (CV_32FC1 or CV_64FC1)
    `src2` - Source floating point array (CV_32FC1 or CV_64FC1)
  See Also:
    org.opencv.imgproc.Imgproc.phaseCorrelate, `createHanningWindow(org.opencv.core.Mat, org.opencv.core.Size, int)`, `Core.dft(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`, `Core.mulSpectrums(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, boolean)`, `Core.getOptimalDFTSize(int)`, `Core.idft(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`

### phaseCorrelate

    public static Point phaseCorrelate(Mat src1,
                                       Mat src2,
                                       Mat window)

  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".

  Return value: detected phase shift (sub-pixel) between the two arrays.

  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:

  mathbf(G)_a = mathcal(F)(src_1), mathbf(G)_b = mathcal(F)(src_2)

  where mathcal(F) is the forward DFT.

  - It then computes the cross-power spectrum of each frequency domain array:

  R = (mathbf(G)_a mathbf(G)_b^*)/(|mathbf(G)_a mathbf(G)_b^*|)

  - Next the cross-correlation is converted back into the time domain via the inverse DFT:

  r = mathcal(F)^(-1)(R)

  - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  (Delta x, Delta y) = weightedCentroid (arg max_((x, y))(r))

  - 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.

  Parameters:
    `src1` - Source floating point array (CV_32FC1 or CV_64FC1)
    `src2` - Source floating point array (CV_32FC1 or CV_64FC1)
    `window` - Floating point array with windowing coefficients to reduce edge effects (optional).
  See Also:
    org.opencv.imgproc.Imgproc.phaseCorrelate, `createHanningWindow(org.opencv.core.Mat, org.opencv.core.Size, int)`, `Core.dft(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`, `Core.mulSpectrums(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, boolean)`, `Core.getOptimalDFTSize(int)`, `Core.idft(org.opencv.core.Mat, org.opencv.core.Mat, int, int)`

### phaseCorrelateRes

    public static Point phaseCorrelateRes(Mat src1,
                                          Mat src2,
                                          Mat window)

### phaseCorrelateRes

    public static Point phaseCorrelateRes(Mat src1,
                                          Mat src2,
                                          Mat window,
                                          double[] response)

### pointPolygonTest

    public static double pointPolygonTest(MatOfPoint2f contour,
                                          Point pt,
                                          boolean measureDist)

  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.

  Parameters:
    `contour` - Input contour.
    `pt` - Point tested against the contour.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.pointPolygonTest

### preCornerDetect

    public static void preCornerDetect(Mat src,
                                       Mat dst,
                                       int ksize)

  Calculates a feature map for corner detection.

  The function calculates the complex spatial derivative-based function of the source image

  dst = (D_x src)^2 * D_(yy) src + (D_y src)^2 * D_(xx) src - 2 D_x src * D_y src * D_(xy) src

  where D_x,D_y are the first image derivatives, D_(xx),D_(yy) are the second image derivatives, and D_(xy) is the mixed derivative. The corners can be found as local maximums of the functions, as shown below: ``

  // C++ 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;

  Parameters:
    `src` - Source single-channel 8-bit of floating-point image.
    `dst` - Output image that has the type `CV_32F` and the same size as `src`.
    `ksize` - Aperture size of the "Sobel".
  See Also:
    org.opencv.imgproc.Imgproc.preCornerDetect

### preCornerDetect

    public static void preCornerDetect(Mat src,
                                       Mat dst,
                                       int ksize,
                                       int borderType)

  Calculates a feature map for corner detection.

  The function calculates the complex spatial derivative-based function of the source image

  dst = (D_x src)^2 * D_(yy) src + (D_y src)^2 * D_(xx) src - 2 D_x src * D_y src * D_(xy) src

  where D_x,D_y are the first image derivatives, D_(xx),D_(yy) are the second image derivatives, and D_(xy) is the mixed derivative. The corners can be found as local maximums of the functions, as shown below: ``

  // C++ 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;

  Parameters:
    `src` - Source single-channel 8-bit of floating-point image.
    `dst` - Output image that has the type `CV_32F` and the same size as `src`.
    `ksize` - Aperture size of the "Sobel".
    `borderType` - Pixel extrapolation method. See "borderInterpolate".
  See Also:
    org.opencv.imgproc.Imgproc.preCornerDetect

### PSNR

    public static double PSNR(Mat src1,
                              Mat src2)

### pyrDown

    public static void pyrDown(Mat src,
                               Mat dst)

  Blurs an image and downsamples it.

  The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel:

  1/256 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

  Then, it downsamples the image by rejecting even rows and columns.

  Parameters:
    `src` - input image.
    `dst` - output image; it has the specified size and the same type as `src`.
  See Also:
    org.opencv.imgproc.Imgproc.pyrDown

### pyrDown

    public static void pyrDown(Mat src,
                               Mat dst,
                               Size dstsize)

  Blurs an image and downsamples it.

  The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel:

  1/256 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

  Then, it downsamples the image by rejecting even rows and columns.

  Parameters:
    `src` - input image.
    `dst` - output image; it has the specified size and the same type as `src`.
    `dstsize` - size of the output image; by default, it is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in any case, the following conditions should be satisfied:

    ltBR gt| dstsize.width *2-src.cols| <= 2 |dstsize.height *2-src.rows| <= 2

  See Also:
    org.opencv.imgproc.Imgproc.pyrDown

### pyrDown

    public static void pyrDown(Mat src,
                               Mat dst,
                               Size dstsize,
                               int borderType)

  Blurs an image and downsamples it.

  The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel:

  1/256 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

  Then, it downsamples the image by rejecting even rows and columns.

  Parameters:
    `src` - input image.
    `dst` - output image; it has the specified size and the same type as `src`.
    `dstsize` - size of the output image; by default, it is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in any case, the following conditions should be satisfied:

    ltBR gt| dstsize.width *2-src.cols| <= 2 |dstsize.height *2-src.rows| <= 2

    `borderType` - a borderType
  See Also:
    org.opencv.imgproc.Imgproc.pyrDown

### pyrMeanShiftFiltering

    public static void pyrMeanShiftFiltering(Mat src,
                                             Mat dst,
                                             double sp,
                                             double sr)

  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:

  (x,y): X- sp <= x <= X+ sp, Y- sp <= y <= Y+ sp, ||(R,G,B)-(r,g,b)|| <= sr

  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:

  (X,Y)~(X',Y'), (R,G,B)~(R',G',B').

  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):

  I(X,Y) <- (R*,G*,B*)

  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`).

  Parameters:
    `src` - The source 8-bit, 3-channel image.
    `dst` - The destination image of the same format and the same size as the source.
    `sp` - The spatial window radius.
    `sr` - The color window radius.
  See Also:
    org.opencv.imgproc.Imgproc.pyrMeanShiftFiltering

### pyrMeanShiftFiltering

    public static void pyrMeanShiftFiltering(Mat src,
                                             Mat dst,
                                             double sp,
                                             double sr,
                                             int maxLevel,
                                             TermCriteria termcrit)

  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:

  (x,y): X- sp <= x <= X+ sp, Y- sp <= y <= Y+ sp, ||(R,G,B)-(r,g,b)|| <= sr

  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:

  (X,Y)~(X',Y'), (R,G,B)~(R',G',B').

  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):

  I(X,Y) <- (R*,G*,B*)

  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`).

  Parameters:
    `src` - The source 8-bit, 3-channel image.
    `dst` - The destination image of the same format and the same size as the source.
    `sp` - The spatial window radius.
    `sr` - The color window radius.
    `maxLevel` - Maximum level of the pyramid for the segmentation.
    `termcrit` - Termination criteria: when to stop meanshift iterations.
  See Also:
    org.opencv.imgproc.Imgproc.pyrMeanShiftFiltering

### pyrUp

    public static void pyrUp(Mat src,
                             Mat dst)

  Upsamples an image and then blurs it.

  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.

  Parameters:
    `src` - input image.
    `dst` - output image. It has the specified size and the same type as `src`.
  See Also:
    org.opencv.imgproc.Imgproc.pyrUp

### pyrUp

    public static void pyrUp(Mat src,
                             Mat dst,
                             Size dstsize)

  Upsamples an image and then blurs it.

  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.

  Parameters:
    `src` - input image.
    `dst` - output image. It has the specified size and the same type as `src`.
    `dstsize` - size of the output image; by default, it is computed as `Size(src.cols*2, (src.rows*2)`, but in any case, the following conditions should be satisfied:

    ltBR gt| dstsize.width -src.cols*2| <= (dstsize.width mod 2) |dstsize.height -src.rows*2| <= (dstsize.height mod 2)

  See Also:
    org.opencv.imgproc.Imgproc.pyrUp

### pyrUp

    public static void pyrUp(Mat src,
                             Mat dst,
                             Size dstsize,
                             int borderType)

  Upsamples an image and then blurs it.

  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.

  Parameters:
    `src` - input image.
    `dst` - output image. It has the specified size and the same type as `src`.
    `dstsize` - size of the output image; by default, it is computed as `Size(src.cols*2, (src.rows*2)`, but in any case, the following conditions should be satisfied:

    ltBR gt| dstsize.width -src.cols*2| <= (dstsize.width mod 2) |dstsize.height -src.rows*2| <= (dstsize.height mod 2)

    `borderType` - a borderType
  See Also:
    org.opencv.imgproc.Imgproc.pyrUp

### remap

    public static void remap(Mat src,
                             Mat dst,
                             Mat map1,
                             Mat map2,
                             int interpolation)

  Applies a generic geometrical transformation to an image.

  The function `remap` transforms the source image using the specified map:

  dst(x,y) = src(map_x(x,y),map_y(x,y))

  where values of pixels with non-integer coordinates are computed using one of available interpolation methods. map_x and map_y can be encoded as separate floating-point maps in map_1 and map_2 respectively, or interleaved floating-point maps of (x,y) in map_1, 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, map_1 contains pairs `(cvFloor(x), cvFloor(y))` and map_2 contains indices in a table of interpolation coefficients.

  This function cannot operate in-place.

  Parameters:
    `src` - Source image.
    `dst` - Destination image. It has the same size as `map1` and the same type as `src`.
    `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.
    `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.
    `interpolation` - Interpolation method (see "resize"). The method `INTER_AREA` is not supported by this function.
  See Also:
    org.opencv.imgproc.Imgproc.remap

### remap

    public static void remap(Mat src,
                             Mat dst,
                             Mat map1,
                             Mat map2,
                             int interpolation,
                             int borderMode,
                             Scalar borderValue)

  Applies a generic geometrical transformation to an image.

  The function `remap` transforms the source image using the specified map:

  dst(x,y) = src(map_x(x,y),map_y(x,y))

  where values of pixels with non-integer coordinates are computed using one of available interpolation methods. map_x and map_y can be encoded as separate floating-point maps in map_1 and map_2 respectively, or interleaved floating-point maps of (x,y) in map_1, 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, map_1 contains pairs `(cvFloor(x), cvFloor(y))` and map_2 contains indices in a table of interpolation coefficients.

  This function cannot operate in-place.

  Parameters:
    `src` - Source image.
    `dst` - Destination image. It has the same size as `map1` and the same type as `src`.
    `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.
    `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.
    `interpolation` - Interpolation method (see "resize"). The method `INTER_AREA` is not supported by this function.
    `borderMode` - Pixel extrapolation method (see "borderInterpolate"). 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.
    `borderValue` - Value used in case of a constant border. By default, it is 0.
  See Also:
    org.opencv.imgproc.Imgproc.remap

### resize

    public static void resize(Mat src,
                              Mat dst,
                              Size dsize)

  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: ``

  // C++ code:

  // explicitly specify dsize=dst.size(); fx and fy will be computed from that.

  resize(src, dst, dst.size(), 0, 0, interpolation);

  If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

  // specify fx and fy and let the function compute the destination image size.

  resize(src, dst, Size(), 0.5, 0.5, interpolation);

  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).

  Parameters:
    `src` - input image.
    `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`.
    `dsize` - output image size; if it equals zero, it is computed as:

    dsize = Size(round(fx*src.cols), round(fy*src.rows))

    Either `dsize` or both `fx` and `fy` must be non-zero.

  See Also:
    org.opencv.imgproc.Imgproc.resize, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`

### resize

    public static void resize(Mat src,
                              Mat dst,
                              Size dsize,
                              double fx,
                              double fy,
                              int interpolation)

  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: ``

  // C++ code:

  // explicitly specify dsize=dst.size(); fx and fy will be computed from that.

  resize(src, dst, dst.size(), 0, 0, interpolation);

  If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

  // specify fx and fy and let the function compute the destination image size.

  resize(src, dst, Size(), 0.5, 0.5, interpolation);

  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).

  Parameters:
    `src` - input image.
    `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`.
    `dsize` - output image size; if it equals zero, it is computed as:

    dsize = Size(round(fx*src.cols), round(fy*src.rows))

    Either `dsize` or both `fx` and `fy` must be non-zero.

    `fx` - scale factor along the horizontal axis; when it equals 0, it is computed as

    (double)dsize.width/src.cols

    `fy` - scale factor along the vertical axis; when it equals 0, it is computed as

    (double)dsize.height/src.rows

    `interpolation` - interpolation method:
    - INTER_NEAREST - a nearest-neighbor interpolation
    - INTER_LINEAR - a bilinear interpolation (used by default)
    - INTER_AREA - 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_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood
    - INTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhood
  See Also:
    org.opencv.imgproc.Imgproc.resize, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`

### Scharr

    public static void Scharr(Mat src,
                              Mat dst,
                              int ddepth,
                              int dx,
                              int dy)

  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

  Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)

  is equivalent to

  Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType).

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - output image depth (see "Sobel" for the list of supported combination of `src.depth()` and `ddepth`).
    `dx` - order of the derivative x.
    `dy` - order of the derivative y.
  See Also:
    org.opencv.imgproc.Imgproc.Scharr, `Core.cartToPolar(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, boolean)`

### Scharr

    public static void Scharr(Mat src,
                              Mat dst,
                              int ddepth,
                              int dx,
                              int dy,
                              double scale,
                              double delta)

  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

  Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)

  is equivalent to

  Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType).

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - output image depth (see "Sobel" for the list of supported combination of `src.depth()` and `ddepth`).
    `dx` - order of the derivative x.
    `dy` - order of the derivative y.
    `scale` - optional scale factor for the computed derivative values; by default, no scaling is applied (see "getDerivKernels" for details).
    `delta` - optional delta value that is added to the results prior to storing them in `dst`.
  See Also:
    org.opencv.imgproc.Imgproc.Scharr, `Core.cartToPolar(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, boolean)`

### Scharr

    public static void Scharr(Mat src,
                              Mat dst,
                              int ddepth,
                              int dx,
                              int dy,
                              double scale,
                              double delta,
                              int borderType)

  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

  Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)

  is equivalent to

  Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType).

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - output image depth (see "Sobel" for the list of supported combination of `src.depth()` and `ddepth`).
    `dx` - order of the derivative x.
    `dy` - order of the derivative y.
    `scale` - optional scale factor for the computed derivative values; by default, no scaling is applied (see "getDerivKernels" for details).
    `delta` - optional delta value that is added to the results prior to storing them in `dst`.
    `borderType` - pixel extrapolation method (see "borderInterpolate" for details).
  See Also:
    org.opencv.imgproc.Imgproc.Scharr, `Core.cartToPolar(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, boolean)`

### sepFilter2D

    public static void sepFilter2D(Mat src,
                                   Mat dst,
                                   int ddepth,
                                   Mat kernelX,
                                   Mat kernelY)

  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`.

  Parameters:
    `src` - Source image.
    `dst` - Destination image of the same size and the same number of channels as `src`.
    `ddepth` - Destination image depth. The following combination of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the destination image will have the same depth as the source.

    `kernelX` - Coefficients for filtering each row.
    `kernelY` - Coefficients for filtering each column.
  See Also:
    org.opencv.imgproc.Imgproc.sepFilter2D, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Sobel(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, int, double, double, int)`, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `org.opencv.imgproc.Imgproc#createSeparableLinearFilter`

### sepFilter2D

    public static void sepFilter2D(Mat src,
                                   Mat dst,
                                   int ddepth,
                                   Mat kernelX,
                                   Mat kernelY,
                                   Point anchor,
                                   double delta)

  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`.

  Parameters:
    `src` - Source image.
    `dst` - Destination image of the same size and the same number of channels as `src`.
    `ddepth` - Destination image depth. The following combination of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the destination image will have the same depth as the source.

    `kernelX` - Coefficients for filtering each row.
    `kernelY` - Coefficients for filtering each column.
    `anchor` - Anchor position within the kernel. The default value (-1, 1) means that the anchor is at the kernel center.
    `delta` - Value added to the filtered results before storing them.
  See Also:
    org.opencv.imgproc.Imgproc.sepFilter2D, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Sobel(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, int, double, double, int)`, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `org.opencv.imgproc.Imgproc#createSeparableLinearFilter`

### sepFilter2D

    public static void sepFilter2D(Mat src,
                                   Mat dst,
                                   int ddepth,
                                   Mat kernelX,
                                   Mat kernelY,
                                   Point anchor,
                                   double delta,
                                   int borderType)

  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`.

  Parameters:
    `src` - Source image.
    `dst` - Destination image of the same size and the same number of channels as `src`.
    `ddepth` - Destination image depth. The following combination of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the destination image will have the same depth as the source.

    `kernelX` - Coefficients for filtering each row.
    `kernelY` - Coefficients for filtering each column.
    `anchor` - Anchor position within the kernel. The default value (-1, 1) means that the anchor is at the kernel center.
    `delta` - Value added to the filtered results before storing them.
    `borderType` - Pixel extrapolation method. See "borderInterpolate" for details.
  See Also:
    org.opencv.imgproc.Imgproc.sepFilter2D, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Sobel(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, int, double, double, int)`, `boxFilter(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Size, org.opencv.core.Point, boolean, int)`, `blur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `org.opencv.imgproc.Imgproc#createSeparableLinearFilter`

### Sobel

    public static void Sobel(Mat src,
                             Mat dst,
                             int ddepth,
                             int dx,
                             int dy)

  Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.

  In all cases except one, the ksize x<BR>ksize separable kernel is used to calculate the derivative. When ksize = 1, the 3 x 1 or 1 x 3 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 3x3 Scharr filter that may give more accurate results than the 3x3 Sobel. The Scharr aperture is

  |-3 0 3| |-10 0 10| |-3 0 3|

  for the x-derivative, or transposed for the y-derivative.

  The function calculates an image derivative by convolving the image with the appropriate kernel:

  dst = (d^(xorder+yorder) src)/(dx^(xorder) dy^(yorder))

  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:

  |-1 0 1| |-2 0 2| |-1 0 1|

  The second case corresponds to a kernel of:

  |-1 -2 -1| |0 0 0| |1 2 1|

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - output image depth; the following combinations of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the destination image will have the same depth as the source; in the case of 8-bit input images it will result in truncated derivatives.

    `dx` - a dx
    `dy` - a dy
  See Also:
    org.opencv.imgproc.Imgproc.Sobel, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Core.cartToPolar(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, boolean)`, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `Laplacian(org.opencv.core.Mat, org.opencv.core.Mat, int, int, double, double, int)`, `Scharr(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, double, double, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`

### Sobel

    public static void Sobel(Mat src,
                             Mat dst,
                             int ddepth,
                             int dx,
                             int dy,
                             int ksize,
                             double scale,
                             double delta)

  Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.

  In all cases except one, the ksize x<BR>ksize separable kernel is used to calculate the derivative. When ksize = 1, the 3 x 1 or 1 x 3 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 3x3 Scharr filter that may give more accurate results than the 3x3 Sobel. The Scharr aperture is

  |-3 0 3| |-10 0 10| |-3 0 3|

  for the x-derivative, or transposed for the y-derivative.

  The function calculates an image derivative by convolving the image with the appropriate kernel:

  dst = (d^(xorder+yorder) src)/(dx^(xorder) dy^(yorder))

  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:

  |-1 0 1| |-2 0 2| |-1 0 1|

  The second case corresponds to a kernel of:

  |-1 -2 -1| |0 0 0| |1 2 1|

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - output image depth; the following combinations of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the destination image will have the same depth as the source; in the case of 8-bit input images it will result in truncated derivatives.

    `dx` - a dx
    `dy` - a dy
    `ksize` - size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
    `scale` - optional scale factor for the computed derivative values; by default, no scaling is applied (see "getDerivKernels" for details).
    `delta` - optional delta value that is added to the results prior to storing them in `dst`.
  See Also:
    org.opencv.imgproc.Imgproc.Sobel, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Core.cartToPolar(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, boolean)`, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `Laplacian(org.opencv.core.Mat, org.opencv.core.Mat, int, int, double, double, int)`, `Scharr(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, double, double, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`

### Sobel

    public static void Sobel(Mat src,
                             Mat dst,
                             int ddepth,
                             int dx,
                             int dy,
                             int ksize,
                             double scale,
                             double delta,
                             int borderType)

  Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.

  In all cases except one, the ksize x<BR>ksize separable kernel is used to calculate the derivative. When ksize = 1, the 3 x 1 or 1 x 3 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 3x3 Scharr filter that may give more accurate results than the 3x3 Sobel. The Scharr aperture is

  |-3 0 3| |-10 0 10| |-3 0 3|

  for the x-derivative, or transposed for the y-derivative.

  The function calculates an image derivative by convolving the image with the appropriate kernel:

  dst = (d^(xorder+yorder) src)/(dx^(xorder) dy^(yorder))

  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:

  |-1 0 1| |-2 0 2| |-1 0 1|

  The second case corresponds to a kernel of:

  |-1 -2 -1| |0 0 0| |1 2 1|

  Parameters:
    `src` - input image.
    `dst` - output image of the same size and the same number of channels as `src`.
    `ddepth` - output image depth; the following combinations of `src.depth()` and `ddepth` are supported:
    - `src.depth()` = `CV_8U`, `ddepth` = -1/`CV_16S`/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_16U`/`CV_16S`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_32F`, `ddepth` = -1/`CV_32F`/`CV_64F`
    - `src.depth()` = `CV_64F`, `ddepth` = -1/`CV_64F`

    when `ddepth=-1`, the destination image will have the same depth as the source; in the case of 8-bit input images it will result in truncated derivatives.

    `dx` - a dx
    `dy` - a dy
    `ksize` - size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
    `scale` - optional scale factor for the computed derivative values; by default, no scaling is applied (see "getDerivKernels" for details).
    `delta` - optional delta value that is added to the results prior to storing them in `dst`.
    `borderType` - pixel extrapolation method (see "borderInterpolate" for details).
  See Also:
    org.opencv.imgproc.Imgproc.Sobel, `GaussianBlur(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Core.cartToPolar(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, boolean)`, `sepFilter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Point, double, int)`, `Laplacian(org.opencv.core.Mat, org.opencv.core.Mat, int, int, double, double, int)`, `Scharr(org.opencv.core.Mat, org.opencv.core.Mat, int, int, int, double, double, int)`, `filter2D(org.opencv.core.Mat, org.opencv.core.Mat, int, org.opencv.core.Mat, org.opencv.core.Point, double, int)`

### threshold

    public static double threshold(Mat src,
                                   Mat dst,
                                   double thresh,
                                   double maxval,
                                   int type)

  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 ("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` :

  - THRESH_BINARY

  dst(x,y) = maxval if src(x,y) > thresh; 0 otherwise

  - THRESH_BINARY_INV

  dst(x,y) = 0 if src(x,y) > thresh; maxval otherwise

  - THRESH_TRUNC

  dst(x,y) = threshold if src(x,y) > thresh; src(x,y) otherwise

  - THRESH_TOZERO

  dst(x,y) = src(x,y) if src(x,y) > thresh; 0 otherwise

  - THRESH_TOZERO_INV

  dst(x,y) = 0 if src(x,y) > thresh; src(x,y) otherwise

  Also, the special value `THRESH_OTSU` may be combined with one of the above values. In this case, the function determines the optimal threshold value using the Otsu's algorithm and uses it instead of the specified `thresh`. The function returns the computed threshold value. Currently, the Otsu's method is implemented only for 8-bit images.

  Parameters:
    `src` - input array (single-channel, 8-bit or 32-bit floating point).
    `dst` - output array of the same size and type as `src`.
    `thresh` - treshold value.
    `maxval` - maximum value to use with the `THRESH_BINARY` and `THRESH_BINARY_INV` thresholding types.
    `type` - thresholding type (see the details below).
  See Also:
    org.opencv.imgproc.Imgproc.threshold, `findContours(org.opencv.core.Mat, java.util.List, org.opencv.core.Mat, int, int, org.opencv.core.Point)`, `Core.max(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `adaptiveThreshold(org.opencv.core.Mat, org.opencv.core.Mat, double, int, int, int, double)`, `Core.compare(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int)`, `Core.min(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### undistort

    public static void undistort(Mat src,
                                 Mat dst,
                                 Mat cameraMatrix,
                                 Mat distCoeffs)

  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 "initUndistortRectifyMap" (with unity `R`) and "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 "getOptimalNewCameraMatrix" to compute the appropriate `newCameraMatrix` depending on your requirements.

  The camera matrix and the distortion parameters can be determined using "calibrateCamera". If the resolution of images is different from the resolution used at the calibration stage, f_x, f_y, c_x and c_y need to be scaled accordingly, while the distortion coefficients remain the same.

  Parameters:
    `src` - Input (distorted) image.
    `dst` - Output (corrected) image that has the same size and type as `src`.
    `cameraMatrix` - Input camera matrix A =

    |f_x 0 c_x| |0 f_y c_y| |0 0 1|

    .
    `distCoeffs` - Input vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
  See Also:
    org.opencv.imgproc.Imgproc.undistort

### undistort

    public static void undistort(Mat src,
                                 Mat dst,
                                 Mat cameraMatrix,
                                 Mat distCoeffs,
                                 Mat newCameraMatrix)

  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 "initUndistortRectifyMap" (with unity `R`) and "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 "getOptimalNewCameraMatrix" to compute the appropriate `newCameraMatrix` depending on your requirements.

  The camera matrix and the distortion parameters can be determined using "calibrateCamera". If the resolution of images is different from the resolution used at the calibration stage, f_x, f_y, c_x and c_y need to be scaled accordingly, while the distortion coefficients remain the same.

  Parameters:
    `src` - Input (distorted) image.
    `dst` - Output (corrected) image that has the same size and type as `src`.
    `cameraMatrix` - Input camera matrix A =

    |f_x 0 c_x| |0 f_y c_y| |0 0 1|

    .
    `distCoeffs` - Input vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
    `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.
  See Also:
    org.opencv.imgproc.Imgproc.undistort

### undistortPoints

    public static void undistortPoints(MatOfPoint2f src,
                                       MatOfPoint2f dst,
                                       Mat cameraMatrix,
                                       Mat distCoeffs)

  Computes the ideal point coordinates from the observed point coordinates.

  The function is similar to "undistort" and "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. ``

  // C++ code:

  // (u,v) is the input point, (u', v') is the output point

  // camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1]

  // P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz]

  x" = (u - cx)/fx

  y" = (v - cy)/fy

  (x',y') = undistort(x",y",dist_coeffs)

  [X,Y,W]T = R*[x' y' 1]T

  x = X/W, y = Y/W

  // only performed if P=[fx' 0 cx' [tx]; 0 fy' cy' [ty]; 0 0 1 [tz]] is specified

  u' = x*fx' + cx'

  v' = y*fy' + cy',

  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).

  Parameters:
    `src` - Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
    `dst` - Output ideal point coordinates after undistortion and reverse perspective transformation. If matrix `P` is identity or omitted, `dst` will contain normalized point coordinates.
    `cameraMatrix` - Camera matrix

    |f_x 0 c_x| |0 f_y c_y| |0 0 1|

    .
    `distCoeffs` - Input vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
  See Also:
    org.opencv.imgproc.Imgproc.undistortPoints

### undistortPoints

    public static void undistortPoints(MatOfPoint2f src,
                                       MatOfPoint2f dst,
                                       Mat cameraMatrix,
                                       Mat distCoeffs,
                                       Mat R,
                                       Mat P)

  Computes the ideal point coordinates from the observed point coordinates.

  The function is similar to "undistort" and "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. ``

  // C++ code:

  // (u,v) is the input point, (u', v') is the output point

  // camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1]

  // P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz]

  x" = (u - cx)/fx

  y" = (v - cy)/fy

  (x',y') = undistort(x",y",dist_coeffs)

  [X,Y,W]T = R*[x' y' 1]T

  x = X/W, y = Y/W

  // only performed if P=[fx' 0 cx' [tx]; 0 fy' cy' [ty]; 0 0 1 [tz]] is specified

  u' = x*fx' + cx'

  v' = y*fy' + cy',

  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).

  Parameters:
    `src` - Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
    `dst` - Output ideal point coordinates after undistortion and reverse perspective transformation. If matrix `P` is identity or omitted, `dst` will contain normalized point coordinates.
    `cameraMatrix` - Camera matrix

    |f_x 0 c_x| |0 f_y c_y| |0 0 1|

    .
    `distCoeffs` - Input vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
    `R` - 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 used.
    `P` - New camera matrix (3x3) or new projection matrix (3x4). `P1` or `P2` computed by "stereoRectify" can be passed here. If the matrix is empty, the identity new camera matrix is used.
  See Also:
    org.opencv.imgproc.Imgproc.undistortPoints

### warpAffine

    public static void warpAffine(Mat src,
                                  Mat dst,
                                  Mat M,
                                  Size dsize)

  Applies an affine transformation to an image.

  The function `warpAffine` transforms the source image using the specified matrix:

  dst(x,y) = src(M _11 x + M _12 y + M _13, M _21 x + M _22 y + M _23)

  when the flag `WARP_INVERSE_MAP` is set. Otherwise, the transformation is first inverted with "invertAffineTransform" and then put in the formula above instead of `M`. The function cannot operate in-place.

  Note: `cvGetQuadrangleSubPix` is similar to `cvWarpAffine`, but the outliers are extrapolated using replication border mode.

  Parameters:
    `src` - input image.
    `dst` - output image that has the size `dsize` and the same type as `src`.
    `M` - 2x 3 transformation matrix.
    `dsize` - size of the output image.
  See Also:
    org.opencv.imgproc.Imgproc.warpAffine, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `getRectSubPix(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, org.opencv.core.Mat, int)`, `resize(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Core.transform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### warpAffine

    public static void warpAffine(Mat src,
                                  Mat dst,
                                  Mat M,
                                  Size dsize,
                                  int flags)

  Applies an affine transformation to an image.

  The function `warpAffine` transforms the source image using the specified matrix:

  dst(x,y) = src(M _11 x + M _12 y + M _13, M _21 x + M _22 y + M _23)

  when the flag `WARP_INVERSE_MAP` is set. Otherwise, the transformation is first inverted with "invertAffineTransform" and then put in the formula above instead of `M`. The function cannot operate in-place.

  Note: `cvGetQuadrangleSubPix` is similar to `cvWarpAffine`, but the outliers are extrapolated using replication border mode.

  Parameters:
    `src` - input image.
    `dst` - output image that has the size `dsize` and the same type as `src`.
    `M` - 2x 3 transformation matrix.
    `dsize` - size of the output image.
    `flags` - combination of interpolation methods (see "resize") and the optional flag `WARP_INVERSE_MAP` that means that `M` is the inverse transformation (dst->src).
  See Also:
    org.opencv.imgproc.Imgproc.warpAffine, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `getRectSubPix(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, org.opencv.core.Mat, int)`, `resize(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Core.transform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### warpAffine

    public static void warpAffine(Mat src,
                                  Mat dst,
                                  Mat M,
                                  Size dsize,
                                  int flags,
                                  int borderMode,
                                  Scalar borderValue)

  Applies an affine transformation to an image.

  The function `warpAffine` transforms the source image using the specified matrix:

  dst(x,y) = src(M _11 x + M _12 y + M _13, M _21 x + M _22 y + M _23)

  when the flag `WARP_INVERSE_MAP` is set. Otherwise, the transformation is first inverted with "invertAffineTransform" and then put in the formula above instead of `M`. The function cannot operate in-place.

  Note: `cvGetQuadrangleSubPix` is similar to `cvWarpAffine`, but the outliers are extrapolated using replication border mode.

  Parameters:
    `src` - input image.
    `dst` - output image that has the size `dsize` and the same type as `src`.
    `M` - 2x 3 transformation matrix.
    `dsize` - size of the output image.
    `flags` - combination of interpolation methods (see "resize") and the optional flag `WARP_INVERSE_MAP` that means that `M` is the inverse transformation (dst->src).
    `borderMode` - pixel extrapolation method (see "borderInterpolate"); 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.
    `borderValue` - value used in case of a constant border; by default, it is 0.
  See Also:
    org.opencv.imgproc.Imgproc.warpAffine, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `warpPerspective(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `getRectSubPix(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, org.opencv.core.Mat, int)`, `resize(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`, `Core.transform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`

### warpPerspective

    public static void warpPerspective(Mat src,
                                       Mat dst,
                                       Mat M,
                                       Size dsize)

  Applies a perspective transformation to an image.

  The function `warpPerspective` transforms the source image using the specified matrix:

  dst(x,y) = src((M_11 x + M_12 y + M_13)/(M_(31) x + M_32 y + M_33),<BR>(M_21 x + M_22 y + M_23)/(M_(31) x + M_32 y + M_33))

  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.

  Parameters:
    `src` - input image.
    `dst` - output image that has the size `dsize` and the same type as `src`.
    `M` - 3x 3 transformation matrix.
    `dsize` - size of the output image.
  See Also:
    org.opencv.imgproc.Imgproc.warpPerspective, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `Core.perspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `getRectSubPix(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, org.opencv.core.Mat, int)`, `resize(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`

### warpPerspective

    public static void warpPerspective(Mat src,
                                       Mat dst,
                                       Mat M,
                                       Size dsize,
                                       int flags)

  Applies a perspective transformation to an image.

  The function `warpPerspective` transforms the source image using the specified matrix:

  dst(x,y) = src((M_11 x + M_12 y + M_13)/(M_(31) x + M_32 y + M_33),<BR>(M_21 x + M_22 y + M_23)/(M_(31) x + M_32 y + M_33))

  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.

  Parameters:
    `src` - input image.
    `dst` - output image that has the size `dsize` and the same type as `src`.
    `M` - 3x 3 transformation matrix.
    `dsize` - size of the output image.
    `flags` - combination of interpolation methods (`INTER_LINEAR` or `INTER_NEAREST`) and the optional flag `WARP_INVERSE_MAP`, that sets `M` as the inverse transformation (dst->src).
  See Also:
    org.opencv.imgproc.Imgproc.warpPerspective, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `Core.perspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `getRectSubPix(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, org.opencv.core.Mat, int)`, `resize(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`

### warpPerspective

    public static void warpPerspective(Mat src,
                                       Mat dst,
                                       Mat M,
                                       Size dsize,
                                       int flags,
                                       int borderMode,
                                       Scalar borderValue)

  Applies a perspective transformation to an image.

  The function `warpPerspective` transforms the source image using the specified matrix:

  dst(x,y) = src((M_11 x + M_12 y + M_13)/(M_(31) x + M_32 y + M_33),<BR>(M_21 x + M_22 y + M_23)/(M_(31) x + M_32 y + M_33))

  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.

  Parameters:
    `src` - input image.
    `dst` - output image that has the size `dsize` and the same type as `src`.
    `M` - 3x 3 transformation matrix.
    `dsize` - size of the output image.
    `flags` - combination of interpolation methods (`INTER_LINEAR` or `INTER_NEAREST`) and the optional flag `WARP_INVERSE_MAP`, that sets `M` as the inverse transformation (dst->src).
    `borderMode` - pixel extrapolation method (`BORDER_CONSTANT` or `BORDER_REPLICATE`).
    `borderValue` - value used in case of a constant border; by default, it equals 0.
  See Also:
    org.opencv.imgproc.Imgproc.warpPerspective, `warpAffine(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, int, int, org.opencv.core.Scalar)`, `remap(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat, int, int, org.opencv.core.Scalar)`, `Core.perspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Mat)`, `getRectSubPix(org.opencv.core.Mat, org.opencv.core.Size, org.opencv.core.Point, org.opencv.core.Mat, int)`, `resize(org.opencv.core.Mat, org.opencv.core.Mat, org.opencv.core.Size, double, double, int)`

### watershed

    public static void watershed(Mat image,
                                 Mat markers)

  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 [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.

  Visual demonstration and usage example of the function can be found in the OpenCV samples directory (see the `watershed.cpp` demo).

  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.

  Parameters:
    `image` - Input 8-bit 3-channel image.
    `markers` - Input/output 32-bit single-channel image (map) of markers. It should have the same size as `image`.
  See Also:
    org.opencv.imgproc.Imgproc.watershed, `findContours(org.opencv.core.Mat, java.util.List, org.opencv.core.Mat, int, int, org.opencv.core.Point)`

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